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 optimisation 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.
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Qi, J., Burns, G. & Harrison, D. The Application of Parallel Multipopulation Genetic Algorithms to Dynamic Job-Shop Scheduling. Int J Adv Manuf Technol 16, 609–615 (2000). https://doi.org/10.1007/s001700070052
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DOI: https://doi.org/10.1007/s001700070052