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University of Texas – Austin
1.
Zhang, Chuwen, active 21st century.
Controlling work in process during semiconductor assembly and test operations.
Degree: MSin Engineering, Operations Research and Industrial Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/62683 
► In the semiconductor industry, products go through a series of steps over a three- to four-month period that begins with the fabrication of chips and…
(more)
▼ In the semiconductor industry, products go through a series of steps over a three- to four-month period that begins with the fabrication of chips and ends with assembly and test (AT) and shipment. This paper introduces a mid-term planning model for scheduling AT operations aimed at minimizing the difference between customer demand and product completions each day. A secondary objective is to maximize daily throughput. Typically, semiconductor companies have 1000s of products or devices in their catalog that can be organized into unique groups of up to 100 devices each. This simplifies the planning process because it is only necessary to consider the groups as a whole rather than the individual devices when constructing schedules.
In all, we developed and tested three related models. Each provides daily run rates at each processing step or logpoint for each device group for up to one month at a time. The models are distinguished by how cycle time is treated. The first takes a steady-state approach and uses Little’s Law to formulate a WIP target constraint based on the average cycle time at each processing step. The second and third include integer and fractional cycle times in the variable definitions. To find solutions, raw production data are analyzed in a preprocessing step and then converted to input files in a standard format. FlopC++ from the COIN-OR open source software project is used to write and solve the model. Testing was done using three datasets from the Taiwan AT facility of a global semiconductor firm. By comparing model output with historical data for 6 device groups and 33 logpoints, we were able to realize decreases in shortages of up to 40% per month.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Chacon, Rodolfo (committee member).
Subjects/Keywords: Semiconductor; Work-in-process; Optimization
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APA (6th Edition):
Zhang, Chuwen, a. 2. c. (2017). Controlling work in process during semiconductor assembly and test operations. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62683
Chicago Manual of Style (16th Edition):
Zhang, Chuwen, active 21st century. “Controlling work in process during semiconductor assembly and test operations.” 2017. Masters Thesis, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/62683.
MLA Handbook (7th Edition):
Zhang, Chuwen, active 21st century. “Controlling work in process during semiconductor assembly and test operations.” 2017. Web. 05 Mar 2021.
Vancouver:
Zhang, Chuwen a2c. Controlling work in process during semiconductor assembly and test operations. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/62683.
Council of Science Editors:
Zhang, Chuwen a2c. Controlling work in process during semiconductor assembly and test operations. [Masters Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62683
2.
Deng, Yumin.
Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing.
Degree: PhD, Operations Research and Industrial Engineering, 2009, University of Texas – Austin
URL: http://hdl.handle.net/2152/17309
► Planning and scheduling in semiconductor manufacturing is a difficult problem due to long cycle times, a large number of operational steps, diversified product types, and…
(more)
▼ Planning and scheduling in semiconductor manufacturing is a difficult problem due to long cycle times, a large number of operational steps, diversified product types, and low-volume high-mix customer demand. This research addresses several problems that arise in the semiconductor industry related to front-end wafer fabrication operations and back-end assembly and test operations. The mathematical models built for these problems turn out to be large-scale mixed integer programs and hard to solve with exact methods. The major contribution of this research is to combine mathematical programming with metaheuristics to find high quality solutions within the time limits imposed by the industrial engineers who oversee the fabrication and test facilities. In order to reduce the size of problems that arise in practice, it is common to cluster similar product types into groups that reflect their underlying technology. The first part of the research is aimed at developing a greedy randomized adaptive search procedure (GRASP) coupled with path relinking (PR) to solve the capacitated clustering problem. The model is generic and can be applied in many different situations. The objective is to maximize a similarity measure within each cluster such that the sum of the weights associated with the product types does not exceed the cluster capacity in each case. In phase I, both a heaviest weight edge (HWE) algorithm and a constrained minimum cut (CMC) algorithm are used to select seeds for initializing the clusters. Feasible solutions are obtained with the help of a self-adjusting restricted candidate list. In phase II, three neighborhoods are defined and explored using the following strategies: cyclic neighborhood search, variable neighborhood descent, and randomized variable neighborhood descent (RVND). The best solutions found are stored in an elite pool. In a post-processing step, PR coupled with local search is applied to the pool members to cyclically generate paths between each pair. The elite pool is updated after each iteration and the procedure ends when no further improvement is possible. After grouping the product types into technologies, a new model is presented for production planning in a high volume fab that uses quarterly commitments to define daily target outputs. Rather than relying on due dates and priority rules to schedule lot starts and move work in process through the shop, the objective is to minimize the sum of the deviations between the target outputs and finished goods inventory. The model takes the form of a large-scale linear program that is intractable for planning horizons beyond a few days. Both Lagrangian relaxation and Benders decomposition were investigated but each proved ineffective. As a consequence, a methodology was developed which was more tailored to the problem’s structure. This involved creating weekly subproblems that were myopic but could be solved to optimality within a few minutes, and then postprocessing the results with a decomposition algorithm to fully utilize the excessive…
Advisors/Committee Members: Bard, Jonathan F. (advisor).
Subjects/Keywords: Semiconductors; Wafer fabrication; Back-end assembly; Test operations; Greedy randomized adaptive search procedure; Path relinking; Capacitated clustering problem
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APA (6th Edition):
Deng, Y. (2009). Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/17309
Chicago Manual of Style (16th Edition):
Deng, Yumin. “Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing.” 2009. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/17309.
MLA Handbook (7th Edition):
Deng, Yumin. “Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing.” 2009. Web. 05 Mar 2021.
Vancouver:
Deng Y. Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2009. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/17309.
Council of Science Editors:
Deng Y. Combining mathematical programming and enhanced GRASP metaheuristics : an application to semiconductor manufacturing. [Doctoral Dissertation]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/17309
3.
Purnomo, Hadi Waskito.
Solving midterm and short-term nurse scheduling problems.
Degree: PhD, Operations Research & Industrial Engineering, 2005, University of Texas – Austin
URL: http://hdl.handle.net/2152/30229
► As in many service organizations, hospitals use a variety of shift types when scheduling nurse resources. In general, the operational decisions of workforce planning can…
(more)
▼ As in many service organizations, hospitals use a variety of shift types when scheduling nurse resources. In general, the operational decisions of workforce planning can be divided into two interrelated problems: (1) midterm planning in terms of shift assignments for up to six weeks at a time, and (2) the short-term daily adjustment of schedules. Individual nurse profiles are a function of a unit's skill requirements, labor laws, and other qualifications, and are results of the long-term planning decision. At the midterm level, the goal is to match nurse resources with the expected workload over the planning horizon. Rosters are designed to maximize personnel preferences as well as minimize cost. To investigate this problem, a large-scale integer program model was developed and solved with two methodologies. The first is based on Lagrangian Relaxation based heuristic, which uses a combination of subgradient optimization and Bundle methods, with variable fixing strategy and IP-based heuristic. The second methodology is a branch-and-price algorithm that makes use of several new branching rules, an extremely effective rounding heuristic, a dual bound procedure, and specialized aggregation scheme. To extend the algorithms to solve different levels of nursing skills, a downgrading strategy is used by giving scheduling priorities to higher level of worker. The midterm schedules provide a blueprint for the monthly work assignments of the staff. Because of absenteeism and unpredicted demand fluctuations, though, a hospital-wide reallocation of resources is needed on a daily basis. While the overall goal is to ensure adequate coverage at minimize cost, a secondary goal is to minimize changes to the assigned rosters. Nevertheless, to allow more flexibility, nurses are permitted to work in several units during a shift rather than just their home unit. An IP-based column generation methodology was developed to solve this problem and applied within a rolling horizon framework. The idea is to consider 24 hours at a time, but implement the results for only the first 8 hours. All algorithms were tested on data obtained from a 400-bed US hospital. The results show an order-of-magnitude improvement over current approaches in terms of solution quality and computation times.
