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Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem

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  • Selcuk Colak
  • Anurag Agarwal

Abstract

In this paper we propose some non‐greedy heuristics and develop an Augmented‐Neural‐Network (AugNN) formulation for solving the classical open‐shop scheduling problem (OSSP). AugNN is a neural network based meta‐heuristic approach that allows integration of domain‐specific knowledge. The OSSP is framed as a neural network with multiple layers of jobs and machines. Input, output and activation functions are designed to enforce the problem constraints and embed known heuristics to generate a good feasible solution fast. Suitable learning strategies are applied to obtain better neighborhood solutions iteratively. The new heuristics and the AugNN formulation are tested on several benchmark problem instances in the literature and on some new problem instances generated in this study. The results are very competitive with other meta‐heuristic approaches, both in terms of solution quality and computational times. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Suggested Citation

  • Selcuk Colak & Anurag Agarwal, 2005. "Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(7), pages 631-644, October.
    • Handle: RePEc:wly:navres:v:52:y:2005:i:7:p:631-644
    DOI: 10.1002/nav.20102
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    References listed on IDEAS

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    1. Agarwal, Anurag & Pirkul, Hasan & Jacob, Varghese S., 2003. "Augmented neural networks for task scheduling," European Journal of Operational Research, Elsevier, vol. 151(3), pages 481-502, December.
    2. Gueret, Christelle & Prins, Christian, 1998. "Classical and new heuristics for the open-shop problem: A computational evaluation," European Journal of Operational Research, Elsevier, vol. 107(2), pages 306-314, June.
    3. Liaw, Ching-Fang, 2000. "A hybrid genetic algorithm for the open shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 124(1), pages 28-42, July.
    4. Liaw, Ching-Fang, 1998. "An iterative improvement approach for the nonpreemptive open shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 111(3), pages 509-517, December.
    5. Sabuncuoglu, Ihsan & Gurgun, Burckaan, 1996. "A neural network model for scheduling problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 288-299, September.
    6. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    7. Gueret, Christelle & Jussien, Narendra & Prins, Christian, 2000. "Using intelligent backtracking to improve branch-and-bound methods: An application to Open-Shop problems," European Journal of Operational Research, Elsevier, vol. 127(2), pages 344-354, December.
    8. Christian Prins, 2000. "Competitive genetic algorithms for the open-shop scheduling problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 52(3), pages 389-411, December.
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    Cited by:

    1. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    2. Ahmadian, Mohammad Mahdi & Khatami, Mostafa & Salehipour, Amir & Cheng, T.C.E., 2021. "Four decades of research on the open-shop scheduling problem to minimize the makespan," European Journal of Operational Research, Elsevier, vol. 295(2), pages 399-426.
    3. Anurag Agarwal & Selcuk Colak & Jason Deane, 2010. "NeuroGenetic approach for combinatorial optimization: an exploratory analysis," Annals of Operations Research, Springer, vol. 174(1), pages 185-199, February.

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