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An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times

Author

Listed:
  • S. M. Mousavi

    (Mazandaran University of Science and Technology)

  • I. Mahdavi

    (Mazandaran University of Science and Technology)

  • J. Rezaeian

    (Mazandaran University of Science and Technology)

  • M. Zandieh

    (Shahid Beheshti University, G. C.)

Abstract

This paper deals with a bi-objective hybrid flow shop scheduling problem minimizing the maximum completion time (makespan) and total tardiness, in which we consider re-entrant lines, setup times and position-dependent learning effects. The solution method based on genetic algorithm is proposed to solve the problem approximately, which belongs to non-deterministic polynomial-time (NP)-hard class. The solution procedure is categorized through methods where various solutions are found and then, the decision-makers select the most adequate (a posteriori approach). Taguchi method is applied to set the parameters of proposed algorithm. To demonstrate the validation of proposed algorithm, the full enumeration algorithm is used to find the Pareto-optimal front for special small problems. To show the efficiency and effectiveness of the proposed algorithm in comparison with other efficient algorithm in the literature (namely MLPGA) on our problem, the experiments were conducted on three dimensions of problems: small, medium and large. Computational results are expressed in terms of standard multi-objective metrics. The results show that the proposed algorithm is able to obtain more diversified and competitive Pareto sets than the MLPGA.

Suggested Citation

  • S. M. Mousavi & I. Mahdavi & J. Rezaeian & M. Zandieh, 2018. "An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times," Operational Research, Springer, vol. 18(1), pages 123-158, April.
    • Handle: RePEc:spr:operea:v:18:y:2018:i:1:d:10.1007_s12351-016-0257-6
    DOI: 10.1007/s12351-016-0257-6
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    References listed on IDEAS

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    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
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    5. Dugardin, Frédéric & Yalaoui, Farouk & Amodeo, Lionel, 2010. "New multi-objective method to solve reentrant hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 203(1), pages 22-31, May.
    6. 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.
    7. Biskup, Dirk, 2008. "A state-of-the-art review on scheduling with learning effects," European Journal of Operational Research, Elsevier, vol. 188(2), pages 315-329, July.
    8. Andres, Carlos & Albarracin, Jose Miguel & Tormo, Guillermina & Vicens, Eduardo & Garcia-Sabater, Jose Pedro, 2005. "Group technology in a hybrid flowshop environment: A case study," European Journal of Operational Research, Elsevier, vol. 167(1), pages 272-281, November.
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    Cited by:

    1. Julio Mar-Ortiz & Alex J. Ruiz Torres & Belarmino Adenso-Díaz, 2022. "Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier," Operational Research, Springer, vol. 22(4), pages 4585-4605, September.
    2. Maedeh Fasihi & Reza Tavakkoli-Moghaddam & Fariborz Jolai, 2023. "A bi-objective re-entrant permutation flow shop scheduling problem: minimizing the makespan and maximum tardiness," Operational Research, Springer, vol. 23(2), pages 1-41, June.
    3. Hongtao Tang & Jiahao Zhou & Yiping Shao & Zhixiong Yang, 2023. "Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    4. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    5. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.

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