abstract

Genetic algorithm with genetic engineering technology for multi-objective dynamic job shop scheduling problems

Authors:

Todor Dimitrov,

Michael BaumannAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 833 - 834

https://doi.org/10.1145/2001858.2002112

Published: 12 July 2011 Publication History

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Abstract

Genetic algorithms were intensively investigated in various modifications and in combinations with other algorithms for solving the NP-hard scheduling problem. This extended abstract describes a genetic algorithm approach for solving large job shop problems that uses hints from the schedule evaluation in the genetic operators. The result is a hybrid genetic algorithm with smaller randomness and more managed search to find better solutions in shorter processing time. The hybridized genetic algorithm was tested with data from wafer production with thousands of jobs and hundreds of machine alternatives. The hybridized genetic algorithm not only achieved smaller tardiness in shorter computation time but was also able to reduce the sequence dependent change-over times between jobs in comparison with the classical genetic algorithm.

References

[1]

Smith, S.F. OPIS: A methodology and architecture for reactive scheduling. In: Zweben M. and Fox M.S. Editors, Intelligent Scheduling, Morgan Kaufmann, San Francisco, CA, 1994, 29--66.

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[2]

Affenzeller, M., Wagner, S., and Braune, R. On the Analysis of Crossover Schemes for Genetic Algorithms Applied to the Job Shop Scheduling Problem. In Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) 2005, Orlando, 2005, 236--241.

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Pereira IMadureira A(2015)Racing based approach for Metaheuristics parameter tuning2015 10th Iberian Conference on Information Systems and Technologies (CISTI)10.1109/CISTI.2015.7170351(1-6)Online publication date: Jun-2015
https://doi.org/10.1109/CISTI.2015.7170351
Mourtzis DDoukas MVlachou AXanthopoulos N(2014)Machine Availability Monitoring for Adaptive Holistic Scheduling: A Conceptual Framework for Mass CustomizationProcedia CIRP10.1016/j.procir.2014.10.05625(406-413)Online publication date: 2014
https://doi.org/10.1016/j.procir.2014.10.056
Ogur EAydin MAlba E(2013)Refining scheduling policies with genetic algorithmsProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482730(1513-1518)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482730
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Index Terms

Genetic algorithm with genetic engineering technology for multi-objective dynamic job shop scheduling problems
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures
        Procedures, functions and subroutines

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Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Pereira IMadureira A(2015)Racing based approach for Metaheuristics parameter tuning2015 10th Iberian Conference on Information Systems and Technologies (CISTI)10.1109/CISTI.2015.7170351(1-6)Online publication date: Jun-2015
https://doi.org/10.1109/CISTI.2015.7170351
Mourtzis DDoukas MVlachou AXanthopoulos N(2014)Machine Availability Monitoring for Adaptive Holistic Scheduling: A Conceptual Framework for Mass CustomizationProcedia CIRP10.1016/j.procir.2014.10.05625(406-413)Online publication date: 2014
https://doi.org/10.1016/j.procir.2014.10.056
Ogur EAydin MAlba E(2013)Refining scheduling policies with genetic algorithmsProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482730(1513-1518)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482730
Perez-Lopez ABaltazar RCarpio MTerashima-Marin HMagaña-Lozano DPuga H(2013)Homogeneous Population Solving the Minimal Perturbation Problem in Dynamic Scheduling of SurgeriesAdvances in Artificial Intelligence and Its Applications10.1007/978-3-642-45114-0_37(473-484)Online publication date: 2013
https://doi.org/10.1007/978-3-642-45114-0_37
Dimitrov TBaumann M(2012)Criteria evaluation considering the current time used by a genetic scheduling algorithm for changeover and tardiness reduction2012 IEEE International Conference on Automation Science and Engineering (CASE)10.1109/CoASE.2012.6386499(425-430)Online publication date: Aug-2012
https://doi.org/10.1109/CoASE.2012.6386499

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Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

A hybrid genetic algorithm for the job shop scheduling problems

A two-stage genetic algorithm for multi-objective job shop scheduling problems

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