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Enhanced Optimization Strategies for No-Wait Flow Shop Scheduling with Sequence-Dependent Setup Times: A Hybrid NEH-GRASP Approach for Minimizing the Total Weighted Flow Time and Energy Cost

Author

Listed:
  • Hafsa Mimouni

    (PCMT Laboratory, National Graduate School of Arts and Crafts, Mohamed V University, Rabat 10100, Morocco)

  • Abdelilah Jalid

    (PCMT Laboratory, National Graduate School of Arts and Crafts, Mohamed V University, Rabat 10100, Morocco)

  • Said Aqil

    (LISIME Laboratory, National Graduate School of Arts and Crafts, Hassan II University, Casablanca 20360, Morocco)

Abstract

Efficient production scheduling is a key challenge in industrial operations and continues to attract significant interest within the field of operations research. This paper investigates a range of methodological approaches designed to solve the permutation flow shop scheduling problem (PFSP) with sequence-dependent setup times (SDST). The main objective is to minimize the total weighted flow time (TWFT) while ensuring a no-wait production environment. The proposed solution strategy is based on using algorithms with a mixed integer linear programming (MILP) formulation, heuristics, and their combination. The heuristics utilized in this paper include an advanced greedy randomized adaptive search procedure (GRASP) based on a priority rule and Hybrid-GRASP-NEH (HGRASP), where Nawaz-Enscore-Ham (NEH) takes place to initiate solutions, based on iterative global and local search methods to refine exploration capabilities and improve solution quality. These approaches were validated using a comprehensive set of experiments across diverse instance sizes that proved the efficiency of HGRASP, with the results showing a high-performance level that closely matched that of the exact MILP approach. Statistical analysis via the Friedman test (χ 2 = 46.75, p = 7.04 × 10 −11 ) confirmed significant performance differences among MILP, GRASP, and HGRASP. While MILP guarantees theoretical optimality, its practical effectiveness was limited by imposed computational time constraints, and HGRASP consistently achieved near-optimal solutions with superior computational efficiency, as demonstrated across diverse instance sizes.

Suggested Citation

  • Hafsa Mimouni & Abdelilah Jalid & Said Aqil, 2025. "Enhanced Optimization Strategies for No-Wait Flow Shop Scheduling with Sequence-Dependent Setup Times: A Hybrid NEH-GRASP Approach for Minimizing the Total Weighted Flow Time and Energy Cost," Sustainability, MDPI, vol. 17(17), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7599-:d:1730739
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    References listed on IDEAS

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    1. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.
    2. Victor Fernandez-Viagas & Luis Sanchez-Mediano & Alvaro Angulo-Cortes & David Gomez-Medina & Jose Manuel Molina-Pariente, 2022. "The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-32, September.
    3. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    4. Yeh, Wei-Chang & Chu, Ta-Chung, 2018. "A novel multi-distribution multi-state flow network and its reliability optimization problem," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 209-217.
    5. Hua Xuan & Huixian Zhang & Bing Li, 2019. "An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence-Dependent Setup Times," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, December.
    6. Ankit Khare & Sunil Agrawal, 2021. "Effective heuristics and metaheuristics to minimise total tardiness for the distributed permutation flowshop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7266-7282, December.
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