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Design and Analysis of Evolutionary Algorithms for the No-wait Flow-shop Scheduling Problem

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Metaheuristics in the Service Industry

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 624))

Abstract

Evolutionary algorithms are generally based on populations of solutions which are subject to the application of operators such as recombination, mutation, and selection in order to evolve the population and eventually obtain high-quality solutions. Different, yet often similar evolutionary algorithms are discussed in various research communities from different perspectives. In this work we strive for a better understanding of the performance of different designs within the general framework of evolutionary computation. We examine and compare (discrete) particle swarm optimization with classic genetic algorithms, both with and without hybridization with local search. In particular, we analyze the effect of different selection and reproduction mechanisms on solution quality, population diversity, and convergence behavior, and examine approaches for maintaining population diversity. As application we consider an NP-hard combinatorial optimization problem, namely the no-wait (continuous) flow-shop scheduling problem with flow-time criterion. The computational results support the importance of local search within (hybridized) evolutionary algorithms and show how solution quality depends on a reasonable design of crossover operators, distance functions, population diversity measures, and the control of population diversity.

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References

  1. Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Oper. Res. 54(1), 99–114 (2006). doi: 10.1287/opre.1050.0243

    Article  Google Scholar 

  2. Adiri, I., Pohoryles, D.: Flowshop/no-idle or no-wait scheduling to minimize the sum of completion times. Nav. Res. Logist. Q. 29, 495–504 (1982)

    Article  Google Scholar 

  3. Aldowaisan, T., Allahverdi, A.: New heuristics for m-machine no-wait flowshop to minimize total completion time. Omega 32(5), 345–352 (2004)

    Article  Google Scholar 

  4. Allahverdi, A., Al-Anzi, F.S.: A PSO and a tabu search heuristic for the assembly scheduling problem of the two-stage distributed database application. Comput. Oper. Res. 33, 1056–1080 (2006)

    Article  Google Scholar 

  5. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Evolutionary Computation 1: Basic Algorithms and Operators. Taylor and Francis, New York (2000)

    Google Scholar 

  6. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Evolutionary Computation 2: Advanced Algorithms and Operators. Institute of Physics Publishing, Bristol (2000)

    Google Scholar 

  7. Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6(2), 154–160 (1994)

    Google Scholar 

  8. Bertolissi, E.: Heuristic algorithm for scheduling in the no-wait flow-shop. J. Mater. Process. Technol. 107, 459–465 (2000)

    Article  Google Scholar 

  9. Bianco, L., Mingozzi, A., Ricciardelli, S.: The traveling salesman problem with cumulative costs. Networks 23(2), 81–91 (1993)

    Article  Google Scholar 

  10. Chen, C.L., Neppalli, R.V., Aljaber, N.: Genetic algorithms applied to the continuous flow shop problem. Comput. Ind. Eng. 30(4), 919–929 (1996)

    Article  Google Scholar 

  11. Clerc, M.: The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation vol. 3, pp. 1951–1957 (1999)

    Google Scholar 

  12. Czogalla, J., Fink, A.: Evolutionary computation for the continuous flow-shop scheduling problem. In: Geiger, M.J., Habenicht, W. (eds.) Proceedings of EU/ME 2007: Metaheuristics in the Service Industry, pp. 37–45 (2007)

    Google Scholar 

  13. Czogalla, J., Fink, A.: Fitness landscape analysis for the continuous flow-shop scheduling problem. In: Third European Graduate Student Workshop on Evolutionary Computation pp. 1–14 (2008)

    Google Scholar 

  14. Czogalla, J., Fink, A.: 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, pp. 61–90. Springer, Berlin (2008)

    Chapter  Google Scholar 

  15. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  16. van Deman, J.M., Baker, K.R.: Minimizing mean flowtime in the flow shop with no intermediate queues. AIIE Trans. 6, 28–34 (1974)

    Google Scholar 

  17. Fink, A., Voß, S.: Solving the continuous flow-shop scheduling problem by metaheuristics. Eur. J. Oper. Res. 151, 400–414 (2003)

