A genetic algorithm approach for scheduling flexible manufacturing system with setup constraint

Abstract

Driven by open global competition, rapidly changing technology, and shorter product life cycles, manufacturing organizations come across continuous change and hence significant amount of uncertainty. Customers’ demand for a greater variety, high quality and competitive cost is in increasing trend. Traditional manufacturing approaches face threats to remain competitive. Flexible Manufacturing Systems (FMS) have brought in significant advantages and benefits to manufacturing sector, particularly to small and medium sized ones. 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 power of Genetic Algorithm (GA) can be beneficially utilized for optimization of scheduling FMS. The present work utilizes this approach for planning & scheduling of FMS producing large variety of parts in batch mode.