Advisors/Committee Members: Bard, Jonathan F. (advisor).
Subjects/Keywords: Nurse scheduling; Workforce planning; Nurses; Schedules; Shifts; Hospitals
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APA (6th Edition):
Purnomo, H. W. (2005). Solving midterm and short-term nurse scheduling problems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/30229
Chicago Manual of Style (16th Edition):
Purnomo, Hadi Waskito. “Solving midterm and short-term nurse scheduling problems.” 2005. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/30229.
MLA Handbook (7th Edition):
Purnomo, Hadi Waskito. “Solving midterm and short-term nurse scheduling problems.” 2005. Web. 05 Mar 2021.
Vancouver:
Purnomo HW. Solving midterm and short-term nurse scheduling problems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2005. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/30229.
Council of Science Editors:
Purnomo HW. Solving midterm and short-term nurse scheduling problems. [Doctoral Dissertation]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/30229
4.
Gao, Zhufeng.
Assembly and test operations with multipass requirement in semiconductor manufacturing.
Degree: PhD, Operations Research & Industrial Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/24901
► In semiconductor manufacturing, wafers are grouped into lots and sent to a separate facility for assembly and test (AT) before being shipped to the customer.…
(more)
▼ In semiconductor manufacturing, wafers are grouped into lots and sent to a separate facility for assembly and test (AT) before being shipped to the customer. Up to a dozen operations are required during AT. The facility in which these operations are performed is a reentrant flow shop consisting of several dozen to several hundred machines and up to a thousand specialized tools. Each lot follows a specific route through the facility, perhaps returning to the same machine multiple times. Each step in the route is referred to as a "pass." Lots in work in process (WIP) that have more than a single step remaining in their route are referred to as multi-pass lots. The multi-pass scheduling problem is to determine machine setups, lot assignments and lot sequences to achieve optimal output, as measured by four objectives related to key device shortages, throughput, machine utilization, and makespan, prioritized in this order. The two primary goals of this research are to develop a new formulation for the multipass problem and to design a variety of solution algorithms that can be used for both planning and real-time control. To begin, the basic AT model considering only single-pass scheduling and the previously developed greedy randomized adaptive search procedure (GRASP) along with its extensions are introduced. Then two alternative schemes are proposed to solve the multipass scheduling problem. In the final phase of this research, an efficient procedure is presented for prioritizing machine changeovers in an AT facility on a periodic basis that provides real-time support. In daily planning, target machine-tooling combinations are derived based on work in process, due dates, and backlogs. As machines finish their current lots, they need to be reconfigured to match their targets. The proposed algorithm is designed to run in real time.
Advisors/Committee Members: Bard, Jonathan, F. (advisor).
Subjects/Keywords: Semiconductor manufacturing; Assembly and test; Re-entrant flow; Optimization; Machine setup; Reactive GRASP; Heuristics; Assignment model; Sequencing; Mixed-integer programming; Real-time control
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APA (6th Edition):
Gao, Z. (2014). Assembly and test operations with multipass requirement in semiconductor manufacturing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/24901
Chicago Manual of Style (16th Edition):
Gao, Zhufeng. “Assembly and test operations with multipass requirement in semiconductor manufacturing.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/24901.
MLA Handbook (7th Edition):
Gao, Zhufeng. “Assembly and test operations with multipass requirement in semiconductor manufacturing.” 2014. Web. 05 Mar 2021.
Vancouver:
Gao Z. Assembly and test operations with multipass requirement in semiconductor manufacturing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/24901.
Council of Science Editors:
Gao Z. Assembly and test operations with multipass requirement in semiconductor manufacturing. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/24901
5.
Lin, Sifeng.
Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles.
Degree: MSin Engineering, Operations Research and Industrial Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/28656
► To optimize the beam angle and fluence map in Intensity Modulated Radiation Therapy (IMRT) planning, we apply Benders decomposition as well as develop a two-stage…
(more)
▼ To optimize the beam angle and fluence map in Intensity Modulated Radiation Therapy (IMRT) planning, we apply Benders decomposition as well as develop a two-stage integer programming-based heuristic. Benders decomposition is first implemented in the traditional manner by iteratively solving the restricted master problem, and then identifying and adding the violated Benders cut. We also implemented Benders decomposition using the “lazy constraint” feature included in CPLEX. In contrast, our two-stage heuristic first seeks to find a good solution by iteratively eliminating the least used angles in the linear programming relaxation solution until the size of the formulation is manageable. In the second stage of the heuristic, the solution is improved by applying local branching. The various methods were tested on real patient data in order to investigate their effectiveness and runtime characteristics. The results indicated that implementing Benders using the lazy constraint usually led to better feasible solutions than the traditional approach. Moreover, the LP rounding heuristic was seen to generate high-quality solutions within a short amount of time, with further improvement obtained with the local branching search.
Advisors/Committee Members: Bard, Jonathan F. (advisor).
Subjects/Keywords: Benders decomposition; IMRT; Local branching
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APA ·
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APA (6th Edition):
Lin, S. (2014). Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28656
Chicago Manual of Style (16th Edition):
Lin, Sifeng. “Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles.” 2014. Masters Thesis, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/28656.
MLA Handbook (7th Edition):
Lin, Sifeng. “Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles.” 2014. Web. 05 Mar 2021.
Vancouver:
Lin S. Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/28656.
Council of Science Editors:
Lin S. Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28656
University of Texas – Austin
6.
-2601-7604.
Decomposition and variance reduction techniques for stochastic mixed integer programs.
Degree: PhD, Operations research and industrial engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/1058
► Obtaining upper and lower bounds on the optimal value of a stochastic integer program can require solution of multiple-scenario problems, which are computationally expensive or…
(more)
▼ Obtaining upper and lower bounds on the optimal value of a stochastic integer program can require solution of multiple-scenario problems, which are computationally expensive or intractable using off-the-shelf integer-programming software. Additionally, optimal solutions to a two-stage problem whose second stage spans long time horizons may be optimistic, due to the model's inappropriate ability to plan for future periods which are not known in practice. To that end, we present a framework for optimizing system design in the face of a restricted class of policies governing system operation, which aim to model realistic operation. This leads to a natural decomposition of the problem yielding upper and lower bounds which we can compute quickly. We illustrate these ideas using a model that seeks to design and operate a microgrid to support a forward operating base. Here, designing the microgrid includes specifying the number and type of diesel generators, PV systems, and batteries while operating the grid involves dispatching these assets to satisfy load at minimum cost. We extend our approach to solve the same problem under load and photovoltaic uncertainty, and propose a method to generate appropriately correlated scenarios by simulating building occupancy via a bottom-up approach, then using the occupancy levels to inform environmental control unit loads on the base. Finally, in a separate line of work, we optimize the design of the strata for a stratified sampling estimator to reduce variance. We extend this method to the multivariate setting by optimizing the strata for a nonuniform Latin hypercube estimator. We then present empirical results that show that our method reduces the variance of the estimator, compared to one using equal-probability strata.
Advisors/Committee Members: Hasenbein, John J. (advisor), Morton, David P. (advisor), Bard, Jonathan F (committee member), Hanasusanto, Grani A (committee member), Newman, Alexandra M (committee member).
