subject:(chance constrained programming)

University of Texas – Austin

1. -6943-657X. Optimal spatiotemporal resource allocation in public health and renewable energy.

Degree: PhD, Operations Research & Industrial Engineering, 2016, University of Texas – Austin

► Optimizing the spatiotemporal allocation and distribution of a limited number of critical resources is a pervasive problem, concerning both government agencies and private companies. This… (more)

Subjects/Keywords: Stochastic optimization; Chance-constrained programming; Pumped-hydroelectric system; Public health

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APA (6^th Edition):

-6943-657X. (2016). Optimal spatiotemporal resource allocation in public health and renewable energy. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44589

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-6943-657X. “Optimal spatiotemporal resource allocation in public health and renewable energy.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed April 16, 2021. http://hdl.handle.net/2152/44589.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-6943-657X. “Optimal spatiotemporal resource allocation in public health and renewable energy.” 2016. Web. 16 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6943-657X. Optimal spatiotemporal resource allocation in public health and renewable energy. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2152/44589.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6943-657X. Optimal spatiotemporal resource allocation in public health and renewable energy. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/44589

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Univerzitet u Beogradu

2. Marković, Stefan R. Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања.

Degree: Fakultet organizacionih nauka, 2020, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:20971/bdef:Content/get

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Datum odbrane: 23. 9. 2019

Стохастичко програмирање је део операционих истраживања које се бави начином на који је могуће укључити неизвесност у процес доношења одлука… (more)

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Datum odbrane: 23. 9. 2019

Стохастичко програмирање је део операционих истраживања које се бави начином на који је могуће укључити неизвесност у процес доношења одлука и које прихвата чињеницу да доносиоцу одлуке неће увек бити доступне све потребне информације. Основни проблем у примени стохастичких модела произилази из неизвесности параметара и чињенице да се оптимално решење дефинише и добија за детерминистички двојник (представник) оригинала. Проблем је оценити квалитет решења одређеног детерминистичког двојника са становишта вредности критеријумске функције, која може бити случајног карактера, као и са становишта вероватноће задовољења стохастичких ограничења. Проблеми стохастичког програмирања се појављују у различитим областима, али неки од најчешће решаваних проблема су у области планирања производње, ланца снабдевања, логистике, транспорта, управљање портфолиом, маркетинга и уопште у области финансија као и у многим другим областима. Приступи решавању проблема стохастичког програмирања се могу поделити у три основна правца: стохастичка оптимизација, робусна оптимизација и вероватносно задовољење ограничења (chance constrained programming) и који представљају полазну тачку свих даљих истраживања у овој области оптимизације. Робусни приступ је конзервативни приступ који је оријентисан на најгори могући сценарио уз дефинисање таквог детерминистичког двојника оригиналног проблема у коме се елиминише сва неизвесност из модела. Вероватносно задовољење ограничења је приступ који посебно третира неизвесност која се јавља у параметрима ограничења и посебно се бави решавањем таквих проблема. Основна претпоставка у овом приступу је да је потребно задовољити неко ограничење које је неизвесно, са најмање унапред одређеном вероватноћом. Повод за развој и примену приступа вероватносног задовољења ограничења је потреба да се скуп ограничења опише у смислу дефинисања вероватноће задовољења ограничења која представља ризик који је доносилц одлуке спреман да прихвати да добијено оптимално решење неће бити допустиво. Основни и најзахтевнији изазов приступа вероватносног задовољења ограничења је његова рачунска изводљивост, која је пре свега повезана са могућношћу проналажења расподеле вероватноће случајних променљивих...

Advisors/Committee Members: Вујошевић, Мирко, 1951- 01174, 10883431.

Subjects/Keywords: Stochastic programming; robust optimization; chance constrained programming; deterministic equivalent; simulation; scenario generation; heuristics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marković, S. R. (2020). Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:20971/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Marković, Stefan R. “Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања.” 2020. Thesis, Univerzitet u Beogradu. Accessed April 16, 2021. https://fedorabg.bg.ac.rs/fedora/get/o:20971/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Marković, Stefan R. “Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања.” 2020. Web. 16 Apr 2021.

Vancouver:

Marković SR. Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања. [Internet] [Thesis]. Univerzitet u Beogradu; 2020. [cited 2021 Apr 16]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:20971/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Marković SR. Оптимизационо-симулациони приступ решавању проблема стохастичког програмирања. [Thesis]. Univerzitet u Beogradu; 2020. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:20971/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brunel University

3. Sheikh Hussin, Siti Aida. Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty.

Degree: PhD, 2012, Brunel University

URL: http://bura.brunel.ac.uk/handle/2438/7505 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575657

► We describe Employees Provident Funds (EPF) Malaysia. We explain about Defined Contribution and Defined Benefit Pension Funds and examine their similarities and differences. We also… (more)

Subjects/Keywords: 519.6; Stochastic optimisation; Pension fund; Scenario generation; Risk adjusted performance measures (RAPM); Chance constrained and integrated chance constraints programming

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APA (6^th Edition):

Sheikh Hussin, S. A. (2012). Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/7505 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575657

Chicago Manual of Style (16^th Edition):

Sheikh Hussin, Siti Aida. “Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty.” 2012. Doctoral Dissertation, Brunel University. Accessed April 16, 2021. http://bura.brunel.ac.uk/handle/2438/7505 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575657.

MLA Handbook (7^th Edition):

Sheikh Hussin, Siti Aida. “Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty.” 2012. Web. 16 Apr 2021.

Vancouver:

Sheikh Hussin SA. Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty. [Internet] [Doctoral dissertation]. Brunel University; 2012. [cited 2021 Apr 16]. Available from: http://bura.brunel.ac.uk/handle/2438/7505 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575657.

Council of Science Editors:

Sheikh Hussin SA. Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty. [Doctoral Dissertation]. Brunel University; 2012. Available from: http://bura.brunel.ac.uk/handle/2438/7505 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575657

Penn State University

4. Mohammadzadeh Jasour, Ashkan. Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control.

Degree: 2017, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/13313aim5346

► This dissertation concentrates on chance constrained optimization problems and their application in systems and control area. In “chance optimization” problems, we aim at maximizing the… (more)

▼ This dissertation concentrates on chance constrained optimization problems and their application in systems and control area. In “chance optimization” problems, we aim at maximizing the probability of a set defined by polynomial inequalities involving decision and uncertain parameters. These problems are, in general, nonconvex and computationally hard. With the objective of developing systematic numerical procedures to solve such problems, a sequence of convex relaxations based on the theory of measures and moments is provided, whose sequence of optimal values is shown to converge to the optimal value of the original problem. Indeed, we provide a sequence of semidefinite programs of increasing dimension which can arbitrarily approximate the solution of the original problem. In addition, we apply obtained results on chance optimization problems to challenging problems in the area of systems, control and data science. We consider the problem of probabilistic control of uncertain systems to ensure that the probability of defined failure/success is minimized/maximized. In particular, we consider the probabilistic robust control and chance constrained model predictive control problems. We also use the obtained results to analysis of stochastic and deterministic systems. More precisely, we address the problem of uncertainty set propagation and computing invariant robust set for uncertain systems and problem of computing region of attraction set for deterministic systems. In the problem of uncertainty propagation, we propagate the set of initial sets through uncertain dynamical systems and find the uncertainty set of states of the system for given time step. In the problem of region of attraction and invariant robust set, we aim at finding the largest set of all initial states whose trajectories converge to the origin. Moreover, we present the problem of corrupted and sparse data reconstruction where we want to complete the data with least possible complexity. In this thesis, to be able to efficiently solve the resulting large-scale problems, a first-order augmented Lagrangian algorithm is also implemented. Numerical examples are presented to illustrate the computational performance of the proposed approach. Advisors/Committee Members: Constantino Manuel Lagoa, Dissertation Advisor/Co-Advisor, Necdet S Aybat, Committee Chair/Co-Chair, Vishal Monga, Committee Member, Minghui Zhu, Committee Member, Antonios Armaou, Outside Member, Alexei Novikov, Committee Member.

Subjects/Keywords: Chance Constrained; Convex Optimization; Semidefinite Programming; Measure and Moments; Polynomials; Sum of Squares Optimization; Duality

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APA (6^th Edition):

Mohammadzadeh Jasour, A. (2017). Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13313aim5346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mohammadzadeh Jasour, Ashkan. “Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control.” 2017. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/13313aim5346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mohammadzadeh Jasour, Ashkan. “Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control.” 2017. Web. 16 Apr 2021.

Vancouver:

Mohammadzadeh Jasour A. Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control. [Internet] [Thesis]. Penn State University; 2017. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/13313aim5346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohammadzadeh Jasour A. Convex Approximation of Chance Constrained Optimization Problems: Application in System and Control. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/13313aim5346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Chaudhary, Damber. Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing.

Degree: MS, Electrical Engineering and Computer Science, 2015, South Dakota State University

URL: https://openprairie.sdstate.edu/etd/1854

► Existing electric distribution systems are designed to deliver power from distribution substations to consumer load centers. Since the last decade, distribution systems are revitalizing… (more)

Subjects/Keywords: Volt/var optimization; chance constrained programming; simulated annealing; smart PV inverter; and distribution power flow.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chaudhary, D. (2015). Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing. (Masters Thesis). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/1854

Chicago Manual of Style (16^th Edition):

Chaudhary, Damber. “Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing.” 2015. Masters Thesis, South Dakota State University. Accessed April 16, 2021. https://openprairie.sdstate.edu/etd/1854.

