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A new improved NEH heuristic for permutation flowshop scheduling problems

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  • Liu, Weibo
  • Jin, Yan
  • Price, Mark

Abstract

Job evaluation and differentiation are crucial in scheduling. Since jobs can be represented by vectors of processing times, the average, standard deviation, and skewness of job processing times can be defined as the moments of their probability distribution. The first and the second moments of processing times are effective in sorting jobs (Dong et al., 2008), however they are not yet optimized to characterize and differentiate distributions of similar jobs. In this paper, skewness is utilized for the first time to construct a new priority rule, which is applied to the Nawaz-Enscore-Ham (NEH) heuristic (Nawaz et al., 1983), for solving scheduling problems in permutation flowshops. A novel tie-breaking rule is also developed by minimizing partial system idle time without increasing computational complexity of the NEH heuristic, in order to further improve the heuristic performance. Computational results show that the new heuristic outperforms the best NEH-based heuristics reported in the literature in terms of solution quality.

Suggested Citation

  • Liu, Weibo & Jin, Yan & Price, Mark, 2017. "A new improved NEH heuristic for permutation flowshop scheduling problems," International Journal of Production Economics, Elsevier, vol. 193(C), pages 21-30.
    • Handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:21-30
    DOI: 10.1016/j.ijpe.2017.06.026
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    References listed on IDEAS

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    Cited by:

    1. Benavides, Alexander J. & Vera, Antony, 2022. "The reversibility property in a job-insertion tiebreaker for the permutational flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(2), pages 407-421.
    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. Libralesso, Luc & Focke, Pablo Andres & Secardin, Aurélien & Jost, Vincent, 2022. "Iterative beam search algorithms for the permutation flowshop," European Journal of Operational Research, Elsevier, vol. 301(1), pages 217-234.
    4. Fernandez-Viagas, Victor & Talens, Carla & Framinan, Jose M., 2022. "Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 869-882.
    5. Gmys, Jan & Mezmaz, Mohand & Melab, Nouredine & Tuyttens, Daniel, 2020. "A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 814-833.
    6. Meenakshi Sharma & Manisha Sharma & Sameer Sharma, 2022. "Desert sparrow optimization algorithm for the bicriteria flow shop scheduling problem with sequence-independent setup time," Operational Research, Springer, vol. 22(4), pages 4353-4396, September.

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