Subjects/Keywords: Mixed integer programming; Stochastic programming; Microgrid design optimization; Variance reduction; Latin hypercube sampling; Monte Carlo simulation
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APA (6th Edition):
-2601-7604. (2019). Decomposition and variance reduction techniques for stochastic mixed integer programs. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1058
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-2601-7604. “Decomposition and variance reduction techniques for stochastic mixed integer programs.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://dx.doi.org/10.26153/tsw/1058.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-2601-7604. “Decomposition and variance reduction techniques for stochastic mixed integer programs.” 2019. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-2601-7604. Decomposition and variance reduction techniques for stochastic mixed integer programs. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Mar 05].
Available from: http://dx.doi.org/10.26153/tsw/1058.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-2601-7604. Decomposition and variance reduction techniques for stochastic mixed integer programs. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1058
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
7.
-3880-9783.
Models and methods for operational planning in freight railroads.
Degree: PhD, Operations Research and Industrial Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/31604
► Railroads are facing increasing demand for freight transportation. Effective planning and scheduling are crucial to improve the utilization of expensive resources (such as crew and…
(more)
▼ Railroads are facing increasing demand for freight transportation. Effective planning and scheduling are crucial to improve the utilization of expensive resources (such as crew and track), reduce operational costs, and provide on-time service. This dissertation focuses on problem modeling and solution method development for real planning problems faced by railroads. It consists of three chapters that study two important planning problems in the daily operations of U.S. freight railroads: crew assignment and train movement planning. Chapter 2 proposes an optimization model to decide crew-to-train assignments and deadheads for double-ended crew districts. We develop an effective solution approach, combining optimization and a standalone heuristic, that generates optimal solutions in minutes. The excellent performance of this solution approach makes it well-suited for implementation within a real-time decision support tool for crew dispatchers. Chapter 3 discusses crew repositioning given the uncertainty in trains’ arrival and departure times. We propose models that minimize the expected crew holding, train delay, and deadheading cost, and develop both exact and heuristic solution methods to provide insights for crew planning under train schedule uncertainty. The last chapter studies the movement planning problem for trains traveling in a territory with multiple through tracks (mainlines) and various junctions. We explore a number of heuristic algorithms to obtain good solutions within a reasonable amount of time. The contributions of this dissertation include modeling enhancements, algorithmic development, implementation and computational testing, and validation using real data.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Balakrishnan, Anantaram (advisor), Dimitrov, Ned (committee member), Hasenbein, John J (committee member), Morton, David P (committee member).
Subjects/Keywords: Optimization; Railroad operational planning
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APA (6th Edition):
-3880-9783. (2015). Models and methods for operational planning in freight railroads. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31604
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-3880-9783. “Models and methods for operational planning in freight railroads.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/31604.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-3880-9783. “Models and methods for operational planning in freight railroads.” 2015. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-3880-9783. Models and methods for operational planning in freight railroads. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/31604.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-3880-9783. Models and methods for operational planning in freight railroads. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31604
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
8.
-9907-6069.
Simulation and optimization techniques applied in semiconductor assembly and test operations.
Degree: PhD, Operations research and industrial engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/40318
► The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry.…
(more)
▼ The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry. In order to achieve low cycle times, high throughput, and high utilization while improving due-date performance, more effective tools are needed to support machine setup and lot dispatching decisions. In previous work, the problem of maximizing the weighted throughput of lots undergoing assembly and test (AT), while ensuring that critical lots are given priority, was investigated and a greedy randomized adaptive search procedure (GRASP) developed to find solutions. Optimization techniques have long been used for scheduling manufacturing operations on a daily basis. Solutions provide a prescription for machine setups and job processing over a finite the planning horizon. In contrast, simulation provides more detail but in a normative sense. It tells you how the system will evolve in real time for a given demand, a given set of resources and rules for using them. A simulation model can also accommodate changeovers, initial setups and multi-pass requirements easily. The first part of the research is to show how the results of an optimization model can be integrated with the decisions made within a simulation model. The problem addressed is defined in terms of four hierarchical objectives: minimize the weighted sum of key device shortages, maximize weighted throughput, minimize the number of machines used, and minimize makespan for a given set of lots in queue, and a set of resources that includes machines and tooling. The facility can be viewed as a reentrant flow shop. The basic simulation was written in AutoSched AP (ASAP) and then enhanced with the help of customization features available in the software. Several new dispatch rules were developed. Rule_First_setup is able to initialize the simulation with the setups obtained with the GRASP. Rule_All_setups enables a machine to select the setup provided by the optimization solution whenever a decision is about to be made on which setup to choose subsequent to the initial setup. Rule_Hotlot was also proposed to prioritize the processing of the hot lots that contain key devices. The objective of the second part of the research is to design and implement heuristics within the simulation model to schedule back-end operations in a semiconductor AT facility. Rule_Setupnum lets the machines determine which key device to process according to a machine setup frequency table constructed from the GRASP solution. GRASP_asap embeds a more robust selection features of GRASP in the ASAP model through customization. This allows ASAP to explore a larger portion of the feasible region at each decision point by randomizing machine setups using adaptive probability distributions that are a function of solution quality. Rule_Greedy, which is a simplification of GRASP_asap, always picks the setup for a particular machine that gives the greatest marginal improvement in the objective function among all candidates.…
Advisors/Committee Members: Bard, Jonathan F. (advisor), Morrice, Douglas J (committee member), Hasenbein, John (committee member), Khajavirad, Aida (committee member), Gao, Zhufeng (committee member).
Subjects/Keywords: Semiconductor assembly and test; AutoSched; GRASP; Dispatch rules; Statistical analysis; Machine setup; Reentrant flow
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APA ·
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APA (6th Edition):
-9907-6069. (2016). Simulation and optimization techniques applied in semiconductor assembly and test operations. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40318
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9907-6069. “Simulation and optimization techniques applied in semiconductor assembly and test operations.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/40318.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9907-6069. “Simulation and optimization techniques applied in semiconductor assembly and test operations.” 2016. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9907-6069. Simulation and optimization techniques applied in semiconductor assembly and test operations. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/40318.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9907-6069. Simulation and optimization techniques applied in semiconductor assembly and test operations. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40318
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
9.
-7689-3691.
Optimization models and methods for transportation services.
Degree: PhD, Operations Research & Industrial Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/46983
► Managing transportation services efficiently is essential to both public and private sectors. This dissertation addresses three scheduling problems in modern transportation systems: the network design…
(more)
▼ Managing transportation services efficiently is essential to both public and private sectors. This dissertation addresses three scheduling problems in modern transportation systems: the network design problem, the train dispatching problem, and the service route design problem. The transportation network design problem with service requirements designs arcs on a directed network and route commodities on the designed arcs so that i) commodities satisfy service requirements and ii) the total cost is minimized. We develop three mathematical programming models: a compact but weak arc-flow formulation, a large but strong path-flow formulation, and a hybrid formulation that uses both the arc-flow and the path-flow representations. We show that the hybrid formulation can significantly strengthen the LP formulation without introducing many variables. To find a good hybrid formulation, we develop columnization and decolumnization algorithms that uses the LP relaxation information to identify commodities that should use the path-flow representation. We also develop valid inequalities for commodities using the path-flow representation. The train dispatching problem schedules the movements of trains on scarce railroad tracks so as to improve the average velocity of trains. We develop a mathematical programming model and strengthen the model using valid inequalities. Besides, we present a heuristic to find a feasible solution quickly, which can serve as the warm-start solution to the MIP solver. For the third problem, we seek to design vehicle routes to deliver and pickup orders for a major grocery chain. We design a GRASP that can incorporate various operational requirements, including warehouse loading capacity, loading sequence, time window requirements, truck volume and weight capacities, and driver time limits. Our GRASP procedure consists of two phases: the solution construction (Phase I) and the Tabu search (Phase II). We show that the neighborhood structure of solutions is highly degenerate, which limits the solution space explored by the Tabu search. We apply the Tabu search with random variable neighborhood to increase the solution space explored.
