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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Published in:	Computers & operations research Vol. 43; pp. 90 - 99
Main Authors:	Crainic, Teodor Gabriel, Hewitt, Mike, Rei, Walter
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.03.2014 Elsevier Pergamon Press Inc
Subjects:	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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Cites_doi	10.1016/j.ejor.2009.06.011 10.1016/j.apm.2010.11.042 10.1002/net.20009 10.1137/0117061 10.1016/S0377-2217(96)00298-6 10.1016/S0377-2217(00)00116-8 10.1287/opre.48.1.20.12441 10.1016/j.ijpe.2008.08.002 10.1016/j.ejor.2004.01.046 10.1007/BF01386316 10.1016/j.cor.2011.06.021 10.1021/ie0100030 10.1016/j.cor.2012.07.009 10.3934/jimo.2007.3.29 10.1023/A:1023071216923 10.1016/j.ejor.2008.11.040 10.1007/s10287-008-0087-y 10.1002/net.20456 10.1287/trsc.1090.0265 10.1287/moor.16.1.119 10.1007/BF00247208 10.1023/A:1021853807313 10.1287/mnsc.47.2.295.9834 10.1016/j.orl.2006.12.008 10.1287/ijoc.14.3.247.111 10.1007/s10287-010-0129-0
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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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References	Escudero, Garín, Merino, Pérez (bib10) 2012; 39 Wallace (bib31) 2000; 48 Heitsch, Römisch (bib14) 2007; 35 Marsland (bib21) 2009 Schütz, Tomasgard, Ahmed (bib25) 2009; 199 Santoso, Ahmed, Goetschalckx, Shapiro (bib24) 2005; 167 Høyland, Wallace (bib17) 2001; 47 Riis, Andersen (bib22) 2002; 14 Crainic, Laporte (bib9) 1997; 97 Azaron, Brown, Tarim, Modarres (bib3) 2008; 116 Crainic TG, Errico F, Rei W, Ricciardi N. Modeling demand uncertainty in two-tiered city logistics planning, Publication CIRRELT-20-54, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, Université de Montréal, Montréal, QC, Canada, 2011. Crainic, Fu, Gendreau, Rei, Wallace (bib8) 2011; 58 Høyland, Kaut, Wallace (bib16) 2003; 24 Slyke, Wets (bib26) 1969; 17 Benders (bib4) 1962; 4 Bidhandi, Yusuff (bib5) 2011; 35 Klibi, Martel, Guitouni (bib18) 2010; 203 Haugen, Løkketangen, Woodruff (bib13) 2001; 132 Heitsch, Römisch (bib15) 2009; 6 Lium, Crainic, Wallace (bib19) 2009; 43 Smith, Schaefer, Yen (bib27) 2004; 44 Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007. p. 1027–35. Alonso-Ayuso, Escudero, Garìn, Ortu no, Pérez (bib1) 2003; 26 Birge, Louveaux (bib6) 2011 Thapalia, Wallace, Kaut, Crainic (bib28) 2012; 9 Escudero, Garín, Pérez, Unzueta (bib11) 2013; 40 Vila, Martel, Beauregard (bib30) 2007; 3 Rockafellar, Wets (bib23) 1991; 16 Tsiakis, Shah, Pantelides (bib29) 2001; 40 Gaivoronski (bib12) 2005; vol. 5 Løkketangen, Woodruff (bib20) 1996; 2 Gaivoronski (10.1016/j.cor.2013.08.020_bib12) 2005; vol. 5 Bidhandi (10.1016/j.cor.2013.08.020_bib5) 2011; 35 Escudero (10.1016/j.cor.2013.08.020_bib11) 2013; 40 Smith (10.1016/j.cor.2013.08.020_bib27) 2004; 44 Crainic (10.1016/j.cor.2013.08.020_bib8) 2011; 58 Thapalia (10.1016/j.cor.2013.08.020_bib28) 2012; 9 Benders (10.1016/j.cor.2013.08.020_bib4) 1962; 4 Heitsch (10.1016/j.cor.2013.08.020_bib15) 2009; 6 10.1016/j.cor.2013.08.020_bib2 Haugen (10.1016/j.cor.2013.08.020_bib13) 2001; 132 10.1016/j.cor.2013.08.020_bib7 Marsland (10.1016/j.cor.2013.08.020_bib21) 2009 Riis (10.1016/j.cor.2013.08.020_bib22) 2002; 14 Azaron (10.1016/j.cor.2013.08.020_bib3) 2008; 116 Rockafellar (10.1016/j.cor.2013.08.020_bib23) 1991; 16 Klibi (10.1016/j.cor.2013.08.020_bib18) 2010; 203 Vila (10.1016/j.cor.2013.08.020_bib30) 2007; 3 Lium (10.1016/j.cor.2013.08.020_bib19) 2009; 43 Høyland (10.1016/j.cor.2013.08.020_bib17) 2001; 47 Escudero (10.1016/j.cor.2013.08.020_bib10) 2012; 39 Heitsch (10.1016/j.cor.2013.08.020_bib14) 2007; 35 Wallace (10.1016/j.cor.2013.08.020_bib31) 2000; 48 Løkketangen (10.1016/j.cor.2013.08.020_bib20) 1996; 2 Schütz (10.1016/j.cor.2013.08.020_bib25) 2009; 199 Crainic (10.1016/j.cor.2013.08.020_bib9) 1997; 97 Slyke (10.1016/j.cor.2013.08.020_bib26) 1969; 17 Santoso (10.1016/j.cor.2013.08.020_bib24) 2005; 167 Alonso-Ayuso (10.1016/j.cor.2013.08.020_bib1) 2003; 26 Birge (10.1016/j.cor.2013.08.020_bib6) 2011 Tsiakis (10.1016/j.cor.2013.08.020_bib29) 2001; 40 Høyland (10.1016/j.cor.2013.08.020_bib16) 2003; 24
