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A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

Author

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  • Mohamed Abdel-Basset

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Reda Mohamed

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Mohamed Abouhawwash

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
    Department of Computational Mathematics, Science, and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI 48824, USA)

  • Ripon K. Chakrabortty

    (Capability Systems Centre, School of Engineering and IT, UNSW Canberra, Campbell, ACT 2612, Australia)

  • Michael J. Ryan

    (Capability Systems Centre, School of Engineering and IT, UNSW Canberra, Campbell, ACT 2612, Australia)

Abstract

In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.

Suggested Citation

  • Mohamed Abdel-Basset & Reda Mohamed & Mohamed Abouhawwash & Ripon K. Chakrabortty & Michael J. Ryan, 2021. "A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem," Mathematics, MDPI, vol. 9(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:270-:d:489351
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    References listed on IDEAS

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    1. J. Heller, 1960. "Some Numerical Experiments for an M × J Flow Shop and its Decision-Theoretical Aspects," Operations Research, INFORMS, vol. 8(2), pages 178-184, April.
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    Cited by:

    1. Pablo Valledor & Alberto Gomez & Javier Puente & Isabel Fernandez, 2022. "Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-20, July.

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