MLA Handbook (7^th Edition):

Chaudhary, Damber. “Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing.” 2015. Web. 16 Apr 2021.

Vancouver:

Chaudhary D. Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing. [Internet] [Masters thesis]. South Dakota State University; 2015. [cited 2021 Apr 16]. Available from: https://openprairie.sdstate.edu/etd/1854.

Council of Science Editors:

Chaudhary D. Chance-Constrained Real-Time Volt/Var Optimization Using Simulated Annealing. [Masters Thesis]. South Dakota State University; 2015. Available from: https://openprairie.sdstate.edu/etd/1854

University of Michigan

6. Deng, Yan. Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications.

Degree: PhD, Industrial and Operations Engineering, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/120850

► The focus of this dissertation is to develop solution methods for stochastic programs with binary decisions and risk-averse features such as chance constraint or risk-minimizing… (more)

▼ The focus of this dissertation is to develop solution methods for stochastic programs with binary decisions and risk-averse features such as chance constraint or risk-minimizing objective. We approach these problems through scenario-based reformulations, which are often of intractable scale due to the use of a large number of scenarios to represent the uncertainty. Our goal is to develop specialized decomposition algorithms for solving the problem in reasonable time. We first study a surgery planning problem with uncertainty in surgery durations. A common practice is to first assign operating rooms to surgeries and then to develop schedules. We propose a chance-constrained model that integrates these two steps. A branch-and-cut algorithm is developed, which exploits valid inequalities derived from a bin packing problem and single-machine scheduling problems. We also discuss models and solutions given ambiguous distributional information. Computational results demonstrate the efficacy of the proposed algorithm and provide insights into enhancing performance by the proposed model. Next, we study general chance-constrained 0-1 programs, where decisions made before realization of uncertainty are binary. We develop dual decomposition algorithms that find solutions through bounds and cuts efficiently. We derive a proposition about computing the Lagrangian dual whose application substantially reduces the number of subproblems to solve, and deploy cut aggregation that accelerates the solution of subproblems. We also explore parallel schemes to implement our algorithms in a distributed system. All of them improve the efficacy effectively. We then study dual decomposition for risk-averse stochastic 0-1 programs, which minimize the risk of some random outcome measured by a coherent risk function. Using generic dual representations for coherent risk measures, we derive equivalent risk-neutral minimax reformulations, to which dual decomposition methods apply. We investigate how to exploit the Lagrangian relaxation as the lower bounds by comparing three approaches. We also study parallelism more comprehensively, testing schemes that represent different combinations of basic/master-worker, synchronous/asynchronous and push/pull systems, and identify that the best is a master-worker, asynchronous and pull scheme, which achieves near-linear or even super-linear speedup. Advisors/Committee Members: Shen, Siqian May (committee member), Scott, Clayton D (committee member), Lee, Jon (committee member), Denton, Brian (committee member), Jiang, Ruiwei (committee member).

Subjects/Keywords: Stochastic integer programming; Decomposition algorithms; Chance-constrained programming; Risk-averse stochastic programming; Parallel computing; Industrial and Operations Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Deng, Y. (2016). Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120850

Chicago Manual of Style (16^th Edition):

Deng, Yan. “Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications.” 2016. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/120850.

MLA Handbook (7^th Edition):

Deng, Yan. “Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications.” 2016. Web. 16 Apr 2021.

Vancouver:

Deng Y. Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/120850.

Council of Science Editors:

Deng Y. Decomposition Algorithms and Parallel Computing for Chance-constrained and Stochastic Integer Programs with Applications. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120850

University of Arizona

7. Karimi, Roya. Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications .

Degree: 2020, University of Arizona

URL: http://hdl.handle.net/10150/656797

► In this dissertation, we investigate chance-constrained linear matrix inequality (LMI) optimization problems that require stochastic LMI constraints to be satisfied with high probability. While chance-constrained… (more)

▼ In this dissertation, we investigate chance-constrained linear matrix inequality (LMI) optimization problems that require stochastic LMI constraints to be satisfied with high probability. While chance-constrained linear matrix inequality (CCLMI) optimization provides a natural way to model optimization problems with uncertainty, finding exact solutions to these problems is notoriously challenging due to the nonconvexity of their feasible set and requirements of high-dimensional integrations. As a result, the main goal of this dissertation is to develop computationally efficient reformulation and approximation approaches along with algorithmic methods that would enable the solution of CCLMI problems. More precisely, the research carries over: (i) the two main types of chance-constrained linear matrix inequality (CCLMI) optimization problems: CCLMI with random technology matrix (RTM), and CCLMI with random right-hand side (RHS), and (ii) the two main types of probability distributions: continuous and discrete probability distributions. For continuous probability distributions, we primarily rely on the partial sample average approximation (PSAA) method to derive a series of computationally tractable approximations for CCLMI problems, analyze their properties, and derive sufficient conditions ensuring convexity for the two most popular - normal and uniform - continuous distributions. In addition, we derive several semidefinite programming PSAA-reformulations efficiently solved by off-the-shelf solvers and design a sequential convex approximation method for the PSAA formulations containing bilinear matrix inequalities. For discrete probability distributions, we use and expand the Boolean reformulation to mixed-integer semi-definite programming (MISDP) reformulations/approximations for CCLMI problems with RTM and RHS, respectively. Furthermore, we propose different efficient Branch-and-Cut algorithms to solve mixed-integer SDP problems. We implement the reformulation, algorithmic, and theoretical findings to solve CCLMI problems in three practical applications: Robust Truss Topology design, and Optimal Control for Uncertain Linear Time-delay Systems by State Feedback Controllers with Memory, and Military Attack Problem with Mobile and Immobile targets. Advisors/Committee Members: Cheng, Jianqiang (advisor), Krokhmal, Pavlo (committeemember), Fan, Neng (committeemember), Ge, Yong (committeemember).

Subjects/Keywords: bilinear matrix inequalities; Branch-and-Cut algorithms; chance-constrained programming; linear matrix inequalities; semidefinite programming; stochastic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Karimi, R. (2020). Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/656797

Chicago Manual of Style (16^th Edition):

Karimi, Roya. “Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications .” 2020. Doctoral Dissertation, University of Arizona. Accessed April 16, 2021. http://hdl.handle.net/10150/656797.

MLA Handbook (7^th Edition):

Karimi, Roya. “Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications .” 2020. Web. 16 Apr 2021.

Vancouver:

Karimi R. Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications . [Internet] [Doctoral dissertation]. University of Arizona; 2020. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10150/656797.

Council of Science Editors:

Karimi R. Chance-Constrained Linear Matrix Inequality Optimization: Theory and Applications . [Doctoral Dissertation]. University of Arizona; 2020. Available from: http://hdl.handle.net/10150/656797

University of Michigan

8. Zhang, Yiling. Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems.

Degree: PhD, Industrial & Operations Engineering, 2019, University of Michigan

URL: http://hdl.handle.net/2027.42/149946

► The primary focus of the dissertation is to develop distributionally robust optimization (DRO) models and related solution approaches for decision making in energy and healthcare… (more)

▼ The primary focus of the dissertation is to develop distributionally robust optimization (DRO) models and related solution approaches for decision making in energy and healthcare service systems with uncertainties, which often involves nonlinear constraints and discrete decision variables. Without assuming specific distributions, DRO techniques solve for solutions against the worst-case distribution of system uncertainties. In the DRO framework, we consider both risk-neutral (e.g., expectation) and risk-averse (e.g., chance constraint and Conditional Value-at-Risk (CVaR)) measures. The aim is twofold: i) developing efficient solution algorithms for DRO models with integer and/or binary variables, sometimes nonlinear structures and ii) revealing managerial insights of DRO models for specific applications. We mainly focus on DRO models of power system operations, appointment scheduling, and resource allocation in healthcare. Specifically, we first study stochastic optimal power flow (OPF), where (uncertain) renewable integration and load control are implemented to balance supply and (uncertain) demand in power grids. We propose a chance-constrained OPF (CC-OPF) model and investigate its DRO variant which is reformulated as a semidefinite programming (SDP) problem. We compare the DRO model with two benchmark models, in the IEEE 9-bus, 39-bus, and 118-bus systems with different flow congestion levels. The DRO approach yields a higher probability of satisfying the chance constraints and shorter solution time. It also better utilizes reserves at both generators and loads when the system has congested flows. Then we consider appointment scheduling under random service durations with given (fixed) appointment arrival order. We propose a DRO formulation and derive a conservative SDP reformulation. Furthermore, we study a scheduling variant under random no-shows of appointments and derive tractable reformulations for certain beliefs of no-show patterns. One preceding problem of appointment scheduling in the healthcare service operations is the surgery block allocation problem that assigns surgeries to operating rooms. We derive an equivalent 0-1 SDP reformulation and a less conservative 0-1 second-order cone programming (SOCP) reformulation for its DRO model. Finally, we study distributionally robust chance-constrained binary programs (DCBP) for limiting the probability of undesirable events, under mean-covariance information. We reformulate DCBPs as equivalent 0-1 SOCP formulations under two moment-based ambiguity sets. We further exploit the submodularity of the 0-1 SOCP reformulations under diagonal and non-diagonal matrices. We derive extended polymatroid inequalities via submodularity and lifting, which are incorporated into a branch-and-cut algorithm incorporated for efficiently solving DCBPs. We demonstrate the computational efficacy and solution performance with diverse instances of a chance-constrained bin packing problem. Advisors/Committee Members: Jiang, Ruiwei (committee member), Shen, Siqian May (committee member), Mathieu, Johanna (committee member), Denton, Brian (committee member), Lee, Jon (committee member).