    Article  Google Scholar 

  18. Fischetti, M., Laporte, G., Martello, S.: The delivery man problem and cumulative matroids. Oper. Res. 41, 1055–1076 (1993)

    Article  Google Scholar 

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

    Google Scholar 

  20. Glover, F.: A template for scatter search and path relinking. In: Hao, J.K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) Lecture Notes in Computer Science, vol. 1363, pp. 13–54. Springer, Berlin (1997)

    Google Scholar 

  21. Glover, F., Laguna, M., Marti, R.: Scatter search. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 519–538. Springer, Berlin (2003)

    Google Scholar 

  22. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)

    Google Scholar 

  23. Gouveia, L., Voß, S.: A classification of formulations for the (time-dependent) traveling salesman problem. Eur. J. Oper. Res. 83, 69–82 (1995)

    Article  Google Scholar 

  24. Greistorfer, P., Voß, S.: Controlled pool maintenance for metaheuristics. In: Rego, C., Alidaee, B. (eds.) Metaheuristic Optimization Via Memory and Evolution: Tabu Search and Scatter Search, pp. 387–424. Kluwer, Boston, MA (2005)

    Chapter  Google Scholar 

  25. Gupta, J.N.D.: Optimal flowshop with no intermediate storage space. Nav. Res. Logist. Q. 23, 235–243 (1976)

    Article  Google Scholar 

  26. Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  27. Howell, D.C.: Statistical Methods for Psychology, 5th edn. Duxbury, Pacific Grove, CA (2002)

    Google Scholar 

  28. Hutter, F., Hoos, H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, pp. 1147–1152. AAAI, Menlo Park, CA (2007)

    Google Scholar 

  29. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst. Man Cybern. B Cybern. 35(6), 1272–1282 (2005)

    Article  Google Scholar 

  30. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  31. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco, CA (2001)

    Google Scholar 

  32. Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1671–1676 (2002)

    Google Scholar 

  33. Krasnogor, N., Smith, J.E.: A tutorial for competent memetic algorithms model, taxonomy, and design issues. IEEE Trans. Evolution. Comput. 9(5), 474–488 (2005)

    Article  Google Scholar 

  34. Kumar, A., Prakash, A., Shankar, R., Tiwari, M.: Psycho-clonal algorithm based approach to solve continuous flow shop scheduling problem. Expert Syst. Appl. 31(3), 504–514 (2006)

    Article  Google Scholar 

  35. Liu, B., Wang, L., Jin, Y.H.: An effective hybrid particle swarm optimization for no-wait flow shop scheduling. Int. J. Adv. Manufact. Technol. 31(9–10), 1001–1011 (2007)

    Article  Google Scholar 

  36. Liu, B., Wang, L., Jin, Y.H.: An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans. Syst. Man Cybern. B Cybern. 37(1), 18–27 (2007)

    Article  Google Scholar 

  37. Lucena, A.: Time-dependent traveling salesman problem-the deliveryman case. Networks 20(6), 753–763 (1990). doi: 10.1002/net.3230200605

    Article  Google Scholar 

  38. Martí, R., Laguna, M., Campos, V.: Scatter search vs. genetic algorithms. An experimental evaluation with permutation problems. In: Rego, C., Alidaee, B. (eds.) Metaheuristic Optimization Via Memory and Evolution. Tabu Search and Scatter Search, Operations Research/Computer Science Interfaces Series, pp. 263–282. Kluwer, Boston, MA (2005)

    Google Scholar 

  39. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Trans. Evolution. Comput. 8(3), 204–210 (2004)

    Article  Google Scholar 

  40. Merkle, D., Middendorf, M.: Swarm intelligence. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pp. 401–435. Springer, New York (2005)

    Google Scholar 

  41. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1996)

    Google Scholar 

  42. Moraglio, A., Di Chio, C., Poli, R.: Geometric particle swarm optimisation. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L. Esparcia-Alcázar, A.I. (eds.) Proceedings of the 10th European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 4445, pp. 125–136. Springer, Berlin (2007). doi: 10.1007/978-3-540-71605-1_12

    Google Scholar 

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

    Google Scholar 

  44. Pan, Q.K., Tasgetiren, M.F., Liang, Y.C.: 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, pp. 3281–3288 (2006)

