Variable neighborhood search based approach for solving multilevel lot-sizing problems

Authors:
Yiyong Xiao;Ikou Kaku
Affiliations:
Department of System Engineering of Technology Engineering, Beihang University, Beijing, China;Department of Management Science and Engineering, Akita Prefectuarl University, Yulihonjo, Akita, Japan
Venue:
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Year:
2010

Citing 2
Cited 0

Variable neighborhood search

Computers and Operations Research
Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight

Computers & Mathematics with Applications

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Abstract

In this paper, we present an effective approach based on the variable neighborhood search (VNS) for solving multilevel lot-sizing (MLLS) problems. Two kinds of neighborhood search strategies, i.e., move at first improvement (MAFI) and move at best improvement (MABI), are adopted to improve the performance of proposed algorithm. Computational experiments are carried out on 96 benchmark problems to test the optimality against genetic algorithm on identical problems, and also to analyze the mechanism of VNS while it solving MLLS problem. Experimental outcomes show that the VNS algorithm equipped with MABI and emendation by inner corner property enjoys good optimality and high computation effectiveness as well, which is quite competitive to the existing algorithms that have been studied on the MLLS problems.