A genetic algorithm approach to the multiple machine tool selection problem

A number of earlier researches have emphasized the on-the-job scheduling pr oblems that occur with a single flexible machine. Two solutions to the prob lem have generally been considered; namely minimization of tool switches an d minimization of tool switching instances. Methods used to solve the probl ems have included KTNS heuristic, dual-based relaxation heuristic, and non- LP-based branch-and-bound methods. However, scant literature has considered the case of job scheduling on multiple parallel machines which invokes ano ther problem involving machine assignment. This paper addresses the problem of job scheduling and machine assignment on a flexible machining workstati on (FMW) equipped with multiple parallel machines in a tool-sharing environ ment. Under these circumstances, the authors have attempted to model the pr oblem with the objective of simultaneously minimizing both the number of to ol switches and the number of tool switching instances. Furthermore, a set of realistic constraints has been included in the investigation. A novel ge netic algorithm (GA) heuristic has been developed to solve the problem, and performance results show that GA is an appropriate solution.