Multi-Ethnicity Genetic Algorithm for Job Shop Scheduling Problems
Atefeh Momenikorbekandi and Maysam F Abbod (Brunel University London, United Kingdom (Great Britain))
This paper deals with applying an ethnic selection Genetic Algorithm (GA) to optimise job shop scheduling problems for single machine and multi-machine job-shops. One of the most popular machine scheduling problems is the classical job-shop scheduling problem (JSSP). Job-shop scheduling refers to the optimisation problem of the job shop by applying computer science. The makespan of JSSP refers to the total length of the schedule when all the jobs have been finished. This paper develops an algorithm which utilises a combination of different types of GA selection functions, namely stochastic, roulette, sexual, and ageing, which constitute an ethnic selection function that benefits from best population in terms of convergence speed and global solution. The proposed genetic algorithm has been applied in single machine job shop and multi-machine with tardiness, earliness, and due date penalties.
Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V22
Published: no date/time given