Application of parallel multipopulation genetic algorithms to dynamic job-shop scheduling

Abstract

This paper describes the use of parallel multipopulation genetic algorithms (GAs) to meet the dynamic nature of job-shop scheduling. A modified genetic technique is adopted by using a specially formulated genetic operator to provide an efficient optimization search. The proposed technique has been successfully implemented using the programming language MATrix LABoratory (MATLAB), providing a powerful tool for job-shop scheduling. Comparisons indicate that the proposed genetic algorithm has successfully improved upon the solution obtained from conventional approaches, particularly in coping with job-shop scheduling.