A multiobjective evolutionary algorithm for the process planning of flexible manufacturing systems

유연제조시스템의 공정계획을 위한 다목적 진화알고리듬

  • 김여근 (전남대학교 산업공학과) ;
  • 신경석 (전남대학교 고품질전기전자부품 및 시스템 연구센터) ;
  • 김재윤 (전남대학교 고품질전기전자부품 및 시스템 연구센터)
  • Published : 2004.06.01

Abstract

This paper deals with the process planning of flexible manufacturing systems (FMS) with various flexibilities and multiple objectives. The consideration of the manufacturing flexibility is crucial for the efficient utilization of FMS. The machine, tool, sequence, and process flexibilities are considered In this research. The flexibilities cause to increase the Problem complexity. To solve the process planning problem, an this paper an evolutionary algorithm is used as a methodology. The algorithm is named multiobjective competitive evolutionary algorithm (MOCEA), which is developed in this research. The feature of MOCEA is the incorporation of competitive coevolution in the existing multiobjective evolutionary algorithm. In MOCEA competitive coevolution plays a role to encourage population diversity. This results in the improvement of solution quality and, that is, leads to find diverse and good solutions. Good solutions means near or true Pareto optimal solutions. To verify the Performance of MOCEA, the extensive experiments are performed with various test-bed problems that have distinct levels of variations in the four kinds of flexibilities. The experiments reveal that MOCEA is a promising approach to the multiobjective process planning of FMS.

