Super Heuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling

Abstract

The purpose of this paper is to propose a hybrid super heuristic genetic algorithm for solving a class of fuzzy flexible job shop scheduling problems of work pieces processing time represented by using triangular fuzzy numbers, to minimize the optimization goal and reduce the fuzzy completion time. At the same time, under the conditions of product customization trend, diversified development of process routes, provide enterprises with a small-scale customized production that can be realized in time and a solution to improve the flexible operation of production systems .This paper first considers the practical problems in the production process of fuzzy flexible job shop and establishes a multi-objective optimization model. Then carried out a lot of research and analysis on traditional genetic algorithm, finding that the standard genetic algorithm is easy to fall into the problems of local optimum, low search efficiency and infeasible solution when solving the problem of shop scheduling. Came up with the hybrid heuristic algorithm for this problem, the algorithm incorporates methods such as hybrid heuristics, making the generated initial population as much as possible in the solution space of the whole problem, and to ensure the diversity of solution. Finally, through a series of improvements to the traditional genetic algorithm, improve the way of coding and genetic operators based on the super heuristic genetic algorithm, combine elite retention strategies and niche technologies to further optimize the convergence and diversity of algorithms. Calculates the fitness of the chromosome by weight coefficient change method. Therefore, the results of experimental analysis show that the proposed algorithm can verify the effectiveness of the proposed sorting criterion and super heuristic genetic algorithm, and can play a good role in the actual production process, it can also fully reflect the target requirements of fuzzy flexible job shop scheduling in production.