Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm

Abstract

Problem statement: Now a day’s many researchers try Genetic algorithm based optimization to find near optimal solution for flexible job shop. It is a global search. In Our study in the GA, some changes are made to search locally and globally by adding jumping genes operation. A typical flexible job shop model is considered for this research study. For that layout, five different example problems are formulated for purpose of evaluation. The material flow time for different shop types, processing times of products, waiting times of products, sequences of products are created and given in tabular form. Approach: The one of best evolutionary approach i.e., genetic algorithm with jumping genes operation is applied in this study, to optimize AGV flow time and the performance measures of Flexible Job shop manufacturing system. The non dominated sorting approach is used. Genetic algorithm with jumping genes operator is used to evaluate the method. Results: The AGV flow sequence is found out. Using this flow sequence make span, flow time of products with AGV, completion of the products is minimized. The position of the shop types are calculated for all products. The effectiveness of the proposed method is proved by comparing with Hamed Fazlollahtabar method. Conclusion: It is found that jumping genes genetic algorithm delivered good solutions as like as other evolutionary algorithms. Jumping genes genetic algorithm may applied to Multi objective optimization techniques in future.