Keywords

References

  1. International Journal of Production Research v.33 A hierarchical bicriterion approach to integrated process plan selection and job scheduling Brandimarte,P.;M.Calderini https://doi.org/10.1080/00207549508930142
  2. Knowledge and Information Sytems v.1 no.3 A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques Coello,C.A.C. https://doi.org/10.1007/BF03325101
  3. Evolutionary Coputation v.7 no.3 Multi-objective Genetic Algorithms : Problem Diffiulties and Construction of Test Problems Deb,K. https://doi.org/10.1162/evco.1999.7.3.205
  4. Parallel Problem Solving from Nature PPSN Ⅵ A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi- Objective Optimization : NSGA-Ⅱ Deb,K.S.;M.S.(et al.)(Ed.)
  5. Genetic Algorithms : Proceeding of the Fifth International Conference Genetic algorithm for multiobjective optimization, formulation, discussion and generalization Fonseca,C.M.;P.J.Fleming;Forrest,S.(ed.)
  6. Genetic Algorithms in Search, Optimization, and Machine Learning Goldberg,D.E.
  7. International Journal of Production Research v.34 Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities Ho,Y.C.;C.L.Moodie https://doi.org/10.1080/00207549608905065
  8. International Journal of Production Research v.37 Machine loading and part type selection in flexible manufacturing systems Guerrero,F.;S.Lozano;T.Koltai;J.Larraneta https://doi.org/10.1080/002075499191265
  9. IEEE international Conference on Evolytionary Computation v.1 A niched Pareto genetic algorithm for multi objective optimization Horn,J.;N.Nafpliotis;D.E.Goldberg
  10. Computers & Operations Research v.25 A genetic algorithm for multiple objective sequencing problems in mixed model assembly Hyun,C.J.;Y.H.Kim;Y.K.Kim
  11. Evolutionary Multi-Criterion Optimization, Second International Conference, EMO 2003 Performance Scaling of Multi-objective Evolutionary Algorithms Khare,V.;X.Yao;K.Deb;Carlos M. Fonseca(ed.);Peter J. Fleming(ed.);Eckart Zitzler(ed.);Kalyanmoy Deb(ed.);Lothar Thiele(ed.)
  12. Lecture Notes in Computer Scienc. v.2632
  13. Journal of Heuristics v.9 no.Issue3 Tournament Competition and its Merits for Coevolutionary Algorithms Kim,J.Y.;Kim,Y.K.;Kim,Y.H. https://doi.org/10.1023/A:1023769324585
  14. A set of data for the integration of process planning and scheduling in FMS Kim,Y.K.
  15. Computers & Operations Research v.30 A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling Kim,Y.K.;K.T.Park;J.S.Ko https://doi.org/10.1016/S0305-0548(02)00063-1
  16. IEEE International Conference on Computation The Pareto archived evolution strategy : A new baseline algorithm for multi-objective optimization Knowles,J.D.;D.W.Corne
  17. International Journal of Production Research v.38 A genection algorithm for FMS part type selection and machine loading Kumar,N.;K.Shanker https://doi.org/10.1080/00207540050176058
  18. Genetic Algorithms+Data Structures=Evolution Programs(Second, Extended Edition) Michalewics,Z.
  19. International Journal of Production Research v.33 Models and solution approaches for part movement minimization and load and process plan flexibilities Modi,B.K.;K.Shanker https://doi.org/10.1080/00207549508904782
  20. International Journal of Production Research v.36 Part type selection, machine loading and part type volume determination problem in FMS planning Nayak,G.K.;D.Acharya https://doi.org/10.1080/002075498192977
  21. Parallel Problem Solving from Nature PPSN Ⅴ Selective breeding in a multiobjective genetic algorithm Parks,G.T.;I.Miller;A.E.E.(et al.)(Ed.)
  22. proceedings of the Fifth IEEE Conference on Evolutionary Computation On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set Rudolph,G.
  23. Genetic Algorithms and their Applications : Proceedings of the First International Conference on Genetic Algorithms Multiple Objective Optimiation with Vector Evaluated Genetic Algorithms Schaffer,J.D.
  24. Evolutionary Computation v.2 no.3 Multiobjective optimization using nondominated sorting in genetic algorithms Srinivas,N.;K.Deb https://doi.org/10.1162/evco.1994.2.3.221
  25. Management sciences v.29 Formulation and solution of nonlinear integer production planning problem for flexible manufacturing systems Stecke,K.E. https://doi.org/10.1287/mnsc.29.3.273
  26. IEEE Transactions on Engineering Management v.42 FMS plannig decisions, operating flexibilities and system performance Stecke,K.E.;N.Raman https://doi.org/10.1109/17.366408
  27. Artificial Intelligence Review v.17 Evolutionary Algorithms for Multi-Objective Optimization : Performance Assessments and Comparison Tan,K.C.;T.H.LEE;E.F.Khor
  28. International Journal of Production Research v.38 Solving machine loading problems in flexible manufacturing system using a genetic algorithm based heuristic approach Tiwari,M.K.;N.K.Vidyarthi https://doi.org/10.1080/002075400418298
  29. Genetic Programming 1998 : Proceedings of the Third Annual Conference Evolutionary computation and convergence to a parato front Veldhuizen,D.A.V.;G.B.Lamont;J.R.Koza(ed.);W.Banzhaf(ed.);K.Chellapilla(ed.);K.Deb(ed.);M.Dorigo(ed.);D.B.Fogel(ed.);M.H.Garzon(ed.);D.E.Goldgerg(ed.);H.Iba(ed.);R.Riolo(ed.)
  30. Evolutionary Computation v.8 no.2 Multiobjective evolutionary algorithms : Analyzing the state-of-the-art Veldhuizen,D.A.V.;G.B.Lamont https://doi.org/10.1162/106365600568158
  31. Evolutionary Computation v.8 no.2 Comparison of multiobjective evolutionary algorithms : Empirical results Zitzler,E.;K.Deb;L.Thiele https://doi.org/10.1162/106365600568202
  32. IEEE Transactions on Evolutionary Computation v.3 no.4 Mutlobjective evolutionary algorithms : A omparative case study and the strength Pareto approach Zitzler,E.;L.Thiele https://doi.org/10.1109/4235.797969
  33. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology(ETH) Zurich SPEZ2 : Improving the Stength Parto evolutionary Algorithm Zitzler,E.;M.Laumanns;L.Thiele
  34. Evolutionary Computation v.11 no.2 Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms Toffolo,A.;E.Benini https://doi.org/10.1162/106365603766646816