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Metaheuristics for Scheduling in Industrial and Manufacturing Applications pp 61–89Cite as

On the Effectiveness of Particle Swarm Optimization and Variable Neighborhood Descent for the Continuous Flow-Shop Scheduling Problem

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 128))

Summary

Recently population-based meta-heuristics under the cover of swarm intelligence have gained prominence. This includes particle swarm optimization (PSO), where the search strategy draws ideas from the social behavior of organisms. While PSO has been reported as an effective search method in several papers, we are interested in the critical success factors of PSO for solving combinatorial optimization problems. In particular, we examine the application of PSO with different crossover operators and hybridization with variable neighborhood descent as an embedded local search procedure. Computational results are reported for the continuous (nowait) flow-shop scheduling problem. The findings demonstrate the importance of local search as an element of the applied PSO procedures. We report new best solutions for a number of problem instances from the literature.

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References

  1. I. Adiri and D. Pohoryles. Flowshop/no-idle or no-wait scheduling to minimize the sum of completion times. Naval Research Logistics Quarterly, 29:495–504, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  2. A. Allahverdi and F. S. Al-Anzi. A PSO and a tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Computers & Operations Research, 33:1056–1080, 2006.

    Article  MATH  Google Scholar 

  3. D. Anghinolfi and M. Paolucci. A new discrete particle swarm optimization approach for the total tardiness scheduling scheduling problem with sequence-dependent setup times. Working Paper, 2007.

    Google Scholar 

  4. T. Bäck, D. B. Fogel, and Z. Michalewicz. Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics Publishing, Bristol, 2000.

    MATH  Google Scholar 

  5. J. C. Bean. Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6:154–160, 1994.

    MATH  Google Scholar 

  6. E. Bertolissi. Heuristic algorithm for scheduling in the no-wait flow-shop. Journal of Materials Processing Technology, 107:459–465, 2000.

    Article  Google Scholar 

  7. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York, NY, 1999.

    MATH  Google Scholar 

  8. C.-L. Chen, R. V. Neppalli, and N. Aljaber. Genetic algorithms applied to the continuous flow shop problem. Computers & Industrial Engineering, 30:919–929, 1996.

    Article  Google Scholar 

  9. M. Clerc. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, volume 3, pages 1951–1957, 1999.

    Article  Google Scholar 

  10. P. Cowling and W. Rezig. Integration of continuous caster and hot strip mill planning for steel production. Journal of Scheduling, 3:185–208, 2000.

    Article  MATH  Google Scholar 

  11. L. Davis. Applying adaptive algorithms to epistatic domains. Proceedings of the 9th International Joint Conference on Artificial Intelligence, 1:162–164, 1985.

    Google Scholar 

  12. J. E. Day and M. P. Hottenstein. Review of sequencing research. Naval Research Logistics Quarterly, 17:11–39, 1970.

    Article  MATH  Google Scholar 

  13. R. A. Dudek, S. S. Panwalkar, and M. L. Smith. The lessons of flowshop scheduling research. Operations Research, 40:7–13, 1992.

    Article  MATH  Google Scholar 

  14. R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Sixth International Symposium on Micro Machine and Human Science, pages 39–43, 1995.

    Google Scholar 

  15. A. Fink and S. Voß. Solving the continuous flow-shop scheduling problem by meta-heuristics. European Journal of Operational Research, 151:400–414, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  16. J. Gimmler, T. Stützle, and T. E. Exner. Hybrid particle swarm optimization: An examination of the influence of iterative improvement algorithms on performance. In M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, and T. Stützle, editors, Ant Colony Optimization and Swarm Intelligence, 5th International Workshop ANTS 2006, volume 4150 of Lecture Notes in Computer Science, pages 436–443. Springer, Berlin, 2006.

    Chapter  Google Scholar 

  17. F. Glover, M. Laguna, and R. Marti. Scatter search. In A. Ghosh and S. Tsutsui, editors, Advances in Evolutionary Computing: Theory and Applications, pages 519–538. Springer, Berlin, 2003.

    Google Scholar 

  18. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989.

    MATH  Google Scholar 

  19. D. E. Goldberg and R. Lingle. Alleles, loci, and the traveling salesman problem. In J. Grefenstette, editor, Proceedings of the International Conference on Genetic Algorithms and Their Applications, pages 154–159. Lawrence Erlbaum Associates, Hillsdale, NJ, 1985.

    Google Scholar 

  20. J. Grabowski and J. Pempera. Sequencing of jobs in some production system. European Journal of Operational Research, 125:535–550, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  21. J. N. D. Gupta. Optimal flowshop with no intermediate storage space. Naval Research Logistics Quarterly, 23:235–243, 1976.

