A scalable parallel implementation of the Cluster Benders Decomposition algorithm
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Benders Decomposition (BD) is a method used to solve stochastic linear problems via scenario analysis. Cluster BD (CBD) is one of its smart improvements that speed up the execution time, taking advantage of tighter feasible cuts found by grouping scenarios into clusters. In this paper, we propose a new design for CBD, one which takes into account the role played by optimal cuts in the solution. Besides, we propose a new parallel scheme for CBD to deal with large-scale two-stage stochastic linear problems. Moreover, we characterise the problems for which our proposal performs best. The results obtained show computational gains from our proposal compared with the plain use of CPLEX, serial BD, parallel BD, serial CBD and parallel CBD.
KeywordsStochastic linear optimization Parallelization Scalability Clustering in stochastic optimzation Benders Decomposition
The Ministerio de Economía y Competitividad supported this work under contract TIN2017-84553-C2-2-R. Lluis M. Pla-Aragones wishes to acknowledge the financial support of the Spanish Research Program TRA2013-48180-C3-P. Some authors are members of the research group 2017-SGR363, funded by the Generalitat de Catalunya. Besides, this research is partly supported by the European Union FEDER (CAPAP-H6 network TIN2016-81840-REDT).
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