A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs

A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs Comput Manag Sci https://doi.org/10.1007/s10287-018-0311-3 ORIGINAL PAPER A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs 1 1 Semih Atakan · Suvrajeet Sen Received: 27 September 2017 / Accepted: 7 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Progressive Hedging (PH) is a well-known algorithm for solving multi- stage stochastic convex optimization problems. Most previous extensions of PH for mixed-integer stochastic programs have been implemented without convergence guarantees. In this paper, we present a new framework that shows how PH can be uti- lized while guaranteeing convergence to globally optimal solutions of mixed-integer stochastic convex programs. We demonstrate the effectiveness of the proposed frame- work through computational experiments. Keywords Multi-stage mixed-integer stochastic convex programming · Progressive Hedging · Branch-and-bound 1 Introduction A wide variety of operational and financial problems are modeled as mixed-integer programs (MIPs), where a certain subset of the decision variables are restricted to assume integer values. Examples include scheduling, production and inventory plan- ning, transportation, portfolio optimization, among many others. These problems are inherently difficult to handle as the decisions must be determined over a non-convex This research was funded by the NSF Grant ECCS 1548847 and AFOSR Grant FA9550-15-1-0267. B Suvrajeet http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Management Science Springer Journals

A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Optimization
ISSN
1619-697X
eISSN
1619-6988
D.O.I.
10.1007/s10287-018-0311-3
Publisher site
See Article on Publisher Site

Abstract

Comput Manag Sci https://doi.org/10.1007/s10287-018-0311-3 ORIGINAL PAPER A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs 1 1 Semih Atakan · Suvrajeet Sen Received: 27 September 2017 / Accepted: 7 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Progressive Hedging (PH) is a well-known algorithm for solving multi- stage stochastic convex optimization problems. Most previous extensions of PH for mixed-integer stochastic programs have been implemented without convergence guarantees. In this paper, we present a new framework that shows how PH can be uti- lized while guaranteeing convergence to globally optimal solutions of mixed-integer stochastic convex programs. We demonstrate the effectiveness of the proposed frame- work through computational experiments. Keywords Multi-stage mixed-integer stochastic convex programming · Progressive Hedging · Branch-and-bound 1 Introduction A wide variety of operational and financial problems are modeled as mixed-integer programs (MIPs), where a certain subset of the decision variables are restricted to assume integer values. Examples include scheduling, production and inventory plan- ning, transportation, portfolio optimization, among many others. These problems are inherently difficult to handle as the decisions must be determined over a non-convex This research was funded by the NSF Grant ECCS 1548847 and AFOSR Grant FA9550-15-1-0267. B Suvrajeet

Journal

Computational Management ScienceSpringer Journals

Published: Jun 5, 2018

References

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