    Article  MATH  MathSciNet  Google Scholar 

  22. N. G. Hall and C. Sriskandarajah. A survey of machine scheduling problems with blocking and no-wait in process. Operations Research, 44:510–525, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  23. P. Hansen and N. Mladenović. Variable neighborhood search. In E. K. Burke and G. Kendall, editors, Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pages 211–238. Springer, New York, NY, 2005.

    Google Scholar 

  24. P. Hansen, N. Mladenovic, and D. Perez-Brito. Variable neighborhood decomposition search. Journal of Heuristics, 7:335–350, 2001.

    Article  MATH  Google Scholar 

  25. W. E. Hart, N. Krasnogor, and J. E. Smith. Memetic evolutionary algorithms. In W. E. Hart, N. Krasnogor, and J. E. Smith, editors, Recent Advances in Memetic Algorithms, volume 166 of Studies in Fuzziness and Soft Computing, pages 3–30. Springer, Berlin, 2005.

    Chapter  Google Scholar 

  26. H. H. Hoos and T. Stützle. Stochastic Local Search: Foundations and Applications. Elsevier, Amsterdam, 2005.

    MATH  Google Scholar 

  27. S. Janson and M. Middendorf. A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, 35:1272–1282, 2005.

    Article  Google Scholar 

  28. S. M. Johnson. Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1:61–68, 1954.

    Article  Google Scholar 

  29. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, pages 1942–1948, 1995.

    Google Scholar 

  30. J. Kennedy and R. C. Eberhart. A discrete binary version of the particle swarm algorithm. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, volume 5, pages 4104–4108, 1997.

    Google Scholar 

  31. J. Kennedy and R. C. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, 2001.

    Google Scholar 

  32. J. Kennedy and R. Mendes. Population structure and particle swarm performance. Proceedings of the 2002 Congress on Evolutionary Computation, volume 2, pages 1671–1676, 2002.

    Google Scholar 

  33. B. Liu, L. Wang, and Y.-H. Jin. An effective hybrid particle swarm optimization for no-wait flow shop scheduling. The International Journal of Advanced Manufacturing Technology, 31:1001–1011, 2007.

    Article  Google Scholar 

  34. B. Liu, L. Wang, and Y.-H. Jin. An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transaction on Systems, Man, and Cybernetics. Part B: Cybernetics, 37:18–27, 2007.

    Article  Google Scholar 

  35. H. R. Lourenço, O. C. Martin, and T. Stützle. Iterated local search. In F. Glover and G. A. Kochenberger, editors, Handbook on Meta-heuristics, pages 321–353. Kluwer Academic Publishers, Norwell, MA, 2002.

    Google Scholar 

  36. R. Mendes, J. Kennedy, and J. Neves. The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation, 8: 204–210, 2004.

    Article  Google Scholar 

  37. D. Merkle and M. Middendorf. Swarm intelligence. In E. K. Burke and G. Kendall, editors, Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pages 401–435. Springer, New York, NY, 2005.

    Google Scholar 

  38. A. Moraglio, C. Di Chio, and R. Poli. Geometric particle swarm optimisation. In M. Ebner, M. O’Neill, A. Ekárt, L. Vanneschi, and A. I. Esparcia-Alcázar, editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 125–136. Springer, Berlin, 2007.

    Google Scholar 

  39. A. Moraglio and R. Poli. Topological crossover for the permutation representation. In F. Rothlauf et al., editors, Genetic and Evolutionary Computation Conference, GECCO 2005 Workshop Proceedings, 2005, pages 332–338. ACM Press, New York, NY, 2005.

    Chapter  Google Scholar 

  40. P. Moscato. On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report Report 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, 1989.

    Google Scholar 

  41. M. Nawaz, E. E. Enscore, jr., and I. Ham. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11:91–95, 1983.

    Article  Google Scholar 

  42. Q.-K. Pan, M. F. Tasgetiren, and Y.-C. Liang. A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Working Paper, 2005.

    Google Scholar 

  43. Q.-K. Pan, M. Tasgetiren, and Y.-C. Liang. A discrete particle swarm optimization algorithm for single machine total earliness and tardiness problem with a common due date. In IEEE Congress on Evolutionary Computation 2006, pages 3281–3288, 2006.

    Google Scholar 

  44. C. H. Papadimitriou and P. C. Kanellakis. Flowshop scheduling with limited temporary storage. Journal of the ACM, 27:533–549, 1980.

    Article  MATH  MathSciNet  Google Scholar 

  45. K. E. Parsopoulos and M. N. Vrahatis. Studying the performance of unified particle swarm optimization on the single machine total weighted tardiness problem. In A. Sattar and B.-H. Kang, editors, AI 2006: Advances in Artificial Intelligence, volume 4304 of Lecture Notes in Artificial Intelligence, pages 760–769. Springer, Berlin, 2006.

