Scheduling complex manufacturing systems using a genetic algorithm

Abstract

This paper presents a Genetic Algorithm (GA) application for solving flexible job shop problems with alternative routings. Such manufacturing systems combine features of both project and flexible manufacturing systems that include alternative processing routes, parallel execution of manufacturing operations, multiple options formachine selection, and job recirculation, among others. The proposed chromosome representation uses a combination of integer and random keys. The integer section is used for resource selection and the random keys are used as priorities by the schedule procedure to generate feasible schedules. The performance of the GA was tested using both problems from the literature and from a real case study. Two objective functions were chosen: makespan and mean flow time. Computational results show the effectiveness of the proposed algorithm.