2026-09-19T09:05:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/677702022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Jérémie Gallien.
Williams, Gareth Pierce
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2011-12-19T18:49:16Z
2011-12-19T18:49:16Z
2011
2011
http://hdl.handle.net/1721.1/67770
767527864
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 203-208).
Planning and controlling production in a large make-to-order manufacturing network poses complex and costly operational problems. As customers continually submit customized orders, a centralized decision-maker must quickly allocate each order to production facilities with limited but flexible labor, production capacity, and parts availability. In collaboration with a major desktop manufacturing firm, we study these relatively unexplored problems, the firm's solutions to it, and alternate approaches based on mathematical optimization. We develop and analyze three distinct models for these problems which incorporate the firm's data, testing, and feedback, emphasizing realism and usability. The problem is cast as a Dynamic Program with a detailed model of demand uncertainty. Decisions include planning production over time, from a few hours to a quarter year, and determining the appropriate amount of labor at each factory. The objective is to minimize shipping and labor costs while providing superb customer service by producing orders on-time. Because the stochastic Dynamic Program is too difficult to solve directly, we propose deterministic, rolling-horizon, Mixed Integer Linear Programs, including one that uses recently developed affinely-adjustable Robust Optimization techniques, that can be solved in a few minutes. Simulations and a perfect hindsight upper bound show that they can be near-optimal. Consistent results indicate that these solutions offer several hundred thousand dollars in daily cost saving opportunities by accounting for future demand and repeatedly re-balancing factory loads via re-allocating orders, improving capacity utilization, and improving on-time delivery.
by Gareth Pierce Williams.
Ph.D.
208 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Operations Research Center.
Dynamic order allocation for make-to-order manufacturing networks : an industrial case study of optimization under uncertainty/
Thesis
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Publication
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<Abstract>Planning and controlling production in a large make-to-order manufacturing network poses complex and costly operational problems. As customers continually submit customized orders, a centralized decision-maker must quickly allocate each order to production facilities with limited but flexible labor, production capacity, and parts availability. In collaboration with a major desktop manufacturing firm, we study these relatively unexplored problems, the firm's solutions to it, and alternate approaches based on mathematical optimization. We develop and analyze three distinct models for these problems which incorporate the firm's data, testing, and feedback, emphasizing realism and usability. The problem is cast as a Dynamic Program with a detailed model of demand uncertainty. Decisions include planning production over time, from a few hours to a quarter year, and determining the appropriate amount of labor at each factory. The objective is to minimize shipping and labor costs while providing superb customer service by producing orders on-time. Because the stochastic Dynamic Program is too difficult to solve directly, we propose deterministic, rolling-horizon, Mixed Integer Linear Programs, including one that uses recently developed affinely-adjustable Robust Optimization techniques, that can be solved in a few minutes. Simulations and a perfect hindsight upper bound show that they can be near-optimal. Consistent results indicate that these solutions offer several hundred thousand dollars in daily cost saving opportunities by accounting for future demand and repeatedly re-balancing factory loads via re-allocating orders, improving capacity utilization, and improving on-time delivery.</Abstract>
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