Advisors/Committee Members: Balakrishnan, Anantaram (advisor), Bard, Jonathan F. (advisor), Mirchandani, Prakash (committee member), Hasenbein , John (committee member), Dimitrov, Nedialko (committee member).
Subjects/Keywords: Network design; Optimization; Integer programming; Train dispatching; Vehicle routing
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APA (6th Edition):
-7689-3691. (2015). Optimization models and methods for transportation services. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46983
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-7689-3691. “Optimization models and methods for transportation services.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/46983.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-7689-3691. “Optimization models and methods for transportation services.” 2015. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-7689-3691. Optimization models and methods for transportation services. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/46983.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-7689-3691. Optimization models and methods for transportation services. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/46983
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
10.
-1860-9725.
Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic.
Degree: PhD, Operations Research and Industrial Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/31603
► Clinical experiences during the three years of residencies occur in inpatient and outpatient settings on generalist and specialist clinical services. Housestaff rotate through different clinical…
(more)
▼ Clinical experiences during the three years of residencies occur in inpatient and outpatient settings on generalist and specialist clinical services. Housestaff rotate through different clinical experiences monthly, with their primary care clinic time overlaid longitudinally on these other clinical services. The primary goals of this research are to construct housestaff schedules and improve efficiencies for residency programs. In the first phase of the research, we developed two models for constructing monthly clinic schedules for housestaff training in Internal Medicine. In our first model, the objective is to both maximize clinic utilization and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program in which several of the hard constraints are temporarily allowed to be violated to avoid infeasibility. A three-phase methodology is then proposed to find solutions. The second model solves a similar problem with the objective of maximizing the number of interns and residents that are assigned clinic duty each month during their training in Internal Medicine. A complexity analysis is provided that demonstrates that the basic problem can be modeled as a pure network and the full problem can be modeled as a network with gains. In the second phase of the research, the goal was to redesign the monthly templates that comprise the annual block rotations to obtain better housestaff schedules. To implement this model, we investigate two different programs: Family Medicine and Internal Medicine. The problems were formulated as mixed-integer programs but proved too difficult to solve exactly. As an alternative, several heuristics were developed that yielded good feasible solutions. For the last part of the research, we focused on improving patient flow at a family health clinic. The objective was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Morrice, Douglas J. (Douglas John), 1962- (advisor), Khajavirad, Aida (committee member), Dimitrov, Ned (committee member), Leykum, Luci (committee member).
Subjects/Keywords: Resident scheduling; Residency outpatient clinic; Medical rotations; Goal programming; Mixed-integer programming; Patient flow; Simulation; Performance analysis
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APA (6th Edition):
-1860-9725. (2015). Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31603
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-1860-9725. “Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/31603.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-1860-9725. “Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic.” 2015. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-1860-9725. Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/31603.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-1860-9725. Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31603
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
11.
-6943-657X.
Optimal spatiotemporal resource allocation in public health and renewable energy.
Degree: PhD, Operations Research & Industrial Engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/44589
► 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)
▼ 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 challenge is complicated by mismatches between supply and demand over time and also by uncertainty in demand and/or supply. We study such problems in public health and in renewable energy. Public health resources, such as antiviral medications and vaccines, are often in limited availability at the start of an influenza pandemic. Government agencies need to make balanced policy decisions, accounting for regional equity while maintaining an efficient distribution to mitigate spread of the influenza virus. In the absence of good initial information regarding the demand, resource allocation decisions need to encompass a variety of demand scenarios. On the renewable energy side, we seek to provide a fixed supply of energy from a system which includes a highly variable renewable source, such as wind power. Here, we must commit to a decision before the stochastic supply is realized. When bidding into the electricity market to buy or sell energy, an added difficulty concerning the prices of energy arises. We study five specific problems in these contexts.
Advisors/Committee Members: Dimitrov, Nedialko B. (advisor), Morton, David P. (advisor), Meyers, Lauren A (committee member), Santoso, Surya (committee member), Hasenbein, John J (committee member), Bard, Jonathan F (committee member).
Subjects/Keywords: Stochastic optimization; Chance-constrained programming; Pumped-hydroelectric system; Public health
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APA (6th 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 (16th Edition):
-6943-657X. “Optimal spatiotemporal resource allocation in public health and renewable energy.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 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 (7th Edition):
-6943-657X. “Optimal spatiotemporal resource allocation in public health and renewable energy.” 2016. Web. 05 Mar 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 Mar 05].
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
12.
Wang, Huan, master of science in engineering.
The therapist scheduling problem for patients with fixed appointment times.
Degree: MSin Engineering, Operations Research & Industrial Engineering, 2011, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2011-12-4512
► This report presents a series of models that can be used to find weekly schedules for therapists who provide ongoing treatment to patients scattered around…
(more)
▼ This report presents a series of models that can be used to find weekly schedules for therapists who provide ongoing treatment to patients scattered around a geographical region. In all cases, the patients’ appointment times and visit days are known prior to the beginning of the planning horizon. Variations in the model include single vs. multiple home bases, homogeneous vs. heterogeneous therapists, lunch break requirements, and a nonlinear cost structure for mileage reimbursement and overtime. The single home base and homogeneous therapist cases proved to be easy to solve and so were not investigated. This left two cases of interest: the first includes only lunch breaks while the second adds overtime and mileage reimbursement. In all, 40 randomly generated data sets were solved that consisted of either 15 or 20 therapists and between roughly 300 and 540 visits over five days. For each instance, we were able to obtain the minimum cost of providing home healthcare services for both models using CPLEX 12.2. The results showed that CPU time increases more rapidly than total cost as the total number of visits grows. In general, data sets with therapists who have different starting and ending locations are more difficult to solve than those whose therapists have the same home base.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Jarrah, Ahmad I. (committee member).
Subjects/Keywords: Home healthcare; Patient scheduling; Mixed-integer programming; Multiple depots; Heterogeneous servers
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APA (6th Edition):
Wang, Huan, m. o. s. i. e. (2011). The therapist scheduling problem for patients with fixed appointment times. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4512
Chicago Manual of Style (16th Edition):
Wang, Huan, master of science in engineering. “The therapist scheduling problem for patients with fixed appointment times.” 2011. Masters Thesis, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/ETD-UT-2011-12-4512.
MLA Handbook (7th Edition):
Wang, Huan, master of science in engineering. “The therapist scheduling problem for patients with fixed appointment times.” 2011. Web. 05 Mar 2021.
Vancouver:
Wang, Huan mosie. The therapist scheduling problem for patients with fixed appointment times. [Internet] [Masters thesis]. University of Texas – Austin; 2011. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4512.
Council of Science Editors:
Wang, Huan mosie. The therapist scheduling problem for patients with fixed appointment times. [Masters Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4512
13.
Yang, Yutian, active 21st century.
Portfolio optimization using stochastic programming with market trend forecast.
Degree: MSin Engineering, Operations Research & Industrial Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/26248
► This report discusses a multi-stage stochastic programming model that maximizes expected ending time profit assuming investors can forecast a bull or bear market trend. If…
(more)
▼ This report discusses a multi-stage stochastic programming model that maximizes expected ending time profit assuming investors can forecast a bull or bear market trend. If an investor can always predict the market trend correctly and pick the optimal stochastic strategy that matches the real market trend, intuitively his return will beat the market performance. For investors with different levels of prediction accuracy, our analytical results support their decision of selecting the highest return strategy. Real stock prices of 154 stocks on 73 trading days are collected. The computational results verify that accurate prediction helps to exceed market return while portfolio profit drops if investors partially predict or forecast incorrectly part of the time. A sensitivity analysis shows how risk control requirements affect the investor's decision on selecting stochastic strategies under the same prediction accuracy.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Lasdon, Leon S., 1939- (advisor).