References_xml	– volume: vol. 5 start-page: 669 year: 2005 end-page: 704 ident: bib12 article-title: Stochastic optimization problems in telecommunications publication-title: Applications of stochastic programming of MPS/SIAM series on optimization – volume: 35 start-page: 731 year: 2007 end-page: 738 ident: bib14 article-title: A note on scenario reduction for two-stage stochastic programs publication-title: Operations Research Letters – volume: 2 start-page: 111 year: 1996 end-page: 128 ident: bib20 article-title: Progressive hedging and tabu search applied to mixed integer (0,1) multistage stochastic programming publication-title: Journal of Heuristics – volume: 40 start-page: 362 year: 2013 end-page: 377 ident: bib11 article-title: Scenario cluster decomposition of the lagrangian dual in two-stage stochastic mixed 0–1 optimization publication-title: Computers & Operations Research – volume: 26 start-page: 97 year: 2003 end-page: 124 ident: bib1 article-title: An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming publication-title: Journal of Global Optimization – volume: 39 start-page: 1133 year: 2012 end-page: 1144 ident: bib10 article-title: An algorithmic framework for solving large-scale multistage sotchastic mixed 0–1 problems with nonsymmetric scenario trees publication-title: Computers & Operations Research – volume: 167 start-page: 96 year: 2005 end-page: 115 ident: bib24 article-title: A stochastic programming approach for supply chain network design under uncertainty publication-title: European Journal of Operational Research – reference: Crainic TG, Errico F, Rei W, Ricciardi N. Modeling demand uncertainty in two-tiered city logistics planning, Publication CIRRELT-20-54, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, Université de Montréal, Montréal, QC, Canada, 2011. – volume: 199 start-page: 409 year: 2009 end-page: 419 ident: bib25 article-title: Supply chain design under uncertainty using sample average approximation and dual decomposition publication-title: European Journal of Operational Research – volume: 3 start-page: 29 year: 2007 end-page: 50 ident: bib30 article-title: Taking market forces into account in the design of production-distribution networks publication-title: Journal of Industrial and Management Optimization – volume: 203 start-page: 283 year: 2010 end-page: 293 ident: bib18 article-title: The design of robust value-creating supply chain networks publication-title: European Journal of Operational Research – year: 2009 ident: bib21 article-title: Machine learning – volume: 97 start-page: 409 year: 1997 end-page: 438 ident: bib9 article-title: Planning models for freight transportation publication-title: European Journal of Operational Research – reference: Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007. p. 1027–35. – volume: 35 start-page: 2618 year: 2011 end-page: 2630 ident: bib5 article-title: Integrated supply chain planning under uncertainty using an improved stochastic approach publication-title: Applied Mathematical Modelling – volume: 40 start-page: 3585 year: 2001 end-page: 3604 ident: bib29 article-title: Design of multi-echelon supply chain networks under demand uncertainty publication-title: Industrial & Engineering Chemistry Research – volume: 116 start-page: 129 year: 2008 end-page: 138 ident: bib3 article-title: A multi-objective stochastic programming approach for supply chain design considering risk publication-title: International Journal of Production Economics – volume: 132 start-page: 116 year: 2001 end-page: 122 ident: bib13 article-title: Progressive hedging as a meta-heuristic applied to stochastic lot-sizing publication-title: European Journal of Operational Research – volume: 43 start-page: 144 year: 2009 end-page: 157 ident: bib19 article-title: A study of demand stochasticity in service network design publication-title: Transportation Science – volume: 4 start-page: 238 year: 1962 end-page: 252 ident: bib4 article-title: Partitioning procedures for solving mixed-variables programming problems publication-title: Numerische Mathematik – volume: 14 start-page: 247 year: 2002 end-page: 260 ident: bib22 article-title: Capacitated network design with uncertain demand publication-title: INFORMS Journal on Computing – volume: 44 start-page: 12 year: 2004 end-page: 26 ident: bib27 article-title: