A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

Abstract A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided. Scope and purpose Job-shop scheduling is usually a strongly NP-complete problem of combinatorial optimization problems and is the most typical one of the production scheduling problems. It is usually very hard to find its optimal solution. Practically researchers turn to search its near-optimal solutions with all kind of heuristic algorithms. The scope of this paper is to present a new hybrid approach in dealing with this job-shop scheduling problem based on adaptive neural network and heuristics.

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