Subjects/Keywords: Optimization under Uncertainty; Nonlinear Programming; Integer Programming; Chance-constrained Binary Programs; Optimal Power Flow; Appointment Scheduling; Industrial and Operations Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, Y. (2019). Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149946

Chicago Manual of Style (16^th Edition):

Zhang, Yiling. “Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems.” 2019. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/149946.

MLA Handbook (7^th Edition):

Zhang, Yiling. “Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems.” 2019. Web. 16 Apr 2021.

Vancouver:

Zhang Y. Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/149946.

Council of Science Editors:

Zhang Y. Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149946

University of Michigan

9. Chen, Zhihao. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.

Degree: PhD, Industrial and Operations Engineering, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/120834

► This thesis proposes three risk-averse models applied under demand uncertainty: a chance-constrained approach to network design problems (NDPs), a distributionally robust approach to NDPs, and… (more)

▼ This thesis proposes three risk-averse models applied under demand uncertainty: a chance-constrained approach to network design problems (NDPs), a distributionally robust approach to NDPs, and a carsharing model to maximize profitability. The first stage makes strategic network design decisions, and the second stage utilizes risk-averse approaches to ensure high levels of demand satisfaction while minimizing network design, commodity flow, and potential quality of service (QoS) penalty costs. The first model optimizes the probabilistic network design problem (PNDP), where we maintain QoS through chance constraints. The chance-constrained models are reformulated as a mixed-integer linear programs (MILPs), and we develop polynomial-time algorithms for special cases. The PNDP is benchmarked against a stochastic programming model that penalizes unmet demand via a linear penalty function. The numerical results suggest cost savings for customized QoS parameters and decreased computational time when using the polynomial-time algorithm over the MILP formulation. The second model proposes a distributionally robust NDP (DR-NDP) with a marginal moment-based ambiguity set, to obtain arc capacity solutions that optimize the worst-case total cost over all candidate distributions. We estimate the optimal value of DR-NDP with an MILP, optimized via a cutting-plane algorithm that iteratively generates valid cuts for the approximate problem. We benchmark the DR-NDP against a sample average approximation-based model, testing on grid and real-world network instances. Our results show the robustness of DR-NDP solutions and their response to changes in observed demand levels, highlighting potential niche uses of DR-NDP in data-scarce contexts. The third model maximizes carsharing profits. The first-stage problem determines fleet allocation and the number of parking lots and permits to purchase. The second stage solves a stochastic minimum cost flow problem on a spatial-temporal network to optimize operational profit, given random one-way and round trip rental demand, and ad-hoc relocation. We penalize the expected number or the conditional-value-at-risk of unserved customers to encourage higher QoS and develop a branch-and-cut algorithm with mixed-integer rounding-enhanced Benders cuts to solve both cases. Through testing on real data from Zipcar Boston, we find profitability and QoS decrease as proportion of one-way rental demand increases, and QoS significantly improves by lowering relocation costs. Advisors/Committee Members: Shen, Siqian May (committee member), Xu, Ming (committee member), Shi, Cong (committee member), Lam, Kwai Hung Henry (committee member), Epelman, Marina A (committee member).

Subjects/Keywords: Two-stage stochastic optimization; Chance-constrained programming; Distributionally robust optimization; Carsharing; Mixed-integer programming; Industrial and Operations Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, Z. (2016). Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120834

Chicago Manual of Style (16^th Edition):

Chen, Zhihao. “Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.” 2016. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/120834.

MLA Handbook (7^th Edition):

Chen, Zhihao. “Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.” 2016. Web. 16 Apr 2021.

Vancouver:

Chen Z. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/120834.

Council of Science Editors:

Chen Z. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120834

10. Helal, Nathalie. An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines.

Degree: Docteur es, Génie Informatique et Automatique, 2017, Université d'Artois

URL: http://www.theses.fr/2017ARTO0208

►

Le problème de tournées de véhicules avec contrainte de capacité est un problème important en optimisation combinatoire. L'objectif du problème est de déterminer l'ensemble des… (more)

▼

Le problème de tournées de véhicules avec contrainte de capacité est un problème important en optimisation combinatoire. L'objectif du problème est de déterminer l'ensemble des routes, nécessaire pour servir les demandes déterministes des clients ayant un cout minimal, tout en respectant la capacité limite des véhicules. Cependant, dans de nombreuses applications réelles, nous sommes confrontés à des incertitudes sur les demandes des clients. La plupart des travaux qui ont traité ce problème ont supposé que les demandes des clients étaient des variables aléatoires. Nous nous proposons dans cette thèse de représenter l'incertitude sur les demandes des clients dans le cadre de la théorie de l'évidence - un formalisme alternatif pour modéliser les incertitudes. Pour résoudre le problème d'optimisation qui résulte, nous généralisons les approches de modélisation classiques en programmation stochastique. Précisément, nous proposons deux modèles pour ce problème. Le premier modèle, est une extension de l'approche chance-constrained programming, qui impose des bornes minimales pour la croyance et la plausibilité que la somme des demandes sur chaque route respecte la capacité des véhicules. Le deuxième modèle étend l'approche stochastic programming with recourse: l'incertitude sur les recours (actions correctives) possibles sur chaque route est représentée par une fonction de croyance et le coût d'une route est alors son coût classique (sans recours) additionné du pire coût espéré des recours. Certaines propriétés de ces deux modèles sont étudiées. Un algorithme de recuit simulé est adapté pour résoudre les deux modèles et est testé expérimentalement.

The capacitated vehicle routing problem is an important combinatorial optimisation problem. Its objective is to find a set of routes of minimum cost, such that a fleet of vehicles initially located at a depot service the deterministic demands of a set of customers, while respecting capacity limits of the vehicles. Still, in many real-life applications, we are faced with uncertainty on customer demands. Most of the research papers that handled this situation, assumed that customer demands are random variables. In this thesis, we propose to represent uncertainty on customer demands using evidence theory - an alternative uncertainty theory. To tackle the resulting optimisation problem, we extend classical stochastic programming modelling approaches. Specifically, we propose two models for this problem. The first model is an extension of the chance-constrained programming approach, which imposes certain minimum bounds on the belief and plausibility that the sum of the demands on each route respects the vehicle capacity. The second model extends the stochastic programming with recourse approach: it represents by a belief function for each route the uncertainty on its recourses (corrective actions) and defines the cost of a route as its classical cost (without recourse) plus the worst expected cost of its recourses. Some properties of these two models are studied. A simulated…

Advisors/Committee Members: Lefèvre, Eric (thesis director).

Subjects/Keywords: Problème de tournées de véhicules; Demandes incertaines; "Chance constrained programming”; “Stochastic programming with recourse”; Théorie de l’évidence; Vehicle routing problem; Uncertain demands; Chance constrained programming; Stochastic programming with recourse; Evidence theory.; 620; 629.8

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Helal, N. (2017). An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines. (Doctoral Dissertation). Université d'Artois. Retrieved from http://www.theses.fr/2017ARTO0208

Chicago Manual of Style (16^th Edition):

Helal, Nathalie. “An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines.” 2017. Doctoral Dissertation, Université d'Artois. Accessed April 16, 2021. http://www.theses.fr/2017ARTO0208.

MLA Handbook (7^th Edition):

Helal, Nathalie. “An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines.” 2017. Web. 16 Apr 2021.

Vancouver:

Helal N. An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines. [Internet] [Doctoral dissertation]. Université d'Artois; 2017. [cited 2021 Apr 16]. Available from: http://www.theses.fr/2017ARTO0208.

Council of Science Editors:

Helal N. An evidential answer for the capacitated vehicle routing problem with uncertain demands : Une réponse évidentielle pour le problème de tournée de véhicules avec contrainte de capacité et demandes incertaines. [Doctoral Dissertation]. Université d'Artois; 2017. Available from: http://www.theses.fr/2017ARTO0208

University of Alberta

11. Li, Zhuangzhi. Chance Constrained Optimization with Robust and Sampling Approximation.

Degree: MS, Department of Chemical and Materials Engineering, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/rn301424z

► Uncertainty is pervasive in various process operations problems. Its appearance spans from the detailed process description of multi-site manufacturing. In practical applications, there is a… (more)

Subjects/Keywords: Linear programming based scenario reduction method; Chance constrained optimization; Optimal robust counterpart optimization approximation; Sample average approximation with scenario reduction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, Z. (2015). Chance Constrained Optimization with Robust and Sampling Approximation. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/rn301424z

Chicago Manual of Style (16^th Edition):

Li, Zhuangzhi. “Chance Constrained Optimization with Robust and Sampling Approximation.” 2015. Masters Thesis, University of Alberta. Accessed April 16, 2021. https://era.library.ualberta.ca/files/rn301424z.

