Using genetic algorithms in process planning for job shop machining

This paper presents a novel computer-aided process planning model for machined parts to be made in a job shop manufacturing environment. The approach deals with process planning problems in a concurrent manner in generating the entire solution space by considering the multiple decision-making activities, i.e., operation selection, machine selection, setup selection, cutting tool selection, and operations sequencing, simultaneously. Genetic algorithms (GAs) were selected due to their flexible representation scheme. The developed GA is able to achieve a near-optimal process plan through specially designed crossover and mutation operators. Flexible criteria are provided for plan evaluation. This technique was implemented and its performance is illustrated in a case study. A space search method is used for comparison