期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 302, 期 3, 页码 909-924出版社
ELSEVIER
DOI: 10.1016/j.ejor.2022.01.047
关键词
Multiple objective programming; Branch and bound; Combinatorial optimization; Linear relaxation; Warm-starting
In this paper, a generic branch-and-bound algorithm for solving multi-objective integer linear programming problems is proposed. The algorithm speeds up the computations and significantly reduces CPU time, effectively addressing the difficulties that arise when non-binary integer variables are introduced.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据