Robust facility layout design for flexible manufacturing: a doe-based heuristic

  • Hani Pourvaziri*
  • , Saeideh Salimpour
  • , Seyed Taghi Akhavan Niaki
  • , Ahmed Azab
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1) where a robust layout is constructed, and sub-problem 2 (SP2) to obtain the best routes of products. To solve SP1, design of experiments is used to find a critical period, which is the period most affected under demand changes. Then, the layout for the critical period is determined using a hybridized genetic-tabu search algorithm. Then SP2 is solved by a branch and cut algorithm to obtain the optimal routes of the products in each period. The performance of the proposed methodology is illustrated using a case study and is benchmarked against rival ones from the literature.

Original languageEnglish
Pages (from-to)5633-5654
Number of pages22
JournalInternational Journal of Production Research
Volume60
Issue number18
DOIs
StatePublished - 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Facilities layout
  • design of experiments
  • flexible manufacturing
  • metaheuristics
  • robust design

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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