    Chapter  Google Scholar 

  46. T. Peram, K. Veeramachaneni, and K. M. Chilukuri. Fitness-distance-ratio based particle swarm optimization. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pages 174–181, 2003.

    Google Scholar 

  47. J.-C. Picard and M. Queyranne. The time-dependent traveling salesman problem and its application to the tardiness problem in one-machine scheduling. Operations Research, 26:86–110, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  48. W. Raaymakers and J. Hoogeveen. Scheduling multipurpose batch process industries with no-wait restrictions by simulated annealing. European Journal of Operational Research, 126:131–151, 2000.

    Article  MATH  Google Scholar 

  49. C. Rajendran. A no-wait flowshop scheduling heuristic to minimize makespan. Journal of the Operational Research Society, 45:472–478, 1994.

    Article  MATH  Google Scholar 

  50. C. Rajendran and D. Chauduri. Heuristic algorithms for continuous flow-shop problem. Naval Research Logistics Quarterly, 37:695–705, 1990.

    Article  MATH  Google Scholar 

  51. C. R. Reeves. Fitness landscapes. In E. K. Burke and G. Kendall, editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pages 587–610. Springer, Berlin, 2005.

    Google Scholar 

  52. A. Reisman, A. Kumar, and J. Motwani. Flowshop scheduling/sequencing research: A statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44:316–329, 1997.

    Article  Google Scholar 

  53. R. Ruiz and C. Maroto. A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165:479–494, 2005.

    Article  MATH  MathSciNet  Google Scholar 

  54. K. Sastry, D. Goldberg, and G. Kendall. Genetic algorithms. In E. K. Burke and G. Kendall, editors, Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pages 96–125. Springer, New York, NY, 2005.

    Google Scholar 

  55. Y. Shi and R. C. Eberhart. Parameter selection in particle swarm optimization. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming VII: Proceedings of the 7th International Conference of Evolutionary Programming, volume 1447 of Lecture Notes in Computer Science, pages 591–600. Springer, London, 1998.

    Google Scholar 

  56. W. Szwarc. A note on the flow-shop problem without interruptions in job processing. Naval Research Logistics Quarterly, 28:665–669, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  57. E. Taillard. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64:278–285, 1993.

    Article  MATH  Google Scholar 

  58. E. D. Taillard, L. M. Gambardella, M. Gendreau, and J.-Y. Potvin. Adaptive memory programming: A unified view of meta-heuristics. European Journal of Operational Research, 135:1–16, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  59. L. Tang, J. Liu, A. Rong, and Z. Yang. A review of planning and scheduling systems and methods for integrated steel production. European Journal of Operational Research, 133:1–20, 2001.

    Article  MATH  Google Scholar 

  60. M. Tasgetiren, M. Sevkli, Y.-C. Liang, and G. Gencyilmaz. Particle swarm optimization algorithm for single machine total weighted tardiness problem. In Proceedings of the 2004 Congress on Evolutionary Computation, volume 2, pages 1412–1419, 2004.

    Article  Google Scholar 

  61. M. F. Tasgetiren, Y.-C. Liang, M. Sevkli, and G. Gencyilmaz. A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, 177:1930–1947, 2007.

    Article  MATH  Google Scholar 

  62. I. C. Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85:317–325, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  63. J. M. van Deman and K. R. Baker. Minimizing mean flowtime in the flow shop with no intermediate queues. AIIE Transactions, 6:28–34, 1974.

    Google Scholar 

  64. J. A. A. van der Veen and R. van Dal. Solvable cases of the no-wait flow-shop scheduling problem. Journal of the Operational Research Society, 42:971–980, 1991.

    MATH  Google Scholar 

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Authors and Affiliations

  1. Helmut-Schmidt-University / UniBw Hamburg, Holstenhofweg 85, 22043, Hamburg, Germany

    Jens Czogalla & Andreas Fink

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  1. Jens Czogalla
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  2. Andreas Fink
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Editors and Affiliations

  1. Department of Languages and Informatics Systems, Polytechnic University of Catalonia, Campus Nord, Ed. Omega C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

  2. Norwegian Center of Excellence Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, NO-7491, Trondheim, Norway

    Ajith Abraham

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Czogalla, J., Fink, A. (2008). On the Effectiveness of Particle Swarm Optimization and Variable Neighborhood Descent for the Continuous Flow-Shop Scheduling Problem. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Studies in Computational Intelligence, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78985-7_3

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  • DOI: https://doi.org/10.1007/978-3-540-78985-7_3

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