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You searched for subject:(Reentrant flow). Showing records 1 – 3 of 3 total matches.

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University of Texas – Austin

1. -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

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 (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

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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 February 28, 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. 28 Feb 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 Feb 28]. 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


Virginia Tech

2. Varadarajan, Amrusha. Stochastic Scheduling for a Network of MEMS Job Shops.

Degree: PhD, Industrial and Systems Engineering, 2006, Virginia Tech

This work is motivated by the pressing need for operational control in the fabrication of Microelectromechanical systems or MEMS. MEMS are miniature three-dimensional integrated electromechanical systems with the ability to absorb information from the environment, process this information and suitably react to it. These devices offer tremendous advantages owing to their small size, low power consumption, low mass and high functionality, which makes them very attractive in applications with stringent demands on weight, functionality and cost. While the system''s "brain" (device electronics) is fabricated using traditional IC technology, the micromechanical components necessitate very intricate and sophisticated processing of silicon or other suitable substrates. A dearth of fabrication facilities with micromachining capabilities and a lengthy gestation period from design to mass fabrication and commercial acceptance of the product in the market are factors most often implicated in hampering the growth of MEMS. These devices are highly application specific with low production volumes and the few fabs that do possess micromachining capabilities are unable to offer a complete array of fabrication processes in order to be able to cater to the needs of the MEMS R&D community. A distributed fabrication network has, therefore, emerged to serve the evolving needs of this high investment, low volume MEMS industry. Under this environment, a central facility coordinates between a network of fabrication centers (Network of MEMS job shops  – NMJS) containing micromachining capabilities. These fabrication centers include commercial, academic and government fabs, which make their services available to the ordinary customer. Wafers are shipped from one facility to another until all processing requirements are met. The lengthy and intricate process sequences that need to be performed over a network of capital intensive facilities are complicated by dynamic job arrivals, stochastic processing times, sequence-dependent set ups and travel between fabs. Unless the production of these novel devices is carefully optimized, the benefits of distributed fabrication could be completely overshadowed by lengthy lead times, chaotic routings and costly processing. Our goal, therefore, is to develop and validate an approach for optimal routing (assignment) and sequencing of MEMS devices in a network of stochastic job shops with the objective of minimizing the sum of completion times and the cost incurred, given a set of fabs, machines and an expected product mix. In view of our goal, we begin by modeling the stochastic NMJS problem as a two-stage stochastic program with recourse where the first-stage variables are binary and the second-stage variables are continuous. The key decision variables are binary and pertain to the assignment of jobs to machines and their sequencing for processing on the machines. The assignment variables essentially fix the route of a job as it travels through the network because these variables specify the machine on which… Advisors/Committee Members: Sarin, Subhash C. (committeechair), Deisenroth, Michael P. (committee member), Sturges, Robert H. (committee member), Fraticelli, Barbara M. P. (committee member), Hendricks, Robert W. (committee member).

Subjects/Keywords: L-shaped Method; Heuristic; Stochastic Programming; Dynamic Scheduling; Deadlock prevention; Feasibility Cuts; Reentrant Flow

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

APA (6th Edition):

Varadarajan, A. (2006). Stochastic Scheduling for a Network of MEMS Job Shops. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77038

Chicago Manual of Style (16th Edition):

Varadarajan, Amrusha. “Stochastic Scheduling for a Network of MEMS Job Shops.” 2006. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/77038.

MLA Handbook (7th Edition):

Varadarajan, Amrusha. “Stochastic Scheduling for a Network of MEMS Job Shops.” 2006. Web. 28 Feb 2021.

Vancouver:

Varadarajan A. Stochastic Scheduling for a Network of MEMS Job Shops. [Internet] [Doctoral dissertation]. Virginia Tech; 2006. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/77038.

Council of Science Editors:

Varadarajan A. Stochastic Scheduling for a Network of MEMS Job Shops. [Doctoral Dissertation]. Virginia Tech; 2006. Available from: http://hdl.handle.net/10919/77038

3. -1113-1730. Optimization models for manufacturing and personnel scheduling.

Degree: PhD, Operations Research and Industrial Engineering, 2017, University of Texas – Austin

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

…in what can be viewed as a reentrant flow shop. The reentrant characteristic of the… …to 10.08%. Moreover, when reentrant flow is taken into account, improvements of up to 42… …and “flow.” Each in turn imposes increasing setup times, ranging from a few minutes to half… …be Oi and the demand at each node j∈N2 be Dj, and let Yij be the nonnegative network flow… 

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

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 February 28, 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. 28 Feb 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 Feb 28]. 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

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