Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design

We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and...

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Vydáno v:	Computers & operations research Ročník 43; s. 90 - 99
Hlavní autoři:	Crainic, Teodor Gabriel, Hewitt, Mike, Rei, Walter
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.03.2014 Elsevier Pergamon Press Inc
Témata:	Applied sciences Commodities Exact sciences and technology Flows in networks. Combinatorial problems Heuristic Heuristic methods Integer programming Machine learning Mathematical models Mathematical problems Network design Network topologies Networks Operational research and scientific management Operational research. Management science Operations research Progressive hedging Scenario clustering Similarity Stochastic models Stochastic programs Stochasticity Strategy Studies Stochastic programs Network design Progressive hedging Scenario clustering Machine learning Lower bound Script Warranty Similarity Mixed integer programming Grouping Raw materials Stochastic programming Learning (artificial intelligence) Classification Heuristic method Metamodel
ISSN:	0305-0548, 1873-765X, 0305-0548
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Abstract	We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and performance of a new progressive hedging-based meta-heuristic for stochastic network design that solves subproblems comprising multiple scenarios. We compare the proposed strategies not only among themselves, but also against the strategy of grouping scenarios randomly and the lower bound provided by a state-of-the-art MIP solver. The results show that, by solving multi-scenario subproblems generated by the strategies we propose, the meta-heuristic produces better results in terms of solution quality and computing efficiency than when either single-scenario subproblems or multiple-scenario subproblems that are generated by picking scenarios at random are solved. The results also show that, considering all the strategies tested, the covering strategy with respect to commodity demands leads to the highest quality solutions and the quickest convergence.
AbstractList	We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and performance of a new progressive hedging-based meta-heuristic for stochastic network design that solves subproblems comprising multiple scenarios. We compare the proposed strategies not only among themselves, but also against the strategy of grouping scenarios randomly and the lower bound provided by a state-of-the-art MIP solver. The results show that, by solving multi-scenario subproblems generated by the strategies we propose, the meta-heuristic produces better results in terms of solution quality and computing efficiency than when either single-scenario subproblems or multiple-scenario subproblems that are generated by picking scenarios at random are solved. The results also show that, considering all the strategies tested, the covering strategy with respect to commodity demands leads to the highest quality solutions and the quickest convergence. We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and performance of a new progressive hedging-based meta-heuristic for stochastic network design that solves subproblems comprising multiple scenarios. We compare the proposed strategies not only among themselves, but also against the strategy of grouping scenarios randomly and the lower bound provided by a state-of-the-art MIP solver. The results show that, by solving multi-scenario subproblems generated by the strategies we propose, the meta-heuristic produces better results in terms of solution quality and computing efficiency than when either single-scenario subproblems or multiple-scenario subproblems that are generated by picking scenarios at random are solved. The results also show that, considering all the strategies tested, the covering strategy with respect to commodity demands leads to the highest quality solutions and the quickest convergence. [PUBLICATION ABSTRACT]
Author	Crainic, Teodor Gabriel Hewitt, Mike Rei, Walter
Author_xml	– sequence: 1 givenname: Teodor Gabriel surname: Crainic fullname: Crainic, Teodor Gabriel email: TeodorGabriel.Crainic@cirrelt.ca organization: CIRRELT, Canada – sequence: 2 givenname: Mike surname: Hewitt fullname: Hewitt, Mike email: mrheie@rit.edu organization: Department of Information Systems & Operations Management, Quinlan School of Business, Loyola University Chicago, United States – sequence: 3 givenname: Walter surname: Rei fullname: Rei, Walter email: Walter.Rei@cirrelt.ca organization: CIRRELT, Canada
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Keywords	Stochastic programs Network design Progressive hedging Scenario clustering Machine learning Lower bound Script Warranty Similarity Mixed integer programming Grouping Raw materials Stochastic programming Learning (artificial intelligence) Classification Heuristic method Metamodel
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Snippet	We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the...
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SubjectTerms	Applied sciences Commodities Exact sciences and technology Flows in networks. Combinatorial problems Heuristic Heuristic methods Integer programming Machine learning Mathematical models Mathematical problems Network design Network topologies Networks Operational research and scientific management Operational research. Management science Operations research Progressive hedging Scenario clustering Similarity Stochastic models Stochastic programs Stochasticity Strategy Studies
Title	Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
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