skip to main content
10.1145/3319619.3321933acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

A BRKGA for the integrated scheduling problem in FMSs

Published:13 July 2019Publication History

ABSTRACT

This work proposes a biased random key genetic algorithm (BRKGA) for the integrated scheduling of manufacturing, transport, and storage/retrieval operations inflexible manufacturing systems (FMSs). Only recently, research on this problem has been reported; however, no heuristic approaches have yet been reported. The computational results show the BRKGA to be capable of finding good quality solutions quickly.

References

  1. JC Bean. 1993. Genetics and random keys for sequencing amd optimization. (1993).Google ScholarGoogle Scholar
  2. Ü Bilge and G Ulusoy. 1995. A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research 43, 6 (1995), 1058--1070. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. OVK Chetty and MS Reddy. 2003. Genetic algorithms for studies on AS/RS integrated with machines. The International Journal of Advanced Manufacturing Technology 22, 11--12 (2003), 932--940.Google ScholarGoogle ScholarCross RefCross Ref
  4. A Gnanavelbabu, J Jerald, A Noorul Haq, and P Asokan. 2009. Multi objective scheduling of jobs, AGVs and AS/RS in FMS using artificial immune system. In Proc. of National conference on Emerging trends in Engineering and Sciences. 229--239.Google ScholarGoogle Scholar
  5. JF Gonçalves and MGC Resende. 2011. Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics 17, 5 (2011), 487--525. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. SM Homayouni and DBMM Fontes. 2017. Integrated Scheduling of Machines, Vehicles, and Storage Tasks in Flexible Manufacturing Systems. In MISTA 2017, Kuala Lumpur, Malaysia. 5--8.Google ScholarGoogle Scholar
  7. N Jawahar, P Aravindan, and SG Ponnambalam. 1998. Optimal random storage allocation for an AS/RS in an FMS. The International Journal of Advanced Manufacturing Technology 14, 2 (1998), 116--132.Google ScholarGoogle ScholarCross RefCross Ref
  8. LAC Roque, DBMM Fontes, and FACC Fontes. 2011. A biased random key genetic algorithm approach for unit commitment problem. In Lecture Notes in Computer Science, Vol. 6630. Springer, 327--339. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A BRKGA for the integrated scheduling problem in FMSs

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
          July 2019
          2161 pages
          ISBN:9781450367486
          DOI:10.1145/3319619

          Copyright © 2019 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 13 July 2019

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate1,669of4,410submissions,38%

          Upcoming Conference

          GECCO '24
          Genetic and Evolutionary Computation Conference
          July 14 - 18, 2024
          Melbourne , VIC , Australia
        • Article Metrics

          • Downloads (Last 12 months)8
          • Downloads (Last 6 weeks)1

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader