Appropriate Evolutionary Algorithm for Scheduling in FMS

Abstract

Driven by open global competition, rapidly changing technology, and shorter product life cycles, manufacturing organizations come across significant amount of uncertainty and hence continuous change. Customers' demand for a greater variety, high quality and competitive cost is in increasing trend. Flexible Manufacturing Systems (FMS) have brought in significant advantages and benefits to manufacturing industries. The ability of FMSs to flex and adapt to both internal and external changes gives rise to improvement in throughput, product quality, information flows, reliability, and other strategic advantages. However, appropriate scheduling methodology can better derive these benefits. The powers Evolutionary Algorithms like genetic algorithm (GA) and simulated annealing (SA) can be beneficially utilized for optimization of scheduling FMS. The present work utilizes these powerful approaches and tries to find out their appropriateness for planning & scheduling of FMS producing variety of parts in batch mode.