Sequence optimization and design of allocation using GA and SA

Two artificial intelligence techniques, evolutionary algorithms and simulated annealing are proposed to search for solutions to machine scheduling problem and optimal design in manufacturing systems. The performance of each the techniques is studied and the results compared with these from conventional methods. Evolutionary algorithms are computer-based problem-solving systems based on principles of evolutionary theory. A variety of evolutionary algorithms have been developed and they all share a common conceptual base of simulating the evolution of individual structures via processes of selection, mutation and recombination. The processes depend on the perceived performance of the individual structures as defined by an environment. One of the most popular evolutionary algorithms is genetic algorithm. Simulated annealing is an intelligent approach designed to give a good though not necessarily optimal solution, within a reasonable computation time. The motivation for simulated annealing comes from an analogy between the physical annealing of solid materials and optimization problem. This paper presents a general purpose schedule optimizer for manufacturing shop scheduling using genetic algorithms and the optimal design of inspection station in manufacturing systems by genetic algorithms and simulated annealing techniques. Then, a novel general effect of mutation rate on minimized objective value are presented. The task is to determine the optimal settings of the production parameters to minimize a cost function.