GA-BASED SCHEDULING SYSTEM FOR FLOW SHOP AND JOB SHOP SCHEDULING PROBLEMS

Abstract

Operational scheduling of manufacturing industry is of paramount importance for cost-effective and competitive operation for survival in the market. The operational scheduling encompasses a wide range of scheduling models and many attempts have been made to solve them using analytical, heuristic and artificial intelligence based approaches but no robust solution is yet available to tackle the diverse range of scheduling models. Recent trend shows that Genetic Algorithm (GA)-based solutions are popular because of their suitability to the combinatorial nature of the scheduling problem. In this paper, a GA-based scheduling system is presented where a novel combination of recently introduced crossover scheme for job based chromosome representation with Stochastic Universal Selection (SUS), simple mutation of exchange of genes and a heuristic for evaluation of chromosomes is proposed which is easy to code and robust enough to deal with flow shop as well as job shop scheduling problem. The system is validated through case studies from real world scheduling problems.