New Crossover Operator Based On The Machine And Mutation...

Abstract - To solve the job shop scheduling problem more effectively, some genetic operators were designed. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm. Keywords –Hybrid genetic algorithm, Job shop scheduling, hybrid scheduler. 1. INTRODUCTION Job – shop is a system that process n number of tasks on m number of machines. In this type of environment, products are made to order and in a low volume. Usually, these orders are differ in term of processing requirements, materials needed, processing time, processing sequence and setup times. Genetic algorithms are inspired by Darwin s theory about evolution. Solution to a problem solved by genetic algorithms is evolved. Algorithm is started with a set of solutions (represented by chromosomes) called population.Show MoreRelatedIs Data Mining A Detection Process?2438 Words   |  10 Pages(chromosomes) known as population. Outcomes from a population are taken and used to structure a new population. This is aggravated by optimism, and the new population will better the old. Chosen solutions are used to form new solutions (offspring) which are selected according to their fitness values- the more apposite will lead to more chances of reproduction[5]. 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