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Warm-starting lower bound set computations for branch-and-bound algorithms for multi objective integer linear programs
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DOI：10.1016/j.ejor.2022.01.047.png)
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摘要
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In this paper we propose a generic branch-and-bound algorithm for solving multi-objective integer linear programming problems. In the recent literature, competitive frameworks has been proposed for bi-objective 0-1 problems, and many of these frameworks rely on the use of the linear relaxation to obtain lower bound sets. When increasing the number of objective functions, however, the polyhedral structure of the linear relaxation becomes more complex, and consequently requires more computational effort to obtain. In this paper we overcome this obstacle by speeding up the computations. To do so, in each branching node we use information available from its father node to warm-start a Bensons-like algorithm. We show that the proposed algorithm significantly reduces the CPU time of the framework on several different problem classes with three, four and five objective functions. Moreover, we point out difficulties that arise when non-binary integer variables are introduced in the models, and test our algorithm on problem that contains non-binary integer variables too. (C) 2022 The Author(s). Published by Elsevier B.V.
Keyword:
Multiple objective programming
Branch and bound
Combinatorial optimization
Linear relaxation
Warm-starting
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IF:
6
论文数:
2.2W
被引数:
6.4W
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引用论文
Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case混合0-1线性规划的多目标分支和界限: 对双目标情况的修正和改进
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