A stochastic integer programming approach to solving a synchronous optical network ring design problem publication-title: Networks – volume: 58 start-page: 114 year: 2011 end-page: 124 ident: bib8 article-title: Progressive hedging-based metaheuristics for stochastic network design publication-title: Networks – volume: 16 start-page: 119 year: 1991 end-page: 147 ident: bib23 article-title: Scenarios and policy aggregation in optimization under uncertainty publication-title: Mathematics of Operations Research – volume: 47 start-page: 295 year: 2001 end-page: 307 ident: bib17 article-title: Generating scenario trees for multistage decision problems publication-title: Management Science – volume: 17 start-page: 638 year: 1969 end-page: 663 ident: bib26 article-title: L-shaped linear programs with application to optimal control and stochastic programming publication-title: SIAM Journal on Applied Mathematics – volume: 9 start-page: 139 year: 2012 end-page: 160 ident: bib28 article-title: Single source single-commodity stochastic network design publication-title: Computational Management Science – year: 2011 ident: bib6 article-title: Introduction to stochastic programming – volume: 48 start-page: 20 year: 2000 end-page: 25 ident: bib31 article-title: Decision making under uncertainty publication-title: Operations Research – volume: 6 start-page: 117 year: 2009 end-page: 133 ident: bib15 article-title: Scenario tree reduction for multistage stochastic programs publication-title: Computational Management Science – volume: 24 start-page: 169 year: 2003 end-page: 185 ident: bib16 article-title: A heuristic for moment-matching scenario generation publication-title: Computational Optimization and Applications – volume: 203 start-page: 283 year: 2010 ident: 10.1016/j.cor.2013.08.020_bib18 article-title: The design of robust value-creating supply chain networks publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2009.06.011 – ident: 10.1016/j.cor.2013.08.020_bib2 – volume: 35 start-page: 2618 year: 2011 ident: 10.1016/j.cor.2013.08.020_bib5 article-title: Integrated supply chain planning under uncertainty using an improved stochastic approach publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2010.11.042 – volume: 44 start-page: 12 issue: 1 year: 2004 ident: 10.1016/j.cor.2013.08.020_bib27 article-title: A stochastic integer programming approach to solving a synchronous optical network ring design problem publication-title: Networks doi: 10.1002/net.20009 – volume: 17 start-page: 638 year: 1969 ident: 10.1016/j.cor.2013.08.020_bib26 article-title: L-shaped linear programs with application to optimal control and stochastic programming publication-title: SIAM Journal on Applied Mathematics doi: 10.1137/0117061 – volume: 97 start-page: 409 issue: 3 year: 1997 ident: 10.1016/j.cor.2013.08.020_bib9 article-title: Planning models for freight transportation publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(96)00298-6 – volume: 132 start-page: 116 year: 2001 ident: 10.1016/j.cor.2013.08.020_bib13 article-title: Progressive hedging as a meta-heuristic applied to stochastic lot-sizing publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(00)00116-8 – volume: 48 start-page: 20 issue: 1 year: 2000 ident: 10.1016/j.cor.2013.08.020_bib31 article-title: Decision making under uncertainty publication-title: Operations Research doi: 10.1287/opre.48.1.20.12441 – volume: vol. 5 start-page: 669 year: 2005 ident: 10.1016/j.cor.2013.08.020_bib12 article-title: Stochastic optimization problems in telecommunications – volume: 116 start-page: 129 year: 2008 ident: 10.1016/j.cor.2013.08.020_bib3 article-title: A multi-objective stochastic programming approach for supply chain design considering risk publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2008.08.002 – volume: 167 start-page: 96 year: 2005 ident: 10.1016/j.cor.2013.08.020_bib24 article-title: A stochastic programming approach for supply chain network design under uncertainty publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2004.01.046 – volume: 4 start-page: 238 year: 1962 ident: 10.1016/j.cor.2013.08.020_bib4 article-title: Partitioning procedures for solving mixed-variables programming problems publication-title: Numerische Mathematik doi: 