Subjects/Keywords: Portfolio optimization; Stochastic programming; Market trend prediction
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APA (6th Edition):
Yang, Yutian, a. 2. c. (2014). Portfolio optimization using stochastic programming with market trend forecast. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26248
Chicago Manual of Style (16th Edition):
Yang, Yutian, active 21st century. “Portfolio optimization using stochastic programming with market trend forecast.” 2014. Masters Thesis, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/26248.
MLA Handbook (7th Edition):
Yang, Yutian, active 21st century. “Portfolio optimization using stochastic programming with market trend forecast.” 2014. Web. 05 Mar 2021.
Vancouver:
Yang, Yutian a2c. Portfolio optimization using stochastic programming with market trend forecast. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/26248.
Council of Science Editors:
Yang, Yutian a2c. Portfolio optimization using stochastic programming with market trend forecast. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/26248
University of Texas – Austin
14.
Nananukul, Narameth, 1970-.
Lot-sizing and inventory routing for a production-distribution supply chain.
Degree: PhD, Operations Research and Industrial Engineering, 2008, University of Texas – Austin
URL: http://hdl.handle.net/2152/3960
► The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate…
(more)
▼ The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate goal is to coordinate production, inventory, and delivery to meet customer demand so that the corresponding costs are minimized. Achieving this goal provides the foundations for streamlining the logistics network and for integrating other operational and financial components of the system. In this paper, a model is presented that includes a single production facility, a set of customers with time varying demand, a finite planning horizon, and a fleet of vehicles for making the deliveries. Demand can be satisfied from either inventory held at the customer sites or from daily product distribution. A procedure centering on a reactive tabu search is developed for solving the full problem. After a solution is found, path relinking is applied to improve the results. A novel feature of the methodology is the use of an allocation model in the form of a mixed integer program to find good feasible solutions that serve as starting points for the tabu search. Lower bounds on the optimum are obtained by solving a modified version of the allocation model. Computational testing on a set of 90 benchmark instances with up to 200 customers and 20 time periods demonstrates the effectiveness of the approach. In all cases, improvements ranging from 10 - 20% were realized when compared to those obtained from an existing greedy randomized adaptive search procedure (GRASP). This often came at a three- to five-fold increase in runtime, however. A hybrid scheme that combines the features of reactive tabu search algorithm and branch-and-price algorithm is also developed. The combined approach takes advantage of the efficiency of the tabu search heuristic and the precision of the branch-and-price algorithm. Branching strategy that is suitable for the problem is proposed. Several advance techniques such as column generation heuristic and rounding heuristic are also implemented to improve the efficiency of the algorithm. Computational testing on standard data sets shows that a hybrid algorithm can practically solve instances with up to 50 customers and 8 time periods which is not possible by standard branch-and-price algorithm alone.
Advisors/Committee Members: Bard, Jonathan F. (advisor).
Subjects/Keywords: Business logistics – Mathematical models; Economic lot size; Production control – Mathematical models
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APA (6th Edition):
Nananukul, Narameth, 1. (2008). Lot-sizing and inventory routing for a production-distribution supply chain. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3960
Chicago Manual of Style (16th Edition):
Nananukul, Narameth, 1970-. “Lot-sizing and inventory routing for a production-distribution supply chain.” 2008. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/3960.
MLA Handbook (7th Edition):
Nananukul, Narameth, 1970-. “Lot-sizing and inventory routing for a production-distribution supply chain.” 2008. Web. 05 Mar 2021.
Vancouver:
Nananukul, Narameth 1. Lot-sizing and inventory routing for a production-distribution supply chain. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2008. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/3960.
Council of Science Editors:
Nananukul, Narameth 1. Lot-sizing and inventory routing for a production-distribution supply chain. [Doctoral Dissertation]. University of Texas – Austin; 2008. Available from: http://hdl.handle.net/2152/3960
University of Texas – Austin
15.
Rojanasoonthon, Siwate.
Parallel machine scheduling with time windows.
Degree: PhD, Operations Research and Industrial Engineering, 2004, University of Texas – Austin
URL: http://hdl.handle.net/2152/2175
Subjects/Keywords: Parallel processing (Electronic computers); Scheduling – Mathematical models; Heuristic programming
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APA (6th Edition):
Rojanasoonthon, S. (2004). Parallel machine scheduling with time windows. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2175
Chicago Manual of Style (16th Edition):
Rojanasoonthon, Siwate. “Parallel machine scheduling with time windows.” 2004. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/2175.
MLA Handbook (7th Edition):
Rojanasoonthon, Siwate. “Parallel machine scheduling with time windows.” 2004. Web. 05 Mar 2021.
Vancouver:
Rojanasoonthon S. Parallel machine scheduling with time windows. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2004. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/2175.
Council of Science Editors:
Rojanasoonthon S. Parallel machine scheduling with time windows. [Doctoral Dissertation]. University of Texas – Austin; 2004. Available from: http://hdl.handle.net/2152/2175
University of Texas – Austin
16.
Wan, Lin.
Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers.
Degree: PhD, Mechanical Engineering., 2005, University of Texas – Austin
URL: http://hdl.handle.net/2152/1756
Subjects/Keywords: Postal service – United States – Personnel management; Manpower planning – United States – Mathematical models; Production scheduling – Mathematical models; United States Postal Service – Management
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APA ·
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APA (6th Edition):
Wan, L. (2005). Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1756
Chicago Manual of Style (16th Edition):
Wan, Lin. “Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers.” 2005. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/1756.
MLA Handbook (7th Edition):
Wan, Lin. “Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers.” 2005. Web. 05 Mar 2021.
Vancouver:
Wan L. Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2005. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/1756.
Council of Science Editors:
Wan L. Staff planning and scheduling in the service industry: an application to US Postal Service mail processing and distribution centers. [Doctoral Dissertation]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/1756
17.
-9365-5003.
Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare.
Degree: PhD, Operations Research and Industrial Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/2085
► This dissertation proposes multiple methods to improve processes and make better decisions in manufacturing and healthcare. First, it investigates algorithms for controlling the automated material…
(more)
▼ This dissertation proposes multiple methods to improve processes and make better decisions in manufacturing and healthcare. First, it investigates algorithms for controlling the automated material handling system (AMHS) in a wafer fab. In particular, this research examines algorithms that route vehicles for both the pickup and delivery of lots, with the goal of improving vehicle flow, cycle time, and avoiding congested segments in the AMHS. The proposed methods are simulated using both a stylized simulation model and a more detailed Automod model. These simulations demonstrate that algorithms designed specifically to anticipate congestion can significantly improve some fab metrics. Secondly, this research develops several algorithms for ranking tools in a manufacturing facility so that routes can be categorized and the best routes can be used for recipe probing. Ranking is performed using three different metrics: score-based metrics where higher implies better, target-based metrics where a balance has to be struck by the decision maker between accuracy and precision of a tool based on a target value, and count based metrics such as defect data where a lower number is better (e.g., zero defects is the best scenario). In this part of the dissertation, the ranking algorithms designed for count based metrics are the main contribution to the tool-ranking literature for the manufacturing industry. Finally, the dissertation addresses the problem of medical decision making under uncertainty during the treatment of epilepsy. Here the sequential decision making problem is modeled as an average cost Markov decision process (MDP) to maximize a patient's remaining quality of life. A crucial issue is the uncertainty in transition probabilities extracted from medical studies in epilepsy due to attrition of patients from studies, lack of data and lack of proper experimental design owing to the complexity in treatment procedure. This is addressed by formulating a robust MDP that suggests the best course of treatment for a patient.