MLA Handbook (7^th Edition):

Li, Zhuangzhi. “Chance Constrained Optimization with Robust and Sampling Approximation.” 2015. Web. 16 Apr 2021.

Vancouver:

Li Z. Chance Constrained Optimization with Robust and Sampling Approximation. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Apr 16]. Available from: https://era.library.ualberta.ca/files/rn301424z.

Council of Science Editors:

Li Z. Chance Constrained Optimization with Robust and Sampling Approximation. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/rn301424z

Texas A&M University

12. Modarresi, Mohammad Sadegh. A Scenario Approach for Operational Planning with Deep Renewables in Power Systems.

Degree: PhD, Electrical Engineering, 2019, Texas A&M University

URL: http://hdl.handle.net/1969.1/187168

► This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one… (more)

▼ This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal… Advisors/Committee Members: Xie, Le (advisor), Singh, Chanan (committee member), Gautam, Natarajan (committee member), Hou, I-Hong (committee member).

Subjects/Keywords: Chance constrained programming; demand response; economic dispatch; flexibility; electricity market; renewable generation; robust optimization; scenario approach

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Modarresi, M. S. (2019). A Scenario Approach for Operational Planning with Deep Renewables in Power Systems. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/187168

Chicago Manual of Style (16^th Edition):

Modarresi, Mohammad Sadegh. “A Scenario Approach for Operational Planning with Deep Renewables in Power Systems.” 2019. Doctoral Dissertation, Texas A&M University. Accessed April 16, 2021. http://hdl.handle.net/1969.1/187168.

MLA Handbook (7^th Edition):

Modarresi, Mohammad Sadegh. “A Scenario Approach for Operational Planning with Deep Renewables in Power Systems.” 2019. Web. 16 Apr 2021.

Vancouver:

Modarresi MS. A Scenario Approach for Operational Planning with Deep Renewables in Power Systems. [Internet] [Doctoral dissertation]. Texas A&M University; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1969.1/187168.

Council of Science Editors:

Modarresi MS. A Scenario Approach for Operational Planning with Deep Renewables in Power Systems. [Doctoral Dissertation]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/187168

University of Kentucky

13. Ibrahim, Sarmad Khaleel. DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION.

Degree: 2018, University of Kentucky

URL: https://uknowledge.uky.edu/ece_etds/116

► In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and… (more)

▼ In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and conventional volt-var control equipment: photovoltaic inverter reactive power control for chance-constrained distribution system performance optimisation, integrated distribution system optimization using a chance-constrained formulation, integrated control of distribution system equipment and distributed generation inverters, and coordination of PV inverters and voltage regulators considering generation correlation and voltage quality constraints for loss minimization. Distributed generation sources (DGs) have important benefits, including the use of renewable resources, increased customer participation, and decreased losses. However, as the penetration level of DGs increases, the technical challenges of integrating these resources into the power system increase as well. One such challenge is the rapid variation of voltages along distribution feeders in response to DG output fluctuations, and the traditional volt-var control equipment and inverter-based DG can be used to address this challenge. These methods aim to achieve an optimal expected performance with respect to the figure of merit of interest to the distribution system operator while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections. The first method is used to optimize only the reactive power output of DGs to improve system performance (e.g., operating profit) and compensate for variations in active power injection while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections over the interval of interest. The second method proposes an integrated volt-var control based on a control action ahead of time to find the optimal voltage regulation tap settings and inverter reactive control parameters to improve the expected system performance (e.g., operating profit) while keeping the voltages across the system within specified ranges and considering the uncertainty of DG power injections over the interval of interest. In the third method, an integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. This control strategy combines the use of inverter reactive power capability with the operation of voltage regulators to improve the expected value of the desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes. The fourth method proposes a coordinated control strategy of voltage and reactive power control equipment to improve the expected system performance (e.g., system losses and voltage profiles) while considering the spatial correlation among the DGs and keeping voltage magnitudes within permissible limits, by formulating chance constraints on the voltage magnitude and considering the uncertainty of PV power injections over the interval of interest. The…

Subjects/Keywords: Distributed power generation; chance-constrained programming; renewable integration; reactive power optimization; voltage control; Power and Energy

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ibrahim, S. K. (2018). DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/ece_etds/116

Chicago Manual of Style (16^th Edition):

Ibrahim, Sarmad Khaleel. “DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION.” 2018. Doctoral Dissertation, University of Kentucky. Accessed April 16, 2021. https://uknowledge.uky.edu/ece_etds/116.

MLA Handbook (7^th Edition):

Ibrahim, Sarmad Khaleel. “DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION.” 2018. Web. 16 Apr 2021.

Vancouver:

Ibrahim SK. DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION. [Internet] [Doctoral dissertation]. University of Kentucky; 2018. [cited 2021 Apr 16]. Available from: https://uknowledge.uky.edu/ece_etds/116.

Council of Science Editors:

Ibrahim SK. DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION. [Doctoral Dissertation]. University of Kentucky; 2018. Available from: https://uknowledge.uky.edu/ece_etds/116

UCLA

14. Maalouf, Sami. Planning and Design of Desalination Plants Effluent Systems.

Degree: Civil Engineering, 2014, UCLA

URL: http://www.escholarship.org/uc/item/60f4s0qn

► Increasing demand for water in urban areas and agricultural zones in arid and semi-arid coastal regions has urged planners and regulators to look for alternative… (more)

▼ Increasing demand for water in urban areas and agricultural zones in arid and semi-arid coastal regions has urged planners and regulators to look for alternative renewable water sources. Seawater reverse osmosis desalination (SWRO) plants have become an essential supply source for the production of freshwater in such regions. However, the disposal of hypersaline wastes from these plants in many of these regions has not been fully and properly addressed. This study aims to develop and present a strategy for the analysis and design of an optimal disposal system of wastes generated by SWRO desalination plants.After current disposal options were evaluated, the use of multiport marine outfalls is recommended as an effective disposal system. Marine outfalls are a reliable means for conveying wastes from process plants, to include wastewater treatment and power plants, into the coastal waters. Their proper use, however, in conjunction with SWRO desalination plants is still in its beginning stage.A simulation-optimization approach is proposed to design a system for safe disposal of brine wastes. This disposal system is comprised of a marine outfall that is equipped with a multiport diffuser structure. A hydrodynamic model (CORMIX) is used to assess the initial dilution of hypersaline effluent discharged into coastal waters. A regression model is developed to relate the input and output parameters of the simulation model. This regression model replaces the simulation model. A mixed-integer linear programming (MILP) optimization model is then formulated to determine the design of the multiport marine outfall. The design parameters are the length, diameter and number of ports of the disposal system. Given the uncertainty of some parameter, such as current speed, wind speed and ambient temperature, a chance-constrained programming model is used to properly incorporate these stochastic parameters into the model. This simulation-optimization framework provides planners with effective tools that preserve a healthy coastal environment, meet environmental permitting requirements and restrictions, while achieving cost savings and adequate hydrodynamic performance. A case study demonstrates the applicability of the proposed methodology.

Subjects/Keywords: Civil engineering; Environmental engineering; Operations research; Chance-constrained programming (CCP); Coastal pollution; Marine outfall; Mixed-integer linear programming (MILP); Optimization; RO Desalination brine discharge

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APA (6^th Edition):

Maalouf, S. (2014). Planning and Design of Desalination Plants Effluent Systems. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/60f4s0qn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Maalouf, Sami. “Planning and Design of Desalination Plants Effluent Systems.” 2014. Thesis, UCLA. Accessed April 16, 2021. http://www.escholarship.org/uc/item/60f4s0qn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Maalouf, Sami. “Planning and Design of Desalination Plants Effluent Systems.” 2014. Web. 16 Apr 2021.

Vancouver:

Maalouf S. Planning and Design of Desalination Plants Effluent Systems. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Apr 16]. Available from: http://www.escholarship.org/uc/item/60f4s0qn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Maalouf S. Planning and Design of Desalination Plants Effluent Systems. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/60f4s0qn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

15. Kůdela, Jakub. Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/180706

► When working with stochastic programming problems, we frequently encounter optimization problems that are too large to be processed by routine methods of mathematical programming. However,… (more)

Subjects/Keywords: stochastická optimalizace; stochastické programování; dekompoziční metody; úlohy dvoustupňového stochastického programování; úlohy s pravděpodobnostním omezením; stochastic optimization; stochastic programming; decomposition methods; two-stage stochastic programming problems; chance constrained problems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kůdela, J. (2019). Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180706

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kůdela, Jakub. “Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization.” 2019. Thesis, Brno University of Technology. Accessed April 16, 2021. http://hdl.handle.net/11012/180706.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kůdela, Jakub. “Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization.” 2019. Web. 16 Apr 2021.

