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Development and Analysis of a Discrete Particle Swarm Optimisation for Bi-criteria Scheduling of a Flow Shop with Sequence-Dependent Setup Time

  • V. AnjanaEmail author
  • R. Sridharan
  • P. N. Ram Kumar
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

Most studies in flow shop scheduling neglect the setup times or consider the setup times along with the processing times. However, in industries that manufacture paint, textiles, ceramic tiles, etc., the setup times are significant and are sequence dependent. This paper addresses the problem of scheduling a flow shop operating in a sequence-dependent setup time (SDST) environment considering the objectives, namely minimisation of makespan and mean tardiness. The evolutionary method of discrete particle swarm optimisation (DPSO) based on weighted approach is developed and applied to SDST benchmark problems of flow shop scheduling. The efficacy of the metaheuristic is compared with that of a hybrid genetic algorithm, and it is observed that on an average, the proposed DPSO provides an improvement of 7.8, 22.3 and 11.3% in the values of mean ideal distance, computational time and diversification matrix, respectively. For most problems, the proposed DPSO performs superior to the hybrid genetic algorithm.

Keywords

Permutation flow shop Sequence-dependent setup time Discrete particle swarm optimisation Hybrid genetic algorithm 

Notes

Acknowledgements

The authors express their sincere thanks to the reviewers for their suggestions which helped in improving the initial version of the paper.

References

  1. 1.
    Ruiz, R., Maroto, C., Alcaraz, J.: Solving the flow shop scheduling problem with sequence dependent setup times using advanced metaheuristic. Eur. J. Oper. Res. 165(1), 34–54 (2005)CrossRefGoogle Scholar
  2. 2.
    Ciavotta, M., Minella, G., Ruiz, R.: Multi-objective sequence dependent setup times permutation flow shop: a new algorithm and comprehensive study. Eur. J. Oper. Res. 227(2), 301–313 (2013)CrossRefGoogle Scholar
  3. 3.
    Vanchipura, R., Sridharan, R.: Development and analysis of constructive heuristic algorithms for flow shop scheduling problems with sequence dependent setup times. Int. J. Adv. Manuf. Technol. 67(5), 1337–1353 (2013)CrossRefGoogle Scholar
  4. 4.
    Roger, Z., Mercado, R., Bard, J.: Computational experience with a branch and cut algorithm for flow shop scheduling with setups. Comput. Oper. Res. 25(5), 351–366 (1998)CrossRefGoogle Scholar
  5. 5.
    Roger, Z., Mercado, R., Bard, J.: A branch and bound algorithm for permutation flow shops with sequence dependent setup times. IIE Trans. 31, 721–731 (1999)Google Scholar
  6. 6.
    Deb, K.: Multi-objective Optimisation Using Evolutionary Algorithms, Student ed. Wiley, Hoboken (2005)Google Scholar
  7. 7.
    Rajendran, C., Ziegler, H.: Scheduling to minimise the sum of weighted flow time and weighted tardiness of jobs in a flow shop with sequence dependent setup time. Eur. J. Oper. Res. 149(3), 513–522 (2003)CrossRefGoogle Scholar
  8. 8.
    Eren, T., Guner, E.: A bi-criteria scheduling with sequence dependent setup time. Appl. Math. Comput. 179(1), 378–385 (2006)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Eren, T.: A multi-criteria flow shop scheduling problem with setup times. J. Mater. Process. Technol. 186(1–3), 60–65 (2007)CrossRefGoogle Scholar
  10. 10.
    Dhingra, A., Chandna, P.: A bi-criteria m-machine sequence dependent setup time flow shop using modified heuristic genetic algorithm. Int. J. Eng. Sci. Technol. 2(5), 216–225 (2010)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Piscataway, NJ (1995)Google Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. IEEE 4104–4108Google Scholar
  13. 13.
    Pan, Q., Tasgetiren, F., Liang, Y.C.: A discrete particle swarm optimisation algorithm for no-wait flow shop scheduling problem. Eur. J. Oper. Res. 35(9), 2807–2839 (2008)zbMATHGoogle Scholar
  14. 14.
    Enscore Jr., E., Ham, I., Nawaz, M.: A heuristic algorithm for the m-machine, n-job flow shop sequencing problem. Omega Int. J. Manage. Sci. 11(1), 91–97 (1983)CrossRefGoogle Scholar
  15. 15.
    Ross, P.J.: Taguchi Techniques for Quality Engineering, 2nd ed. McGraw Hill International Editions (1996)Google Scholar
  16. 16.
    Kaladhar, M., Subbaiah, K.V., Rao, S., Rao, K.N.: Application of Taguchi approach and utility concept in solving the multi-objective problem when turning AISI 202 Austenitic Stainless Steel. J. Eng. Sci. Technol. Rev. 4(1), 55–61 (2011)CrossRefGoogle Scholar
  17. 17.
    Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentNational Institute of Technology CalicutCalicutIndia
  2. 2.Indian Institute of Management KozhikodeCalicutIndia

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