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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2013.08.020