Vancouver:

Kůdela J. Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11012/180706.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kůdela J. Advanced Decomposition Methods in Stochastic Convex Optimization: Advanced Decomposition Methods in Stochastic Convex Optimization. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180706

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Michigan

16. Joshi, Nikhil A. Methodologies for improving product development phases through PLM.

Degree: PhD, Mechanical engineering, 2007, University of Michigan

URL: http://hdl.handle.net/2027.42/126457

► Product designs are becoming increasingly complex; while at the same time product development is distributed amongst various stakeholders in the enterprise. Combined with these trends,… (more)

▼ Product designs are becoming increasingly complex; while at the same time product development is distributed amongst various stakeholders in the enterprise. Combined with these trends, recent regulations have forced manufacturers to manage the environmental impacts of their products. Manufacturers rely heavily on human expertise to manage issues arising due to these considerations at various stages of product development. This research develops methodologies that utilize the Product Lifecycle Management (PLM) framework to systematically address these issues. For the early design stage, a method has been developed to decide substance content specifications for in-house and supplied components, while taking into account all applicable regulations. The method incorporates supplier feedback about technical infeasibilities, as well as estimates of cost and quality, to optimize overall product performance. Chance constrained programming is used to allow designers to account for the uncertainty in these estimates at this early stage. Solution algorithms have been demonstrated to solve both individual chance constraint and joint chance constraint cases that arise. Modifications to completed designs are often required to meet newer regulations, or overcome regulatory violations, or high production costs. To reduce costs and delays due to changes in the later development stages, a systematic decision support system is developed, that will enable both quick and detailed evaluation of engineering changes. The system builds upon existing workflow management capabilities of PLM. The workflow for evaluating the change is generated dynamically using a predefined knowledge base to predict probable effects of making a change. The method further uses experience from earlier engineering changes to prioritize the evaluation of effects. Finally, we propose a framework to enable case-based planning of the treatment strategy for end-of-life products. For this purpose, we created geometric algorithms to identify and characterize joints in CAD assembly models of the product. Rule based methods are used to search the CAD model data-structure to detect presence of key geometric features in mating components to identify the type of joint. The joints are characterized with regards to size, location, and orientation, to aid in automated determination of costs and feasibilities of various disassembly operations. Advisors/Committee Members: Dutta, Debasish (advisor).

Subjects/Keywords: Chance-constrained Programming; Chance-constrained Programs; Dynamic Workflows; Improving; Methodologies; Phases; Plm; Product Development; Product Life Cycle Management; Product Lifecycle Management

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Joshi, N. A. (2007). Methodologies for improving product development phases through PLM. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/126457

Chicago Manual of Style (16^th Edition):

Joshi, Nikhil A. “Methodologies for improving product development phases through PLM.” 2007. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/126457.

MLA Handbook (7^th Edition):

Joshi, Nikhil A. “Methodologies for improving product development phases through PLM.” 2007. Web. 16 Apr 2021.

Vancouver:

Joshi NA. Methodologies for improving product development phases through PLM. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/126457.

Council of Science Editors:

Joshi NA. Methodologies for improving product development phases through PLM. [Doctoral Dissertation]. University of Michigan; 2007. Available from: http://hdl.handle.net/2027.42/126457

Penn State University

17. Solo, Christopher James. MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY.

Degree: 2009, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/9709

► This research involves the development of a flexible, multi-objective optimization tool for use by supply chain managers in the design and operation of manufacturing-distribution networks… (more)

▼ This research involves the development of a flexible, multi-objective optimization tool for use by supply chain managers in the design and operation of manufacturing-distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing supply chain response time. The development of the two-phase mathematical model parallels the supply chain planning process through the formulation of a strategic submodel for infrastructure design followed by a tactical submodel for operational planning. The deterministic strategic submodel, formulated as a multi-period, mixed integer linear programming model, considers an aggregate production planning problem in which long-term decisions such as plant construction, production capacities, and critical raw material supplier selections are optimized. These decisions are then used as inputs in the operational planning portion of the problem. The deterministic tactical submodel, formulated as a multi-period, mixed integer linear goal programming model, uses higher resolution demand and cost data, newly acquired transit time information, and the previously developed infrastructure to determine optimal non-critical raw material supplier selections; revised purchasing, production, inventory, and shipment quantities; and an optimal profit figure. The supply chain scenario is then modified to consider uncertain, long-term demand forecasts in the form of discrete economic scenarios. In this case, a multi-period, mixed integer robust optimization formulation of the strategic submodel is presented to account for the probabilistic demand data. Once the stochastic strategic submodel is presented, short-term, uncertain demand data is assumed to be available in the form of continuous probability distributions. By modifying decision makers’ objectives regarding demand satisfaction, the distribution-based demand data is accounted for through the development of a multi-period, mixed integer chance-constrained goal programming formulation of the tactical submodel. In order to demonstrate the flexibility of both the deterministic and stochastic versions of the overall two-phase model, numerical examples are presented and solved. The resulting work provides supply chain managers with a flexible tool that can aid in the design and operation of real-world production-distribution networks, where uncertain demand data is available at different times and in various forms. Advisors/Committee Members: Arunachalam Ravindran, Dissertation Advisor/Co-Advisor, Arunachalam Ravindran, Committee Chair/Co-Chair, Soundar Rajan Tirupatikumara, Committee Member, M Jeya Chandra, Committee Member, Susan H Xu, Committee Member.

Subjects/Keywords: supply chain; uncertainty; stochastic optimization; robust optimization; chance-constrained goal programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Solo, C. J. (2009). MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/9709

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Solo, Christopher James. “MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY.” 2009. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/9709.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Solo, Christopher James. “MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY.” 2009. Web. 16 Apr 2021.

Vancouver:

Solo CJ. MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY. [Internet] [Thesis]. Penn State University; 2009. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/9709.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Solo CJ. MULTI-OBJECTIVE, INTEGRATED SUPPLY CHAIN DESIGN AND OPERATION UNDER UNCERTAINTY. [Thesis]. Penn State University; 2009. Available from: https://submit-etda.libraries.psu.edu/catalog/9709

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

18. Fleming, James. Robust and stochastic MPC of uncertain-parameter systems.

Degree: PhD, 2016, University of Oxford

URL: https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730224

► Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with… (more)

▼ Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of âsample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.

Subjects/Keywords: 629.8; Convex Optimization; Stochastic Control; Chance Constrained Programming; Robust Control; Model Predictive Control; Optimal Control; Control; Receding-horizon; Optimal; Convex; Robust; Stochastic; Optimisation; MPC; Tube MPC; Constraints

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Fleming, J. (2016). Robust and stochastic MPC of uncertain-parameter systems. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730224

Chicago Manual of Style (16^th Edition):

Fleming, James. “Robust and stochastic MPC of uncertain-parameter systems.” 2016. Doctoral Dissertation, University of Oxford. Accessed April 16, 2021. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730224.

MLA Handbook (7^th Edition):

Fleming, James. “Robust and stochastic MPC of uncertain-parameter systems.” 2016. Web. 16 Apr 2021.

Vancouver:

Fleming J. Robust and stochastic MPC of uncertain-parameter systems. [Internet] [Doctoral dissertation]. University of Oxford; 2016. [cited 2021 Apr 16]. Available from: https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730224.

Council of Science Editors:

Fleming J. Robust and stochastic MPC of uncertain-parameter systems. [Doctoral Dissertation]. University of Oxford; 2016. Available from: https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730224

Brno University of Technology

19. Mrázková, Eva. Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/1571

► Many optimum design problems in engineering areas lead to optimization models constrained by ordinary (ODE) or partial (PDE) differential equations, and furthermore, several elements of… (more)

▼ Many optimum design problems in engineering areas lead to optimization models constrained by ordinary (ODE) or partial (PDE) differential equations, and furthermore, several elements of the problems may be uncertain in practice. Three engineering problems concerning the optimization of vibrations and an optimal design of beam dimensions are considered. The uncertainty in the form of random load or random Young's modulus is involved. It is shown that two-stage stochastic programming offers a promising approach in solving such problems. Corresponding mathematical models involving ODE or PDE type constraints, uncertain parameters and multiple criteria are formulated and lead to (multi-objective) stochastic nonlinear optimization models. It is also proved for which type of problems stochastic programming approach (EO reformulation) should be used and when it is sufficient to solve simpler deterministic problem (EV reformulation). This fact has the big importance in practice in term of computational intensity of large scale problems. Computational schemes for this type of problems are proposed, including discretization methods for random elements and ODE or PDE constraints. By means of derived approximations the mathematical models are implemented and solved in GAMS. The solution quality is determined by an interval estimate of the optimality gap computed via Monte Carlo bounding technique. Parametric analysis of multi-criteria model results in efficient frontier computation. The alternatives of approximations of the model with reliability-related probabilistic terms including mixed-integer nonlinear programming and penalty reformulations are discussed. Furthermore, the progressive hedging algorithm is implemented and tested for the selected problems with respect to future possibilities of parallel computing of large engineering problems. The results show that it can be used even when the mathematical conditions for convergence are not fulfilled. Finite difference method and finite element method are compared for deterministic version of ODE constrained problem by using GAMS and ANSYS with quite comparable results. Advisors/Committee Members: Karpíšek, Zdeněk (advisor), Horová, Ivana (referee), Štěpánek, Petr (referee).

Subjects/Keywords: optimální inženýrský návrh; ODR a PDR omezení; stochastické programování; optimalizace s pravděpodobnostními omezeními; vícekriteriální optimalizace; metoda Monte Carlo; PHA algoritmus; optimum engineering design; ODE and PDE constraints; stochastic programming; chance constrained programming; multi-objective programming; Monte Carlo method; progressive hedging algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mrázková, E. (2019). Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/1571

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mrázková, Eva. “Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications.” 2019. Thesis, Brno University of Technology. Accessed April 16, 2021. http://hdl.handle.net/11012/1571.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mrázková, Eva. “Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications.” 2019. Web. 16 Apr 2021.

