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A BRKGA for the integrated scheduling problem in FMSs

Published: 13 July 2019 Publication 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

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JC Bean. 1993. Genetics and random keys for sequencing amd optimization. (1993).
[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.
[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.
[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.
[5]
JF Gonçalves and MGC Resende. 2011. Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics 17, 5 (2011), 487--525.
[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.
[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.
[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.

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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
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. biased random key genetic algorithm
  2. integrated scheduling
  3. storage/retrieval operations

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  • Research-article

Funding Sources

  • EDER/COMPETE2020/POCI/ MCTES/FCT

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GECCO '19
Sponsor:
GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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