Advisors/Committee Members: Hasenbein, John J. (advisor), Clarke, Dave F (committee member), Kutanoglu, Erhan (committee member), Bard, Jonathan F (committee member).
Subjects/Keywords: Semiconductor; Healthcare; Markov decision processes; Average cost Markov decision process; Statistics; Simulation; Count regression; Ranking; Routing; Robust optimization; Robust Markov decision processes; Robust average cost Markov decision process; Medical decision making; Epilepsy
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APA (6th Edition):
-9365-5003. (2019). Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2085
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9365-5003. “Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://dx.doi.org/10.26153/tsw/2085.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9365-5003. “Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare.” 2019. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9365-5003. Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Mar 05].
Available from: http://dx.doi.org/10.26153/tsw/2085.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9365-5003. Dynamic decision making under uncertainty for semiconductor manufacturing and healthcare. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2085
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
18.
Yang, Yutian.
Solving transportation scheduling problems : models and algorithms.
Degree: PhD, Operations research and industrial engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/39651
► Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved…
(more)
▼ Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved by developing better models and algorithms for transportation planning and scheduling. The main challenges include the mathematical modeling of operational rules, uncertainties in operations, and large-scale problem size. This dissertation addresses crew scheduling in freight railways and vehicle routing problems (VRP) for mail processing and distribution centers (P&DCs). Our goal is to develop models and algorithms that improve efficiency and reduce operating costs. In Chapter 2, we propose an optimization model to support real-time freight railway crew assignment decisions. Due to workload balance requirements and operating regulations, the optimization model is difficult to solve for realistic instances. Hence, we propose model improvements and develop effective solution techniques to find optimal or near-optimal solutions very quickly. Chapter 3 extends the freight rail crew scheduling problem by incorporating uncertainty in train arrival and departure times. We propose a stochastic programming model, but this model is solvable only the number of scenarios is small. As a consequence, we develop heuristics that use an analytical model to calculate the expected total cost of a given choice of crew deadheads. Using this cost evaluator, we develop four local search based heuristic algorithms to sequentially improve crew scheduling decisions under uncertainty. In Chapter 4, we first cluster the pickup and drop off points in mail P&DCs into zones and then minimize the number of vehicles required and the total distance traveled to meet daily transport demand. The clustering is performed with a greedy randomized adaptive search procedure, and two heuristics are developed to find solutions to the VRP, which proved intractable for realistic instances. The heuristics are optimization-based within a rolling horizon framework. An extensive analysis is undertaken to evaluate the relative performance of the two heuristics. The contributions of this dissertation include modeling, algorithmic development, computational testing, and validation using real and randomly generated data.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Balakrishnan, Anant (advisor), Dimitrov, Ned (committee member), Hasenbein, John J. (committee member), Lasdon, Leon (committee member).
Subjects/Keywords: Transportation; Scheduling
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APA (6th Edition):
Yang, Y. (2016). Solving transportation scheduling problems : models and algorithms. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39651
Chicago Manual of Style (16th Edition):
Yang, Yutian. “Solving transportation scheduling problems : models and algorithms.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/39651.
MLA Handbook (7th Edition):
Yang, Yutian. “Solving transportation scheduling problems : models and algorithms.” 2016. Web. 05 Mar 2021.
Vancouver:
Yang Y. Solving transportation scheduling problems : models and algorithms. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/39651.
Council of Science Editors:
Yang Y. Solving transportation scheduling problems : models and algorithms. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39651
19.
Shao, Yufen.
Home therapist network modeling.
Degree: PhD, Operations Research and Industrial Engineering, 2011, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2011-12-4500
► Home healthcare has been a growing sector of the economy over the last three decades with roughly 23,000 companies now doing business in the U.S.…
(more)
▼ Home healthcare has been a growing sector of the economy over the last three decades with roughly 23,000 companies now doing business in the U.S. producing over $56 billion in combined annual revenue. As a highly fragmented market, profitability of individual companies depends on effective management and efficient operations. This dissertation aims at reducing costs and improving productivity for home healthcare companies.
The first part of the research involves the development of a new formulation for the therapist routing and scheduling problem as a mixed integer program. Given the time horizon, a set of therapists and a group of geographically dispersed patients, the objective of the model is to minimize the total cost of providing service by assigning patients to therapists while satisfying a host of constraints concerning time windows, labor regulations and contractual agreements. This problem is NP-hard and proved to be beyond the capability of commercial solvers like CPLEX. To obtain good solutions quickly, three approaches have been developed that include two heuristics and a decomposition algorithm.
The first approach is a parallel GRASP that assigns patients to multiple routes in a series of rounds. During the first round, the procedure optimizes the patient distribution among the available therapists, thus trying to reach a local optimum with respect to the combined cost of the routes. Computational results show that the parallel GRASP can reduce costs by 14.54% on average for real datasets, and works efficiently on randomly generated datasets.
The second approach is a sequential GRASP that constructs one route at a time. When building a route, the procedure tracks the amount of time used by the therapists each day, giving it tight control over the treatment time distribution within a route. Computational results show that the sequential GRASP provides a cost savings of 18.09% on average for the same real datasets, but gets much better solutions with significantly less CPU for the same randomly generated datasets.
The third approach is a branch and price algorithm, which is designed to find exact optima within an acceptable amount of time. By decomposing the full problem by therapist, we obtain a series of constrained shortest path problems, which, by comparison are relatively easy to solve. Computational results show that, this approach is not efficient here because: 1) convergence of Dantzig-Wolfe decomposition is not fast enough; and 2) subproblem is strongly NP-hard and cannot be solved efficiently.
The last part of this research studies a simpler case in which all patients have fixed appointment times. The model takes the form of a large-scale mixed-integer program, and has different computational complexity when different features are considered. With the piece-wise linear cost structure, the problem is strongly NP-hard and not solvable with CPLEX for instances of realistic size. Subsequently, a rolling horizon algorithm, two relaxed mixed-integer models and a…
Advisors/Committee Members: Bard, Jonathan F. (advisor), Jarrah, Ahmad I. (committee member), Lasdon, Leon (committee member), Morton, David P. (committee member), Kutanoglu, Erhan (committee member).
Subjects/Keywords: Therapist scheduling; Home healthcare; Weekly planning; Network modeling; Mixed integer programming; Exact algorithm; Column generation; Heuristic algorithm; GRASP
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APA (6th Edition):
Shao, Y. (2011). Home therapist network modeling. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4500
Chicago Manual of Style (16th Edition):
Shao, Yufen. “Home therapist network modeling.” 2011. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/ETD-UT-2011-12-4500.
MLA Handbook (7th Edition):
Shao, Yufen. “Home therapist network modeling.” 2011. Web. 05 Mar 2021.
Vancouver:
Shao Y. Home therapist network modeling. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2011. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4500.
Council of Science Editors:
Shao Y. Home therapist network modeling. [Doctoral Dissertation]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4500
20.
Queiroz, Anderson Rodrigo de.
A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality.
