The application of parallel multipopulation genetic algorithms to dynamic job-shop scheduling

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