    Google Scholar 

  45. Pan, Q.K., Tasgetiren, M.F., Liang, Y.C.: A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput. Oper. Res. 35(9), 2807–2839 (2008). doi:10.1016/j.cor.2006.12.030

    Article  Google Scholar 

  46. Papadimitriou, C.H., Kanellakis, P.C.: Flowshop scheduling with limited temporary storage. J. ACM 27, 533–549 (1980)

    Article  Google Scholar 

  47. Parsopoulos, K.E., Vrahatis, M.N.: Studying the performance of unified particle swarm optimization on the single machine total weighted tardiness problem. In: Sattar, A., Kang, B.H. (eds.) AI 2006: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, vol. 4304, pp. 760–769. Springer, Berlin (2006)

    Google Scholar 

  48. Peram, T., Veeramachaneni, K., Chilukuri, K.M.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 174–181 (2003)

    Google Scholar 

  49. Picard, J.C., Queyranne, M.: The time-dependent traveling salesman problem and its application to the tardiness problem in one-machine scheduling. Oper. Res. 26, 86–110 (1978)

    Article  Google Scholar 

  50. Rajendran, C., Chaudhuri, D.: Heuristic algorithms for continuous flow-shop problem. Nav. Res. Logist. Q. 37, 695–705 (1990)

    Article  Google Scholar 

  51. Reeves, C.R.: Landscapes, operators and heuristic search. Ann. Oper. Res. 86, 473–490 (1999)

    Article  Google Scholar 

  52. Ronald, S.: More distance functions for order-based encodings. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 558–563 (1998)

    Google Scholar 

  53. Ruiz, R., Maroto, C., Alcaraz, J.: Two new robust genetic algorithms for the flowshop scheduling problem. Omega 34(5), 461–476 (2006). doi: 10.1016/j.omega.2004.12.006

    Article  Google Scholar 

  54. Sahni, S., Gonzales, T.: P-complete approximation problems. J. Assoc. Comput. Mach. 23, 555–565 (1976)

    Google Scholar 

  55. Sastry, K., Goldberg, D., Kendall, G.: Genetic algorithms. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pp. 96–125. Springer, New York (2005)

    Google Scholar 

  56. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) Evolutionary Programming VII: Proceedings of the 7th International Conference of Evolutionary Programming. Lecture Notes in Computer Science, vol. 1447, pp. 591–600. Springer, London, (1998). http://www.engr.iupui.edu/shi/PSO/Paper/EP98/psof6/ep98_pso.html

  57. Sörensen, K., Sevaux, M.: MA|PM: Memetic algorithms with population managment. Comput. Oper. Res. 33(5), 1214–1225 (2006)

    Article  Google Scholar 

  58. Szwarc, W.: A note on the flow-shop problem without interruptions in job processing. Nav. Res. Logist. Q. 28, 665–669 (1981)

    Article  Google Scholar 

  59. Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 278–285 (1993)

    Article  Google Scholar 

  60. Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur. J. Oper. Res. 177(3), 1930–1947 (2007)

    Article  Google Scholar 

  61. Tasgetiren, M.F., Sevkli, M., Liang, Y.C., Gencyilmaz, G.: Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 2, pp. 1412–1419 (2004). doi: 10.1109/CEC.2004.1331062

    Google Scholar 

  62. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform. Process. Lett. 85, 317–325 (2003)

    Article  Google Scholar 

  63. van der Veen, J.A.A., van Dal, R.: Solvable cases of the no-wait flow-shop scheduling problem. J. Oper. Res. Soc. 42(11), 971–980 (1991)

    Article  Google Scholar 

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

    Jens Czogalla & Andreas Fink

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  1. Jens Czogalla
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    Czogalla, J., Fink, A. (2009). Design and Analysis of Evolutionary Algorithms for the No-wait Flow-shop Scheduling Problem. In: Sörensen, K., Sevaux, M., Habenicht, W., Geiger, M. (eds) Metaheuristics in the Service Industry. Lecture Notes in Economics and Mathematical Systems, vol 624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00939-6_7

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