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

The job-shop scheduling problem is one of the most difficult problems in scheduling. This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on 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 jobshop 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 S. Yang Department of Computer Science, University of Leicester University Road, Leicester LE1 7RH, UK E-mail: [email protected] D. Wang · T. Chai Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education Northeastern University, Shenyang 110004, China D. Wang E-mail: [email protected] T. Chai E-mail: [email protected] G. Kendall School of Computer Science, University of Nottingham Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK E-mail: [email protected] 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 jobshop scheduling systems.