A novel hybrid meta-heuristic algorithm for solving multi objective flexible job shop scheduling

• The novel hybrid genetic algorithm and simulated annealing (NHGASA) algorithm is the combination of GA and SA.
• The NHGASA algorithm is introduced to solve multi objective flexible job shop scheduling problem (FJSP).
• The NHGASA algorithm decreases computation time and extremely increases quality of the solutions for multi objective FJSP.
• Another innovation of this paper is using efficiently Pareto optimal solution (MOGA) to solve the FJSP.
• The experimental results prove that the multi-objective results of NHGASA algorithm overcome other approaches for solving the FJSP.

Finding feasible scheduling that optimize all objective functions for flexible job shop scheduling problem (FJSP) is considered by many researchers. In this paper, the novel hybrid genetic algorithm and simulated annealing (NHGASA) is introduced to solve FJSP. The NHGASA is a combination of genetic algorithm and simulated annealing to propose the algorithm that is more efficient than others. The three objective functions in this paper are: minimize the maximum completion time of all the operations (makespan), minimize the workload of the most loaded machine and minimize the total workload of all machines. Pareto optimal solution approach is used in NHGASA for solving FJSP. Contrary to the other methods that assign weights to all objective functions to reduce them to one objective function, in the NHGASA and during all steps, problems are solved by three objectives. Experimental results prove that the NHGASA that uses Pareto optimal solutions for solving multi-objective FJSP overcome previous methods for solving the same benchmarks in the shorter computational time and higher quality.