An Effective Hybrid Multiobjective Flexible Job Shop Scheduling Problem Based on Improved Genetic Algorithm

Multiobjective Flexible Job Shop Scheduling Problem (MO-FJSP) is a scheduling problem used in manufacturing sectors to use energy efficiently and thriftily. The aims increase productivity reduce consumption via mathematical model. With this paper, an effective genetic algorithm proposed for MO-FJSP based on maximum completion time, total machine load, bottleneck load. solution method utilizes hybrid multiobjective algorithm. A combination of global selection fast initialization obtaining uniformly distributed initial population. cross-variance operator adaptively improved enhance the searching Following that elite retention mechanism designed address possible limitations strategy maintaining population diversity. As result, harmonic search introduced improve quality individuals pool. implemented MATLAB R2018a. Tests were conducted using benchmark Kacem test set, BR data with actual production cases. succeeded achieving 13 nondominated solutions 20 runs. Moreover, obtains optimal value criterion accuracy factor. whole, results evaluation testify can be solve high convergence. also provides feasible decision-makers production. Based promising obtained, it deduced has wide applicability range particularly sector.