A biased-randomized iterated local search for the distributed assembly permutation flow-shop problem

Daniele Ferone, Sara Hatami, Eliana M. González-Neira, Angel A. Juan, Paola Festa

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow-shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow-shop configuration; in the second stage, the outcomes of each flow-shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter-less algorithm that makes use of a biased-randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state-of-the-art approaches, obtaining several new best solutions.

Original languageEnglish
Pages (from-to)1368-1391
Number of pages24
JournalInternational Transactions in Operational Research
Volume27
Issue number3
DOIs
StatePublished - 01 May 2020

Keywords

  • assembly system
  • biased randomization
  • distributed manufacturing system
  • iterated local search
  • metaheuristic
  • permutation flow-shop scheduling

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