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Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study

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  • Ciavotta, Michele
  • Minella, Gerardo
  • Ruiz, Rubén

Abstract

The permutation flowshop scheduling problem has been thoroughly studied in recent decades, both from single objective as well as from multi-objective perspectives. To the best of our knowledge, little has been done regarding the multi-objective flowshop with Pareto approach when sequence dependent setup times are considered. As setup times and multi-criteria problems are important in industry, we must focus on this area. We propose a simple, yet powerful algorithm for the sequence dependent setup times flowshop problem with several criteria. The presented method is referred to as Restarted Iterated Pareto Greedy or RIPG and is compared against the best performing approaches from the relevant literature. Comprehensive computational and statistical analyses are carried out in order to demonstrate that the proposed RIPG method clearly outperforms all other algorithms and, as a consequence, it is a state-of-art method for this important and practical scheduling problem.

Suggested Citation

  • Ciavotta, Michele & Minella, Gerardo & Ruiz, Rubén, 2013. "Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study," European Journal of Operational Research, Elsevier, vol. 227(2), pages 301-313.
    • Handle: RePEc:eee:ejores:v:227:y:2013:i:2:p:301-313
    DOI: 10.1016/j.ejor.2012.12.031
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    1. ,, 2001. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1157-1160, December.
    2. J E C Arroyo & V A Armentano, 2004. "A partial enumeration heuristic for multi-objective flowshop scheduling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 1000-1007, September.
    3. Framinan, Jose M. & Leisten, Rainer & Ruiz-Usano, Rafael, 2002. "Efficient heuristics for flowshop sequencing with the objectives of makespan and flowtime minimisation," European Journal of Operational Research, Elsevier, vol. 141(3), pages 559-569, September.
    4. Ruiz, Ruben & Stutzle, Thomas, 2008. "An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1143-1159, June.
    5. Rajendran, Chandrasekharan & Ziegler, Hans, 2003. "Scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 149(3), pages 513-522, September.
    6. Varadharajan, T.K. & Rajendran, Chandrasekharan, 2005. "A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs," European Journal of Operational Research, Elsevier, vol. 167(3), pages 772-795, December.
    7. Roger Z. Ríos-Mercado & Jonathan F. Bard, 2003. "The Flow Shop Scheduling Polyhedron with Setup Times," Journal of Combinatorial Optimization, Springer, vol. 7(3), pages 291-318, September.
    8. Geiger, Martin Josef, 2007. "On operators and search space topology in multi-objective flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 181(1), pages 195-206, August.
    9. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    10. Simons, JV, 1992. "Heuristics in flow shop scheduling with sequence dependent setup times," Omega, Elsevier, vol. 20(2), pages 215-225, March.
    11. Ruiz, Ruben & Maroto, Concepcion & Alcaraz, Javier, 2005. "Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics," European Journal of Operational Research, Elsevier, vol. 165(1), pages 34-54, August.
    12. ,, 2001. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 17(5), pages 1025-1031, October.
    13. Taillard, E., 1990. "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 47(1), pages 65-74, July.
    14. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    15. Rajendran, Chandrasekharan, 1995. "Heuristics for scheduling in flowshop with multiple objectives," European Journal of Operational Research, Elsevier, vol. 82(3), pages 540-555, May.
    16. K-C Ying, 2009. "An iterated greedy heuristic for multistage hybrid flowshop scheduling problems with multiprocessor tasks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 810-817, June.
    17. Rajendran, Chandrasekharan & Ziegler, Hans, 1997. "An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs," European Journal of Operational Research, Elsevier, vol. 103(1), pages 129-138, November.
    18. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    19. Parthasarathy, Srinivasaraghavan & Rajendran, Chandrasekharan, 1997. "An experimental evaluation of heuristics for scheduling in a real-life flowshop with sequence-dependent setup times of jobs," International Journal of Production Economics, Elsevier, vol. 49(3), pages 255-263, May.
    20. Arroyo, Jose Elias Claudio & Armentano, Vinicius Amaral, 2005. "Genetic local search for multi-objective flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 167(3), pages 717-738, December.
    21. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    22. Loukil, T. & Teghem, J. & Tuyttens, D., 2005. "Solving multi-objective production scheduling problems using metaheuristics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 42-61, February.
    23. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
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    3. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
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    6. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    7. Wanlin Yang & Zixiang Li & Chenyu Zheng & Zikai Zhang & Liping Zhang & Qiuhua Tang, 2024. "Multi-Objective Optimization for a Partial Disassembly Line Balancing Problem Considering Profit and Carbon Emission," Mathematics, MDPI, vol. 12(8), pages 1-19, April.
    8. V. Anjana & R. Sridharan & P. N. Ram Kumar, 2020. "Metaheuristics for solving a multi-objective flow shop scheduling problem with sequence-dependent setup times," Journal of Scheduling, Springer, vol. 23(1), pages 49-69, February.
    9. Sioud, A. & Gagné, C., 2018. "Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 264(1), pages 66-73.
    10. Zixiang Li & Mukund Nilakantan Janardhanan & S. G. Ponnambalam, 2021. "Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 989-1007, April.

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