Vancouver:

Mrázková E. Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11012/1571.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mrázková E. Approximations in Stochastic Optimization and Their Applications: Approximations in Stochastic Optimization and Their Applications. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/1571

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Maryland

20. Nair, Rahul. Design and Analysis of Vehicle Sharing Programs: A Systems Approach.

Degree: Civil Engineering, 2010, University of Maryland

URL: http://hdl.handle.net/1903/10968

► Transit, touted as a solution to urban mobility problems, cannot match the addictive flexibility of the automobile. 86% of all trips in the U.S. are… (more)

▼ Transit, touted as a solution to urban mobility problems, cannot match the addictive flexibility of the automobile. 86% of all trips in the U.S. are in personal vehicles. A more recent approach to reduce automobile dependence is through the use of Vehicle Sharing Programs (VSPs). A VSP involves a fleet of vehicles located strategically at stations across the transportation network. In its most flexible form, users are free to check out vehicles at any station and return the vehicle at a station close to their destination. Vehicle fleets are comprised of bicycles, cars or electric vehicles. Such systems offer innovative solutions to the larger mobility problem and can have positive impacts on the transportation system as a whole by reducing urban congestion. This dissertation employs a network modeling framework to quantitatively design and operate VSPs. At the strategic level, the problem of determining the optimal VSP configuration is studied. A bilevel optimization model and associated solution methods are developed and implemented for a large-scale case study in Washington D.C. The model explicitly considers the intermodalism, and views the VSP as a `last-mile' connection of an existing transit network. At the operational level, by transferring control of vehicles to the user for improved system flexibility, exceptional logistical challenges are placed on operators who must ensure adequate vehicle stock (and parking slots) at each station to service all demand. Since demand in the short-term can be asymmetric (flow from one station to another is seldom equal to flow in the opposing direction), service providers need to redistribute vehicles to correct this imbalance. A chance-constrained program is developed that generates least-cost redistribution plans such that most demand in the near future is met. Since the program has a non-convex feasible region, two methods for its solution are developed. The model is applied to a real-world car-sharing system in Singapore where the value of accounting for inherent stochasticities is demonstrated. The framework is used to characterize the efficiency of Velib, a large-scale bicycle sharing system in Paris, France. Advisors/Committee Members: Miller-Hooks, Elise D (advisor).

Subjects/Keywords: Transportation; Urban and Regional Planning; Engineering, Civil; Bicycle sharing; Bilevel programming; Car sharing; Chance constrained programming; Equilibrium network design; Stochastic optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Nair, R. (2010). Design and Analysis of Vehicle Sharing Programs: A Systems Approach. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/10968

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nair, Rahul. “Design and Analysis of Vehicle Sharing Programs: A Systems Approach.” 2010. Thesis, University of Maryland. Accessed April 16, 2021. http://hdl.handle.net/1903/10968.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nair, Rahul. “Design and Analysis of Vehicle Sharing Programs: A Systems Approach.” 2010. Web. 16 Apr 2021.

Vancouver:

Nair R. Design and Analysis of Vehicle Sharing Programs: A Systems Approach. [Internet] [Thesis]. University of Maryland; 2010. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1903/10968.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nair R. Design and Analysis of Vehicle Sharing Programs: A Systems Approach. [Thesis]. University of Maryland; 2010. Available from: http://hdl.handle.net/1903/10968

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Central Florida

21. Huang, Yuping. Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm.

Degree: 2014, University of Central Florida

URL: https://stars.library.ucf.edu/etd/4812

► As energy demands increase and energy resources change, the traditional energy system has been upgraded and reconstructed for human society development and sustainability. Considerable studies… (more)

▼ As energy demands increase and energy resources change, the traditional energy system has been upgraded and reconstructed for human society development and sustainability. Considerable studies have been conducted in energy expansion planning and electricity generation operations by mainly considering the integration of traditional fossil fuel generation with renewable generation. Because the energy market is full of uncertainty, we realize that these uncertainties have continuously challenged market design and operations, even a national energy policy. In fact, only a few considerations were given to the optimization of energy expansion and generation taking into account the variability and uncertainty of energy supply and demand in energy markets. This usually causes an energy system unreliable to cope with unexpected changes, such as a surge in fuel price, a sudden drop of demand, or a large renewable supply fluctuation. Thus, for an overall energy system, optimizing a long-term expansion planning and market operation in a stochastic environment are crucial to improve the system's reliability and robustness. As little consideration was paid to imposing risk measure on the power management system, this dissertation discusses applying risk-constrained stochastic programming to improve the efficiency, reliability and economics of energy expansion and electric power generation, respectively. Considering the supply-demand uncertainties affecting the energy system stability, three different optimization strategies are proposed to enhance the overall reliability and sustainability of an energy system. The first strategy is to optimize the regional energy expansion planning which focuses on capacity expansion of natural gas system, power generation system and renewable energy system, in addition to transmission network. With strong support of NG and electric facilities, the second strategy provides an optimal day-ahead scheduling for electric power generation system incorporating with non-generation resources, i.e. demand response and energy storage. Because of risk aversion, this generation scheduling enables a power system qualified with higher reliability and promotes non-generation resources in smart grid. To take advantage of power generation sources, the third strategy strengthens the change of the traditional energy reserve requirements to risk constraints but ensuring the same level of systems reliability In this way we can maximize the use of existing resources to accommodate internal or/and external changes in a power system. All problems are formulated by stochastic mixed integer programming, particularly considering the uncertainties from fuel price, renewable energy output and electricity demand over time. Taking the benefit of models structure, new decomposition strategies are proposed to decompose the stochastic unit commitment problems which are then solved by an enhanced Benders Decomposition algorithm. Compared to the classic Benders Decomposition, this proposed solution approach is able to increase… Advisors/Committee Members: Zheng, Qipeng.

Subjects/Keywords: Stochastic integer programming; chance constrained programming; benders decomposition; conditional value at risk; expansion planning; power system; unit commitment; operating reserve; Engineering; Industrial Engineering; Dissertations, Academic – Engineering and Computer Science; Engineering and Computer Science – Dissertations, Academic

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Huang, Y. (2014). Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm. (Doctoral Dissertation). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/4812

Chicago Manual of Style (16^th Edition):

Huang, Yuping. “Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm.” 2014. Doctoral Dissertation, University of Central Florida. Accessed April 16, 2021. https://stars.library.ucf.edu/etd/4812.

MLA Handbook (7^th Edition):

Huang, Yuping. “Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm.” 2014. Web. 16 Apr 2021.

Vancouver:

Huang Y. Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm. [Internet] [Doctoral dissertation]. University of Central Florida; 2014. [cited 2021 Apr 16]. Available from: https://stars.library.ucf.edu/etd/4812.

Council of Science Editors:

Huang Y. Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm. [Doctoral Dissertation]. University of Central Florida; 2014. Available from: https://stars.library.ucf.edu/etd/4812

University of Alberta

22. Danso,George K. Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation.

Degree: PhD, Department of Resource Economics and Environmental Sociology, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/1c18dg416

► The overall purpose of the study is to evaluate how water trading could improve water use efficiency in southern Alberta, Canada and how benefits of… (more)

▼ The overall purpose of the study is to evaluate how water trading could improve water use efficiency in southern Alberta, Canada and how benefits of water reallocation could be achieved in the Nile River basin in Africa. The impact of water scarcity has become more prominent in these areas in recent years because of increasing population growth, urbanization rates, and unexpected changes in climate patterns. The ability to supply water to meet the needs of multiple sectors of the society is a compelling challenge to policy makers in the developed and in the developing world. In the first paper, the gain of adopting efficient irrigation technologies as a major water conservation strategy is assessed in southern Alberta, Canada. Water trading is modeled with a choice of irrigation technology adoption. Simulation results show that farmers will be willing to use efficient irrigation technologies when the net gains from adoption are higher than the cost of adoption. However, the adoption of most efficient irrigation technologies is more likely to occur when water conservation-induced polices are provided in the South Saskatchewan River Basin (SSRB). In the second paper, the economic impact of altering the current agreement governing the Nile River Basin is assessed. The Nile River basin is still governed by the 1959 agreement signed between Egypt and Sudan, without the upstream countries. With this agreement, of the annual average 84 billion cubic meters (BCM) of Nile River water, 66 percent is allocated to Egypt and 22 percent to Sudan with the remaining 12 percent going to surface evaporation and seepage at the Aswan High Dam in Egypt. The simulation results show that under certainty conditions, reallocation of water to Ethiopia would have minimal impact on the economies of Egypt and Sudan. However, under stochastic conditions, a greater negative impact is observed in the agricultural sector while in both countries the industrial and services sectors improve. Overall, there is a net welfare gain of 3.1 percent of Gross Domestic Product (GDP) of all the three countries under certainty conditions of water reallocation. Under stochastic conditions, however, there is a 0.53 percent net welfare loss of GDP to the three countries with water reallocation. These results tend to suggest that if these countries could cooperate, it would be possible to mitigate the negative impacts of water reallocation on Egypt and Sudan.

