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

  • Yiyong Xiao
  • , Ikou Kaku*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, ACE '10
Pages119-124
Number of pages6
StatePublished - 2010
Event9th WSEAS International Conference on Applications of Computer Engineering, ACE '10 - Penang, Malaysia
Duration: 23 Mar 201025 Mar 2010

Publication series

NameProceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, ACE '10

Conference

Conference9th WSEAS International Conference on Applications of Computer Engineering, ACE '10
Country/TerritoryMalaysia
CityPenang
Period23/03/1025/03/10

Keywords

  • Genetic algorithm
  • Meta-heuristic
  • Multilevel lot-sizing
  • Variable neighborhood search

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