A scalable parallel implementation of the Cluster Benders Decomposition algorithm

被引：2
|
作者：
Mateo, Jordi [1 ]
Pla, Lluis M. [2 ]
Solsona, Francesc [1 ]
Pages, Adela [2 ]
机构：
[1] Univ Lleida, Dept Comp Sci, Jaume II 69, Lleida 25001, Spain
[2] Univ Lleida, Dept Math, Jaume II 71, Lleida 25001, Spain
来源：
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2019年 / 22卷 / 03期
关键词：
Stochastic linear optimization; Parallelization; Scalability; Clustering in stochastic optimzation; Benders Decomposition;
D O I：
10.1007/s10586-018-2878-4
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：877 / 886
页数：10
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