A HYBRID HOPFIELD NEURAL NETWORKS BASED SIMULATION APPROACH FOR OPTIMISATION OF MANUFACTURING GROUP SCHEDULING

ABSTRACT

A hybrid Neural Networks have been developed and the simulation techniques have been used to solve complex group scheduling problems. The objectives are to find the best performance criterion optimum for a given number of jobs and machines in order to satisfy different production constraints. The solution methods proposed in this paper, have their roots in both operational research and artificial intelligence. The use of combining Hopfield Neural Networks (HNN) and Tabu (local search) approach to define optimal groups of operations which will facilitate the generation of the route sheet has been discussed and presented in this paper. For each one of the operations, the (HNN) will be used to generate the feasible manufacturing alternatives, and the Tabu search technique will be applied to identify the best schedule. The proposed approach, Hybrid Hopfield Neural Networks (HHNN) and the simulation techniques, save time and minimise unbalanced workloads among the operations. The potential ability of the proposed approach is shown using a numerical case example of a big size of group scheduling problem.

Keywords:

• computer integrated manufacturing
• cellular manufacturing
• group technology
• neural network
• tabu search
• computer simulation
• production control