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A Hybrid Algorithm for Multi Objective Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem (FJSP) is one of the most important issues in different areas of production management and manufacturing systems and currently many researchers in the field of optimization issues consider it.This study includes FJSP problem with three objective functions minimization for makespan, maximum workload and total workload of machines. In this study a hybrid metaheuristich algorithm based on tabu search, simulated annealing and genetic algorithms with suitable parameters for solving is presented, So that the targeted and effective combination of random solutions with the results from SA, TS and TSA algorithms are considered to initial population for genetic algorithm. Also in genetic algorithm, a multivariate equation with minimum square error by using regression equations in SPSS software is introduced, which obtains effective numbers of each mutation, crossover, reproduction(Elite-oriented algorithm) operators to produce new generation. In order to evaluate the effectiveness and efficiency of proposed algorithm, obtained results are compared with optimal solutions obtained through solving mathematic model and KACO, PSO, SA, GA and local approach methods. Results of experiments and computational analysis show that the proposed algorithm in this study has ability to achieve close to optimal points at suitable time for different issues in different sizes.