Degree: PhD, Operations Research, 2011, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2011-12-4690
► We consider a hydrothermal scheduling problem with a mid-term horizon(HTSPM) modeled as a large-scale multistage stochastic program with stochastic monthly inflows of water to each…
(more)
▼ We consider a hydrothermal scheduling problem with a mid-term horizon(HTSPM) modeled as a large-scale multistage stochastic program with stochastic monthly inflows of water to each hydro generator. In the HTSPM we seek an operating policy to minimize the sum of present and expected future
costs, which include thermal generation costs and load curtailment costs. In addition to various simple bounds, problem constraints involve water balance,
demand satisfaction and power interchanges.
Sampling-based decomposition algorithms (SBDAs) have been used in the literature to solve HTSPM. SBDAs can be used to approximately solve problem instances with many time stages and with inflows that exhibit interstage dependence. Such dependence requires care in computing valid cuts for the decomposition algorithm.
In order to help maintain tractability, we employ an aggregate reservoir representation (ARR). In an ARR all the hydro generators inside a specific
region are grouped to effectively form one hydro plant with reservoir storage and generation capacity proportional to the parameters of the hydro plants
used to form that aggregate reservoir. The ARR has been used in the literature with energy balance constraints, rather than water balance constraints, coupled with time series forecasts of energy inflows. Instead, we prefer as a model primitive to have the time series model forecast water inflows. This, in turn, requires that we extend existing methods to compute valid cuts for the decomposition method under the resulting form of interstage dependence.
We form a sample average approximation of the original problem and then solve this problem by these special-purpose algorithms. And, we assess the quality of the resulting policy for operating the system. In our analysis, we compute a confidence interval on the optimality gap of a policy generated by solving an approximation on a sampled scenario tree. We present computational results on test problems with 24 monthly stages in which the inter-stage dependency of hydro inflows is modeled using a dynamic linear model. We further
develop a parallel implementation of an SBDA. We apply SBDA to solve
the HTSPM for the Brazilian power system that has 150 hydro generators, 151 thermal generators and 4 regions that each characterize an aggregate reservoir.
We create and solve four different HTSPM instances where we change the input parameters with respect to generation capacity, transmission capacity and load in order to analyze the difference in the total expected cost.
Advisors/Committee Members: Morton, David P. (advisor), Popova, Elmira (committee member), Kutanoglu, Erhan (committee member), Bard, Jonathan F. (committee member), Baldick, Ross (committee member).
Subjects/Keywords: Decomposition algorithms; Stochastic optimization; Multistage mathematical Programs; Hydro-thermal scheduling; Solution quality assessment
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APA (6th Edition):
Queiroz, A. R. d. (2011). A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4690
Chicago Manual of Style (16th Edition):
Queiroz, Anderson Rodrigo de. “A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality.” 2011. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/ETD-UT-2011-12-4690.
MLA Handbook (7th Edition):
Queiroz, Anderson Rodrigo de. “A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality.” 2011. Web. 05 Mar 2021.
Vancouver:
Queiroz ARd. A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2011. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4690.
Council of Science Editors:
Queiroz ARd. A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality. [Doctoral Dissertation]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4690
21.
-1113-1730.
Optimization models for manufacturing and personnel scheduling.
Degree: PhD, Operations Research and Industrial Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/61924
► Personnel scheduling problems have been studied by many researchers over the last five decades but much of the literature has ignored the array of break…
(more)
▼ Personnel scheduling problems have been studied by many researchers over the last five decades but much of the literature has ignored the array of break types used in practice. We investigate the benefits that flexibility offers in daily shift scheduling, especially when demand is uncertain. The different forms of flexibility considered include shift start times, the number of breaks, break lengths, and break placement. Five related mixed-integer programming models are developed and used to compare break scheduling in advance and either sequentially or in real time for various shift and break profiles. In addition, we investigate the same problem under stochastic demand. We formulate a multi-stage stochastic programming model and then transform it into a two-stage model to ease the computational burden. For testing purpose, we consider 61 scenarios. Five metrics are used for evaluating performance.
While the full range of shift and break options are rarely considered in personnel scheduling problems, many practical aspects of machine setups have been neglected in scheduling semiconductor assembly and test (AT) operations. We examine all sides of the problem in a multi-machine, multi-tooling environment to see the impact of using a hierarchical approach to setups on facility performance. The primary objectives of the problem investigated are to minimize the number of shortages of key devices and to maximize weighted throughput, in that order, over a planning horizon of up to five days. Secondary objectives include minimizing the number of machines used to meet output targets, and minimizing makespan.
For the shift scheduling problems with flexible breaks the application studied involves airport ground handlers; for the hierarchical machine setup problem for semiconductor assembly and test facilities testing was done with data provided by
Texas Instruments. In Chapter 2, we investigate the benefits of flexibility for shifts and breaks with both deterministic and randomized demand. A rolling horizon approach is proposed for real-time break scheduling as demand unfolds over the day. In Chapter 3, we extend the shift scheduling problem to more realistically accommodate stochasticity. We introduce a two-stage stochastic programming model and determine the value of stochastic solutions and the expected value of perfect information. In Chapter 4, we develop an optimization model for scheduling multi-pass lots under hierarchical machine setup rules at assembly and test facilities. We determine machine setups, lot assignments and sequences using a greedy randomized adaptive search procedure.
Advisors/Committee Members: Bard, Jonathan F. (advisor), Hasenbein, John J (committee member), Kutanoglu, Erhan (committee member), Kiermaier, Ferdinand (committee member), Frey, Markus (committee member).
Subjects/Keywords: Shift scheduling; Flexible breaks; Rolling horizon framework; Real-time break assignments; Stochastic optimization; Baggage handlers; Implicit modeling of breaks; Semiconductor assembly and test facility; Back-end operations; Hierarchical setups; Reentrant flow; GRASP
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APA (6th Edition):
-1113-1730. (2017). Optimization models for manufacturing and personnel scheduling. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61924
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-1113-1730. “Optimization models for manufacturing and personnel scheduling.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/61924.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-1113-1730. “Optimization models for manufacturing and personnel scheduling.” 2017. Web. 05 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-1113-1730. Optimization models for manufacturing and personnel scheduling. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/61924.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-1113-1730. Optimization models for manufacturing and personnel scheduling. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61924
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
22.
Qu, Yuan, Ph. D.
Pickup and delivery problems with side constraints.
Degree: PhD, Operations Research and Industrial Engineering, 2012, University of Texas – Austin
URL: http://hdl.handle.net/2152/19531
► Pickup and delivery problems (PDPs) have been studied extensively in past decades. A wide variety of research exits on both exact algorithms and heuristics for…
(more)
▼ Pickup and delivery problems (PDPs) have been studied extensively in past decades. A wide variety of research exits on both exact algorithms and heuristics for generic variations of the problem as well as real-life applications, which continue to spark new challenges and open up new opportunities for researchers. In this dissertation, we study two variations of pickup and delivery problem that arise in industry and develop new computational methods that are shown to be effective with respect to existing algorithms and scheduling procedures found in practice.
The first problem is the pickup and delivery problem with transshipment (PDPT). The work presented here was inspired by a daily route planning problem at a regional air carrier. In structuring the analysis, we describe a unique way to model the transshipment option on a directed graph. With the graph as the foundation, we implemented a branch and price algorithm. Preliminary results showed that it has difficulty in solving large instances. As an alternative, we developed a greedy randomized adaptive search procedure (GRASP) with several novel features. In the construction phase, shipment requests are inserted into routes until all demand is satisfied or no feasible insertion exists. In the improvement phase, an adaptive large neighborhood search algorithm is used to reconstruct portions of the feasible routes. Specialized removal and insertion heuristics were designed for this purpose. We also developed a procedure for generating problem instances in the absence of any in the literature. Testing was done on existing PDP data sets and generated PDPT data set. For the former, the performance and solution quality of the GRASP were comparable to the best known heuristics. For the latter, GRASP found the near optimal solution in most test cases.
