Korean Management Science Review (경영과학)

The Korean Operations Research and Management Science Society (한국경영과학회)
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Genetic algorithms with a permutation approach to the parallel machines scheduling problem

  • Han, Yong-Ho (Department of Management Information Systems, Pusan University of Foreign Studies)
  • Published : 1997.11.01
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Abstract

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

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References

  1. OR Spectrum v.17 A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms Bierwirth,C.
  2. Research paper, Dept. of Economics, University of Bremen On Permutation Representations for Scheduling Problems Bierwirth,C.;Mattfeld D.C.;Kopfer,H.
  3. Proceedings of International Conference on Genetic Algorithms Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling Bruns,R.
  4. Handbook of Genetic Algorithms Davis,L.
  5. Proceedings of Interantional Conference on Genetic Algorithms A promising Algorithm Approach to Job-Shop Scheduling Rescheduling, and Open-Shop Scheduling Problems Fang,H.;Ross,P.;Corne,D.
  6. Computer v.27 Genetic-Algorithm Programming Environments Filho,J.L.R.;Treleaven,P.C.;Alippi,C.
  7. Genetic Algorithms in Search, Optimization, and Machine Learning Goldberg,D.E.
  8. Technical Report #95-06-01 An Introduction to GALOPPS (The Genetic ALgorithm OPtimized for Portability and Parallelism System) Release 3.01 Goodman,E.D.
  9. Korean Management Science Review v.12 no.2 Genetic Algorithms for Optimization: A Case Study of Machine-Part Group Formation Problems Han,Y.H.;Ryu,K.R.
  10. IEEE Transactions on Parallel and Distributed Systems v.5 no.2 A Genetic Algorithm for Multiprocessor Scheduling Hou,E.S.H.;Ansari,N.;Ren,H.
  11. Proceedings of International conference on Genetic Algorithms An Efficient Algorithm for Job Shop Schedulings Problems Kobayashi,S.;Ono,I.;Yamamura,M.
  12. Heuristic Scheduling Systems Morton,T.E.;Pentico,D.W.
  13. Research Paper, Automatic Control & Systems Engineering Department, University of Sheffield Initial Study of Multi-Objective Genetic Algorithms for Scheduling the Production of Chilled Ready Meals Shaw,K.J.;Fleming,P.J.
  14. Computer v.27 Genetic Algorithms: A Survey Srinivas,M.;Patnaik,L.M.
  15. Proceedings of International conference on Genetic Algorithms A Parallel Algorithm for Multiobjective Microprocessor Design Problems Stanley,T.J.;Mudge,T.
  16. Computers and Industrial Engineering v.30 no.1 Bicriteria Scheduling Problem for Unrelated Parallel Machines Suresh,V.;Chaudhuri,D.