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An improved constraint satisfaction adaptive neural network for job-shop scheduling

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Abstract

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.

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Authors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Shengxiang Yang

  2. Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Northeastern University, Shenyang, 110004, China

    Dingwei Wang & Tianyou Chai

  3. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

    Graham Kendall

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  1. Shengxiang Yang
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  2. Dingwei Wang
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  3. Tianyou Chai
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  4. Graham Kendall
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Correspondence to Shengxiang Yang.

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Yang, S., Wang, D., Chai, T. et al. An improved constraint satisfaction adaptive neural network for job-shop scheduling. J Sched 13, 17–38 (2010). https://doi.org/10.1007/s10951-009-0106-z

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  • DOI: https://doi.org/10.1007/s10951-009-0106-z

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