In the second part of the dissertation, we focus on a new version of the heterogeneous PDP in which the capacity of each vehicle can be modified by reconfiguring its interior to satisfy different types of customer demands. The work was motivated by a daily route planning problem arising at a senior activity center. A fleet of configurable vans is available each day to transport participants to and from the center as well as to secondary facilities for rehabilitative and medical treatment. To find solutions, we developed a two-phase heuristic that makes use of ideas from greedy randomized adaptive search procedures with multiple starts. In phase I, a set of good feasible solutions is constructed using a series of randomized procedures. A representative subset of those solutions is selected as candidates for improvement by solving a max diversity problem. In phase II, an adaptive large neighborhood search (ALNS) heuristic is used to find local optima by reconstructing portions of the feasible routes. Also, a specialized route feasibility check with vehicle type reassignment is introduced to take full advantage of the heterogeneous nature of vehicles. The effectiveness of the proposed methodology is demonstrated by…
Advisors/Committee Members: Bard, Jonathan F. (advisor), Lasdon, Leon (committee member), Morton, David P (committee member), Kutanoglu, Erhan (committee member), Pachon, Julian (committee member).
Subjects/Keywords: Pickup and delivery; Transshipment; GRASP; ALNS; Branch and price; Column generation; Configurable vehicle capacity; MSALNS; Branch and price and cut
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APA (6th Edition):
Qu, Yuan, P. D. (2012). Pickup and delivery problems with side constraints. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19531
Chicago Manual of Style (16th Edition):
Qu, Yuan, Ph D. “Pickup and delivery problems with side constraints.” 2012. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/19531.
MLA Handbook (7th Edition):
Qu, Yuan, Ph D. “Pickup and delivery problems with side constraints.” 2012. Web. 05 Mar 2021.
Vancouver:
Qu, Yuan PD. Pickup and delivery problems with side constraints. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2012. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/19531.
Council of Science Editors:
Qu, Yuan PD. Pickup and delivery problems with side constraints. [Doctoral Dissertation]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19531
University of Texas – Austin
23.
Zhu, Guidong.
Disruption managment for project scheduling problem.
Degree: PhD, Management Science and Information Systems, 2005, University of Texas – Austin
URL: http://hdl.handle.net/2152/2223
Subjects/Keywords: Production scheduling; Risk management
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APA (6th Edition):
Zhu, G. (2005). Disruption managment for project scheduling problem. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2223
Chicago Manual of Style (16th Edition):
Zhu, Guidong. “Disruption managment for project scheduling problem.” 2005. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/2223.
MLA Handbook (7th Edition):
Zhu, Guidong. “Disruption managment for project scheduling problem.” 2005. Web. 05 Mar 2021.
Vancouver:
Zhu G. Disruption managment for project scheduling problem. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2005. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/2223.
Council of Science Editors:
Zhu G. Disruption managment for project scheduling problem. [Doctoral Dissertation]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/2223
24.
Derinkuyu, Kursad.
Optimization models for transport and service scheduling.
Degree: PhD, Operations Research and Industrial Engineering, 2011, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2011-05-3422
► This dissertation focuses on service scheduling and transshipment problems. The study of service scheduling is motivated by decisions facing service planners, who must inspect and…
(more)
▼ This dissertation focuses on service scheduling and transshipment problems. The study of service scheduling is motivated by decisions facing service planners, who must inspect and maintain geographically dispersed infrastructure facilities. We study the problem of deciding which operations a service unit must perform at each customer location, given the sequence in which the unit periodically visits these locations. Each customer requires multiple service operations, and each operation has a time-varying completion or penalty cost that depends on the previous service time. The goal is to schedule the service start time for each customer and select the operations to perform so as to minimize the total completion cost.
We first discuss how to solve a special case of this problem in which each site is visited only once per service cycle. We formulate this problem as a discrete time indexed network flow problem and prove that it is NP-hard in the ordinary sense. Then, we represent the problem as a multidimensional shortest path problem with path-dependent arc lengths. In this structure, arc costs depend on the total time spent for all customers. The resulting formulation is solvable via algorithms that have pseudo-polynomial run times. Computational results show that the shortest path approach outperformed the general network flow model.
We then analyze the general case of this problem, in which each site can be visited more than once and prove that the problem is NP-Hard in the strong sense. We discuss the valid cuts and describe the preprocessor that reduces the problem size. Next, we examine an application to the general case of the problem and develop a fast and effective heuristic procedure that repeatedly applies the shortest path approach to subsequences that do not visit any customer more than once. Computational results for several problem instances show that the proposed heuristic identifies near optimal results very quickly, whereas a general purpose integer-programming solver (CPLEX) is not able to find an optimal solution even after many hours of computational time. Then we focus on techniques such as problem reduction, branching variables, and subdividing problem to smaller problems to get better solution times for the actual problem. Computational results show that these techniques can improve solution times substantially.
Finally, we study a transshipment problem, in which the shipments need to be transported from their origin to destination and are subject to the logical and physical transportation network on which they rely. We consider a space-time network that allows one to formulate the problem as a multi-commodity network flow problem with additional side constraints and show the complexity results. We propose alternative models and propose algorithms for lower and upper bound calculations.
Advisors/Committee Members: Balakrishnan, Anant (advisor), Kutanoglu, Erhan (advisor), Bard, Jonathan F. (committee member), Morton, David P. (committee member), Waller, Travis S. (committee member).
Subjects/Keywords: Service scheduling; Maintenance; Transportation; Time-space network
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Export
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Manager
APA (6th Edition):
Derinkuyu, K. (2011). Optimization models for transport and service scheduling. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3422
Chicago Manual of Style (16th Edition):
Derinkuyu, Kursad. “Optimization models for transport and service scheduling.” 2011. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/ETD-UT-2011-05-3422.
MLA Handbook (7th Edition):
Derinkuyu, Kursad. “Optimization models for transport and service scheduling.” 2011. Web. 05 Mar 2021.
Vancouver:
Derinkuyu K. Optimization models for transport and service scheduling. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2011. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3422.
Council of Science Editors:
Derinkuyu K. Optimization models for transport and service scheduling. [Doctoral Dissertation]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3422
University of Texas – Austin
25.
Wang, Yong Min.
A column generation approach for stochastic optimization problems.
Degree: PhD, Operations Research and Industrial Engineering, 2006, University of Texas – Austin
URL: http://hdl.handle.net/2152/2979
Subjects/Keywords: Stochastic programming; Integer programming; Combinatorial optimization; Scheduling – Mathematical models; Planning – Mathematical models; Uncertainty
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APA ·
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MLA ·
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Export
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APA (6th Edition):
Wang, Y. M. (2006). A column generation approach for stochastic optimization problems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2979
Chicago Manual of Style (16th Edition):
Wang, Yong Min. “A column generation approach for stochastic optimization problems.” 2006. Doctoral Dissertation, University of Texas – Austin. Accessed March 05, 2021.
http://hdl.handle.net/2152/2979.
MLA Handbook (7th Edition):
Wang, Yong Min. “A column generation approach for stochastic optimization problems.” 2006. Web. 05 Mar 2021.
Vancouver:
Wang YM. A column generation approach for stochastic optimization problems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2006. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2152/2979.
Council of Science Editors:
Wang YM. A column generation approach for stochastic optimization problems. [Doctoral Dissertation]. University of Texas – Austin; 2006. Available from: http://hdl.handle.net/2152/2979
. 
Open Access Theses and Dissertations