Subjects/Keywords: water market, technology adoption, stochasitc modeling, risks and uncertainity; farm level simulation, profit model, CGE model, chance constrained programming; Water trading,water markets, irrigation technology adoption, economic impacts,water reallocation, transboundary cooperation, benefits sharing,Nile River basin, Southern Alberta

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

K, D. (2014). Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/1c18dg416

Chicago Manual of Style (16^th Edition):

K, Danso,George. “Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation.” 2014. Doctoral Dissertation, University of Alberta. Accessed April 16, 2021. https://era.library.ualberta.ca/files/1c18dg416.

MLA Handbook (7^th Edition):

K, Danso,George. “Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation.” 2014. Web. 16 Apr 2021.

Vancouver:

K D. Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2021 Apr 16]. Available from: https://era.library.ualberta.ca/files/1c18dg416.

Council of Science Editors:

K D. Water Trading: Irrigation Technology Adoption and Economic Impact of Transboundary Water Reallocation. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/1c18dg416

University of Houston

23. -4801-0852. Radiation Therapy Optimization Considering Uncertainties and Biological Effects.

Degree: PhD, Industrial Engineering, University of Houston

URL: http://hdl.handle.net/10657/3677

► The goal in radiation therapy is to maximize tumor cell killing while minimizing toxic effects on surrounding healthy tissues. A treatment protocol is generally used… (more)

▼ The goal in radiation therapy is to maximize tumor cell killing while minimizing toxic effects on surrounding healthy tissues. A treatment protocol is generally used to decide on the treatment strategy and is a description of the desired radiation dose to the various regions of interest. Treatment planning then aims to find a plan as close to the treatment protocol as possible. Every step of radiation therapy is subject to some types of uncertainties (i.e., set-up uncertainty, patient motion, and tumor shrinkage), which may compromise the quality of a treatment. Therefore, this dissertation focuses primarily on optimization approaches to meet prescription requirements and manage the uncertainties in radiation therapy treatments. First, the problem of satisfying dose-volume constraints (DVCs) in the fluence map optimization (FMO) is explored. DVCs are normally used to prescribe and control the dose to the target and the healthy structures in a treatment protocol. Solving the FMO problem while satisfying DVCs often requires the use of tedious trial-and-error. Therefore, an automatic approach is proposed to satisfy DVCs using a multi-objective linear programming (LP) model for solving beamlet intensities. The plan quality and the satisfaction of the DVCs by the proposed algorithm are compared with two nonlinear approaches: a nonlinear FMO model and a commercial treatment planning system. Numerical results show that the proposed approach successfully improved the target coverage to meet the DVCs, while trying to keep OAR DVCs satisfied. Second, sharp gradients in intensity modulated proton therapy (IMPT) dose distributions can lead to treatment plans that are very sensitive to uncertainties. Robust optimization takes the uncertainties into account and leads to dose distributions that are resilient to uncertainties and of better quality than conventional approaches. The purpose of this study is to evaluate and compare the performance and effectiveness (in terms of plan quality, robustness, and delivery efficiency) of the two robust optimization approaches (worst case dose and minmax) and the conventional planning target volume (PTV)-based optimization using LP and NLP (nonlinear programming) models. The results show that LP-based methods are suitable for less challenging cancer cases where uncertainty scenarios are favorable for LP with tight constraints for finding a feasible solution. Moreover, plans generated using LP-based methods have notably fewer scanning spots than did those created using NLP-based methods, possibly leading to more efficient delivery. Third, A chance constrained programming (CCP) framework is proposed to handle uncertainties in radiation treatment planning that allows constraint violation up to a certain degree as it is the case in practice. The CCP framework can potentially be employed under different distributional assumptions. The goal of a CCP optimization model is to maximize the confidence level of the plans and the homogeneity of the dose distributions. To generalize CCP models,… Advisors/Committee Members: Lim, Gino J. (advisor), Feng, Qianmei (committee member), Tekin, Eylem (committee member), Vipulanandan, Cumaraswamy (committee member), Mohan, Radhe (committee member).

Subjects/Keywords: Radiation therapy; Robust optimization; Chance Constrained Programming; Relative Biological Effectiveness; Dose-Volume Histogram; Dose-Volume Constraint

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APA (6^th Edition):

-4801-0852. (n.d.). Radiation Therapy Optimization Considering Uncertainties and Biological Effects. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/3677

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
No year of publication.

Chicago Manual of Style (16^th Edition):

-4801-0852. “Radiation Therapy Optimization Considering Uncertainties and Biological Effects.” Doctoral Dissertation, University of Houston. Accessed April 16, 2021. http://hdl.handle.net/10657/3677.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
No year of publication.

MLA Handbook (7^th Edition):

-4801-0852. “Radiation Therapy Optimization Considering Uncertainties and Biological Effects.” Web. 16 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
No year of publication.

Vancouver:

-4801-0852. Radiation Therapy Optimization Considering Uncertainties and Biological Effects. [Internet] [Doctoral dissertation]. University of Houston; [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10657/3677.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
No year of publication.

Council of Science Editors:

-4801-0852. Radiation Therapy Optimization Considering Uncertainties and Biological Effects. [Doctoral Dissertation]. University of Houston; Available from: http://hdl.handle.net/10657/3677

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
No year of publication.

24. Araújo, Julyana Kelly Tavares de. Otimização sob restrições probabilísticas: teoria e aplicações.

Degree: 2012, Universidade Federal da Paraíba; Programa de Pós Graduação em Engenharia de Produção; UFPB; BR; Engenharia de Produção

URL: https://repositorio.ufpb.br/jspui/handle/tede/5236

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Made available in DSpace… (more)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

This Project brings a Chance Constrained Programming substantial approaching (CCP). This kind of optimization is used to pattern uncertainties and became useful to all kind of knowledge areas. The project main idea was to show CCP s theories and beyond this to present some applications on Engineering and Public Politics areas. It is noteworthy to say that this tool is pretty important for the production systems because of its uncertainties process. So after showing the theory whose purpose is to comprehend the Chance Constrained Programming, this subject commits itself to apply such technique in Emergency Medical Care Production Services (SAMU) in João Pessoa using the proposed model from Beraldi et al. (2004). This application was really useful to define the necessary ambulances to supply João Pessoa s city as well as the local they must be. However, to understand this technique and also work with it it s necessary a previous knowledge of Statistics, Applied Mathematics and Computing. Therefore, this work emphasizes the continuous and discreet random variables, as well as the probabilistic functions and concepts. In Applied Mathematics, this work brings a Linear Optimization, Facility Location and log concave functions. Concerning to computing, it was used MATLAB R007, Google Maps and CPLEX to provide the model. The great benefit of using CCP is that it offers possible solutions to the person who chooses between them, according to the reality.

Este trabalho apresenta uma abordagem de Otimização Probabilística (OP). Esse tipo de Otimização é utilizada para modelar incertezas e se tornou útil em diversas áreas do conhecimento. O objetivo principal deste trabalho foi apresentar a teoria de OP e, além disso, expor algumas aplicações nas áreas de Engenharia e Políticas Públicas. Vale ressaltar que tal ferramenta é muito interessante para Sistemas de Produção por existir incertezas inerentes ao processo. Assim, depois de apresentada tal teoria, com o intuito de melhor compreender a melhor a ferramenta de OP, este trabalho, se propôs a aplicar tal técnica no Sistema de Produção dos Serviços de Atendimento Médico de Urgência (SAMU) da cidade João Pessoa usando o Modelo proposto por Beraldi et al.(2004). A aplicação serviu para definir a quantidade de ambulâncias necessárias para atender a demanda de João Pessoa, assim como os possíveis locais que as mesmas devem estar posicionadas. No entanto, para entender melhor sobre essa técnica e trabalhar com a mesma, é necessário um conhecimento prévio…

Advisors/Committee Members: Nascimento, Roberto Quirino do.

Subjects/Keywords: Otimização Probabilísticas; Incertezas no Processo; Distribuição de Poisson; Serviços de Atendimento Médico de Urgências; Chance Constrained Programming; Uncertainties in Process; Poisson Distribution; Emergency Medical Care Production Services; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Araújo, J. K. T. d. (2012). Otimização sob restrições probabilísticas: teoria e aplicações. (Masters Thesis). Universidade Federal da Paraíba; Programa de Pós Graduação em Engenharia de Produção; UFPB; BR; Engenharia de Produção. Retrieved from https://repositorio.ufpb.br/jspui/handle/tede/5236

Chicago Manual of Style (16^th Edition):

Araújo, Julyana Kelly Tavares de. “Otimização sob restrições probabilísticas: teoria e aplicações.” 2012. Masters Thesis, Universidade Federal da Paraíba; Programa de Pós Graduação em Engenharia de Produção; UFPB; BR; Engenharia de Produção. Accessed April 16, 2021. https://repositorio.ufpb.br/jspui/handle/tede/5236.

MLA Handbook (7^th Edition):

Araújo, Julyana Kelly Tavares de. “Otimização sob restrições probabilísticas: teoria e aplicações.” 2012. Web. 16 Apr 2021.

Vancouver:

Araújo JKTd. Otimização sob restrições probabilísticas: teoria e aplicações. [Internet] [Masters thesis]. Universidade Federal da Paraíba; Programa de Pós Graduação em Engenharia de Produção; UFPB; BR; Engenharia de Produção; 2012. [cited 2021 Apr 16]. Available from: https://repositorio.ufpb.br/jspui/handle/tede/5236.

