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Comparison of enhanced constructive layout optimization technique with Tabu-search and Particle Swarm Optimization methodologies

  • Umm Al-Qura University
  • RWTH Aachen University

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

3 Scopus citations

Abstract

An enhanced constructive technique with improved ordering criterion is presented for solving unequal-area facility layout problems (UA-FLPs). The technique is based on modified implementation of cluster boundary search algorithm and using an ordering criterion that is a weighted combination of material handling cost between pairs of facilities and areas of individual facilities. Due to high computational efficiency of the presented technique, it is especially suited for solving very large scale unequal-area facility layout problems. Its performance is compared with published UA-FLP techniques based on Tabu Search (TS) and Particle Swarm Optimization (PSO). It is shown that the presented technique outperforms both TS and PSO techniques. Also, for large-size layout problems it performs better than hybrid techniques formed by integrating TS and PSO methodologies.

Original languageEnglish
Title of host publicationIEOM 2015 - 5th International Conference on Industrial Engineering and Operations Management, Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479960651
DOIs
StatePublished - 23 Apr 2015
Event5th International Conference on Industrial Engineering and Operations Management, IEOM 2015 - Dubai, United Arab Emirates
Duration: 3 Mar 20155 Mar 2015

Publication series

NameIEOM 2015 - 5th International Conference on Industrial Engineering and Operations Management, Proceeding

Conference

Conference5th International Conference on Industrial Engineering and Operations Management, IEOM 2015
Country/TerritoryUnited Arab Emirates
CityDubai
Period3/03/155/03/15

Keywords

  • Facility layout
  • Tabu-search
  • analytical optimization technique
  • constructive placement
  • particle swarm optimization

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