10.1007/BF01386316 – year: 2011 ident: 10.1016/j.cor.2013.08.020_bib6 – volume: 39 start-page: 1133 year: 2012 ident: 10.1016/j.cor.2013.08.020_bib10 article-title: An algorithmic framework for solving large-scale multistage sotchastic mixed 0–1 problems with nonsymmetric scenario trees publication-title: Computers & Operations Research doi: 10.1016/j.cor.2011.06.021 – volume: 40 start-page: 3585 year: 2001 ident: 10.1016/j.cor.2013.08.020_bib29 article-title: Design of multi-echelon supply chain networks under demand uncertainty publication-title: Industrial & Engineering Chemistry Research doi: 10.1021/ie0100030 – volume: 40 start-page: 362 year: 2013 ident: 10.1016/j.cor.2013.08.020_bib11 article-title: Scenario cluster decomposition of the lagrangian dual in two-stage stochastic mixed 0–1 optimization publication-title: Computers & Operations Research doi: 10.1016/j.cor.2012.07.009 – volume: 3 start-page: 29 issue: 1 year: 2007 ident: 10.1016/j.cor.2013.08.020_bib30 article-title: Taking market forces into account in the design of production-distribution networks publication-title: Journal of Industrial and Management Optimization doi: 10.3934/jimo.2007.3.29 – volume: 26 start-page: 97 year: 2003 ident: 10.1016/j.cor.2013.08.020_bib1 article-title: An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming publication-title: Journal of Global Optimization doi: 10.1023/A:1023071216923 – volume: 199 start-page: 409 year: 2009 ident: 10.1016/j.cor.2013.08.020_bib25 article-title: Supply chain design under uncertainty using sample average approximation and dual decomposition publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2008.11.040 – volume: 6 start-page: 117 year: 2009 ident: 10.1016/j.cor.2013.08.020_bib15 article-title: Scenario tree reduction for multistage stochastic programs publication-title: Computational Management Science doi: 10.1007/s10287-008-0087-y – year: 2009 ident: 10.1016/j.cor.2013.08.020_bib21 – volume: 58 start-page: 114 issue: 2 year: 2011 ident: 10.1016/j.cor.2013.08.020_bib8 article-title: Progressive hedging-based metaheuristics for stochastic network design publication-title: Networks doi: 10.1002/net.20456 – volume: 43 start-page: 144 issue: 2 year: 2009 ident: 10.1016/j.cor.2013.08.020_bib19 article-title: A study of demand stochasticity in service network design publication-title: Transportation Science doi: 10.1287/trsc.1090.0265 – ident: 10.1016/j.cor.2013.08.020_bib7 – volume: 16 start-page: 119 issue: 1 year: 1991 ident: 10.1016/j.cor.2013.08.020_bib23 article-title: Scenarios and policy aggregation in optimization under uncertainty publication-title: Mathematics of Operations Research doi: 10.1287/moor.16.1.119 – volume: 2 start-page: 111 year: 1996 ident: 10.1016/j.cor.2013.08.020_bib20 article-title: Progressive hedging and tabu search applied to mixed integer (0,1) multistage stochastic programming publication-title: Journal of Heuristics doi: 10.1007/BF00247208 – volume: 24 start-page: 169 year: 2003 ident: 10.1016/j.cor.2013.08.020_bib16 article-title: A heuristic for moment-matching scenario generation publication-title: Computational Optimization and Applications doi: 10.1023/A:1021853807313 – volume: 47 start-page: 295 issue: 2 year: 2001 ident: 10.1016/j.cor.2013.08.020_bib17 article-title: Generating scenario trees for multistage decision problems publication-title: Management Science doi: 10.1287/mnsc.47.2.295.9834 – volume: 35 start-page: 731 year: 2007 ident: 10.1016/j.cor.2013.08.020_bib14 article-title: A note on scenario reduction for two-stage stochastic programs publication-title: Operations Research Letters doi: 10.1016/j.orl.2006.12.008 – volume: 14 start-page: 247 issue: 3 year: 2002 ident: 10.1016/j.cor.2013.08.020_bib22 article-title: Capacitated network design with uncertain demand publication-title: INFORMS Journal on Computing doi: 10.1287/ijoc.14.3.247.111 – volume: 9 start-page: 139 year: 2012 ident: 10.1016/j.cor.2013.08.020_bib28 article-title: Single source single-commodity stochastic network design publication-title: Computational Management Science doi: 10.1007/s10287-010-0129-0
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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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