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Stochastic Integer Programming: Deco...

Wang, Cheng Marshal.

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Stochastic Integer Programming: Decomposition Methods and Industrial Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic Integer Programming: Decomposition Methods and Industrial Applications./
作者:	Wang, Cheng Marshal.
面頁冊數:	171 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:	Dissertation Abstracts International76-02B(E).
標題:	Operations Research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3637117
ISBN:	9781321195354

Stochastic Integer Programming: Decomposition Methods and Industrial Applications.
Wang, Cheng Marshal.

Stochastic Integer Programming: Decomposition Methods and Industrial Applications. - 171 p.

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2014.

This item must not be sold to any third party vendors.

Many practical problems from industry that contain uncertain demands, costs and other quantities are challenging to solve. Stochastic Mixed Integer Programs (SMIPs) have become an emerging tool to incorporate uncertainty in optimization problems. The stochastic and mixed integer nature of SMIPs makes them very challenging to solve. Decomposition methods have been developed to solve various practical problems modeled as large-scale SMIPs.

ISBN: 9781321195354Subjects--Topical Terms:

626629
Operations Research.

Stochastic Integer Programming: Decomposition Methods and Industrial Applications.
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245 1 0 $a Stochastic Integer Programming: Decomposition Methods and Industrial Applications.
300 $a 171 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
500 $a Adviser: Timothy C. Y. Chan.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Many practical problems from industry that contain uncertain demands, costs and other quantities are challenging to solve. Stochastic Mixed Integer Programs (SMIPs) have become an emerging tool to incorporate uncertainty in optimization problems. The stochastic and mixed integer nature of SMIPs makes them very challenging to solve. Decomposition methods have been developed to solve various practical problems modeled as large-scale SMIPs.
520 $a In the thesis, we propose a scenario-wise decomposition method, the Dynamic Dual Decomposition method (D3 method), to decompose large-scale SMIPs in order to solve practical facility location problems more efficiently. The Lagrangian bounds are dynamically determined. We also consider alternative ways to represent non-anticipativity conditions to improve computational performance. The D3 method efficiently solved moderate and large sized instances whose deterministic equivalent problem could not be solved or solved much slower by a state-of-the-art commercial solver. Three-stage models are also studied and solved by the D3 method, which is found to be effective as well.
520 $a We combine the D3 method and Column Generation approach to solve a stochastic version of the set packing problem. The proposed method is quite effective in numerical experiments and outperforms the commercial solver dramatically in most cases. We study the sensitivity of different density of patterns and find our proposed method is more robust than solving the whole problem by the commercial solver or by implementing the conventional column generation method directly.
520 $a We use the D3 method as a framework and combine it with Benders Decomposition Method (BDM) to solve the Stochastic Multi-plant Facility Location Problem. The mathematical formulation of the model is a two-stage SMIP problem with integer first stage variables and mixed integer second stage variables. The integer variables in both stages cause the large-scale SMIP model to be intractable. Some strategies for accelerating BDM have been developed in solving scenario subproblems which are decomposed by D3 method. We develop the aggregation method to aggregate scenarios in solving decomposed Benders Dual subproblems and approximate the solution of the original problems. Our computational experiments on benchmark data and randomly generated data shows that the proposed method can solve large-sized problems more efficiently than conventional Benders Decomposition or a commercial solver. The computational comparisons also show that the aggregation method can reduce the number of scenarios in large problems and obtain approximately optimal solutions in much shorter computational time.
590 $a School code: 0779.
650 4 $a Operations Research. $3 626629
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Applied Mathematics. $3 1669109
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710 2 $a University of Toronto (Canada). $b Mechanical and Industrial Engineering. $3 2100959
773 0 $t Dissertation Abstracts International $g 76-02B(E).
790 $a 0779
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3637117