Council of Science Editors:

Araújo JKTd. Otimização sob restrições probabilísticas: teoria e aplicações. [Masters Thesis]. Universidade Federal da Paraíba; Programa de Pós Graduação em Engenharia de Produção; UFPB; BR; Engenharia de Produção; 2012. Available from: https://repositorio.ufpb.br/jspui/handle/tede/5236

25. Qiu, Feng. Probabilistic covering problems.

Degree: PhD, Industrial and Systems Engineering, 2013, Georgia Tech

URL: http://hdl.handle.net/1853/47567

► This dissertation studies optimization problems that involve probabilistic covering constraints. A probabilistic constraint evaluates and requires that the probability that a set of constraints involving… (more)

▼ This dissertation studies optimization problems that involve probabilistic covering constraints. A probabilistic constraint evaluates and requires that the probability that a set of constraints involving random coefficients with known distributions hold satisfy a minimum requirement. A covering constraint involves a linear inequality on non-negative variables with a greater or equal to sign and non-negative coefficients. A variety of applications, such as set cover problems, node/edge cover problems, crew scheduling, production planning, facility location, and machine learning, in uncertain settings involve probabilistic covering constraints. In the first part of this dissertation we consider probabilistic covering linear programs. Using the sampling average approximation (SAA) framework, a probabilistic covering linear program can be approximated by a covering k-violation linear program (CKVLP), a deterministic covering linear program in which at most k constraints are allowed to be violated. We show that CKVLP is strongly NP-hard. Then, to improve the performance of standard mixed-integer programming (MIP) based schemes for CKVLP, we (i) introduce and analyze a coefficient strengthening scheme, (ii) adapt and analyze an existing cutting plane technique, and (iii) present a branching technique. Through computational experiments, we empirically verify that these techniques are significantly effective in improving solution times over the CPLEX MIP solver. In particular, we observe that the proposed schemes can cut down solution times from as much as six days to under four hours in some instances. We also developed valid inequalities arising from two subsets of the constraints in the original formulation. When incorporating them with a modified coefficient strengthening procedure, we are able to solve a difficult probabilistic portfolio optimization instance listed in MIPLIB 2010, which cannot be solved by existing approaches. In the second part of this dissertation we study a class of probabilistic 0-1 covering problems, namely probabilistic k-cover problems. A probabilistic k-cover problem is a stochastic version of a set k-cover problem, which is to seek a collection of subsets with a minimal cost whose union covers each element in the set at least k times. In a stochastic setting, the coefficients of the covering constraints are modeled as Bernoulli random variables, and the probabilistic constraint imposes a minimal requirement on the probability of k-coverage. To account for absence of full distributional information, we define a general ambiguous k-cover set, which is ``distributionally-robust." Using a classical linear program (called the Boolean LP) to compute the probability of events, we develop an exact deterministic reformulation to this ambiguous k-cover problem. However, since the boolean model consists of exponential number of auxiliary variables, and hence not useful in practice, we use two linear program based bounds on the probability that at least k events occur, which can be obtained by… Advisors/Committee Members: Ahmed, Shabbir (Committee Chair), Dey, Santanu S. (Committee Co-Chair), Goldberg, David A. (Committee Member), Johnson, Ellis (Committee Member), Luedtke, James (Committee Member), Nemhauser, George (Committee Member).

Subjects/Keywords: Optimization; Stochastic programming; Chance-constrained program; Mixed-integer program; Probabilistic program; Covering problem; Mathematical optimization; Linear programming

…x28;or chance constrained programs), i.e., min f (x) s.t. P{G(x, ζ… …constraint to each of the constraints individually. Probabilistically constrained programming was… …chance constrained problems as long as the underlying distribution can be sampled because the… …constrained (or chance-constrained) optimization approach to address covering problems… …integer programming (MIP) based schemes for CKVLP, we (i) introduce and…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Qiu, F. (2013). Probabilistic covering problems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47567

Chicago Manual of Style (16^th Edition):

Qiu, Feng. “Probabilistic covering problems.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 16, 2021. http://hdl.handle.net/1853/47567.

MLA Handbook (7^th Edition):

Qiu, Feng. “Probabilistic covering problems.” 2013. Web. 16 Apr 2021.

Vancouver:

Qiu F. Probabilistic covering problems. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1853/47567.

Council of Science Editors:

Qiu F. Probabilistic covering problems. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47567

Indian Institute of Science

26. Jagarlapudi, Saketha Nath. Learning Algorithms Using Chance-Constrained Programs.

Degree: PhD, Faculty of Engineering, 2010, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/733

► This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning… (more)

▼ This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with specified error rates, large dataset classification and Ordinal Regression (OR). Using moments of training data, the CCPs are posed as Second Order Cone Programs (SOCPs). Novel iterative algorithms for solving the resulting SOCPs are also derived. Borrowing ideas from robust optimization theory, the proposed formulations are made robust to moment estimation errors. A maximum margin classifier with specified false positive and false negative rates is derived. The key idea is to employ chance-constraints for each class which imply that the actual misclassification rates do not exceed the specified. The formulation is applied to the case of biased classification. The problems of large dataset classification and ordinal regression are addressed by deriving formulations which employ chance-constraints for clusters in training data rather than constraints for each data point. Since the number of clusters can be substantially smaller than the number of data points, the resulting formulation size and number of inequalities are very small. Hence the formulations scale well to large datasets. The scalable classification and OR formulations are extended to feature spaces and the kernelized duals turn out to be instances of SOCPs with a single cone constraint. Exploiting this speciality, fast iterative solvers which outperform generic SOCP solvers, are proposed. Compared to state-of-the-art learners, the proposed algorithms achieve a speed up as high as 10000 times, when the specialized SOCP solvers are employed. The proposed formulations involve second order moments of data and hence are susceptible to moment estimation errors. A generic way of making the formulations robust to such estimation errors is illustrated. Two novel confidence sets for moments are derived and it is shown that when either of the confidence sets are employed, the robust formulations also yield SOCPs. Advisors/Committee Members: Bhattacharyya, Chiranjib (advisor).

Subjects/Keywords: Machine Learning; Classification; Dataset Classification; Ordinal Regression (OR); Chance-Constrained Programming (CCP); Classification - Algorithms; Ordinal Regression - Algorithms; Machine Learning - Algorithms; Second Order Cone Programs (SOCPs); Maximum Margin Classification; Focused Crawling; Large Datasets; Error Rates; Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jagarlapudi, S. N. (2010). Learning Algorithms Using Chance-Constrained Programs. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/733

Chicago Manual of Style (16^th Edition):

Jagarlapudi, Saketha Nath. “Learning Algorithms Using Chance-Constrained Programs.” 2010. Doctoral Dissertation, Indian Institute of Science. Accessed April 16, 2021. http://etd.iisc.ac.in/handle/2005/733.

MLA Handbook (7^th Edition):

Jagarlapudi, Saketha Nath. “Learning Algorithms Using Chance-Constrained Programs.” 2010. Web. 16 Apr 2021.

Vancouver:

Jagarlapudi SN. Learning Algorithms Using Chance-Constrained Programs. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2010. [cited 2021 Apr 16]. Available from: http://etd.iisc.ac.in/handle/2005/733.

Council of Science Editors:

Jagarlapudi SN. Learning Algorithms Using Chance-Constrained Programs. [Doctoral Dissertation]. Indian Institute of Science; 2010. Available from: http://etd.iisc.ac.in/handle/2005/733

University of South Africa

27. Adeyefa, Segun Adeyemi. Satisficing solutions for multiobjective stochastic linear programming problems .

Degree: 2011, University of South Africa

URL: http://hdl.handle.net/10500/5703

► Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management… (more)

Subjects/Keywords: Multiobjective programming; Stochastic programming; Linear programming; Satisfying solution; Chance constrained; Expected value optimality/efficiency; Variance optimality/efficiency; Expected value and standard deviation optimality/efficiency; Minimum risk optimality/efficiency; Optimality/efficiency with given probabilities; Fuzzy random variables; Random closed sets; Embedding theorem

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Adeyefa, S. A. (2011). Satisficing solutions for multiobjective stochastic linear programming problems . (Doctoral Dissertation). University of South Africa. Retrieved from http://hdl.handle.net/10500/5703

Chicago Manual of Style (16^th Edition):

Adeyefa, Segun Adeyemi. “Satisficing solutions for multiobjective stochastic linear programming problems .” 2011. Doctoral Dissertation, University of South Africa. Accessed April 16, 2021. http://hdl.handle.net/10500/5703.

MLA Handbook (7^th Edition):

Adeyefa, Segun Adeyemi. “Satisficing solutions for multiobjective stochastic linear programming problems .” 2011. Web. 16 Apr 2021.

Vancouver:

Adeyefa SA. Satisficing solutions for multiobjective stochastic linear programming problems . [Internet] [Doctoral dissertation]. University of South Africa; 2011. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10500/5703.

Council of Science Editors:

Adeyefa SA. Satisficing solutions for multiobjective stochastic linear programming problems . [Doctoral Dissertation]. University of South Africa; 2011. Available from: http://hdl.handle.net/10500/5703

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Open Access Theses and Dissertations