Optimization of Flowshop Scheduling with Fuzzy Due Dates Using a Hybrid Evolutionary Algorithm

Abstract

The concept of global work-place in the recent past has been the major driving factor to remain competitive by using the appropriate techniques in various aspects of manufacturing activities. Genetic Algorithm (GA) has proven to be a useful method of optimization for the combinatorial optimization problems. Particle Swarm Optimization (PSO), a new method of optimization, is able to accomplish the same goal as GA optimization in a faster way. The purpose of the present work is to investigate performance of the two algorithms when applied to flowshop scheduling with fuzzy due dates. An attempt has been made to hybridize the two algorithms in series. It is established that the hybrid algorithm (PSOGA) produces better results on problems of larger size when compared to the individual performance of the algorithms.