Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system

This paper proposes an improved hybrid genetic algorithm with variable neighborhood search (HGA-VNS) for addressing the flexible job shop scheduling problem (FJSP) considering the machine work load balance in machining system, with the minimization of the makespan. In the HGA-VNS algorithm, each solution is represented by a chromosome consists of two parts, where the first part is the code of the machining machine number, and the second part chromosome is the code of the machining process number. Second, considering the slow convergence speed of the previous algorithms, a combined methods in crossover and mutation operators that considers the machine work load balance is proposed. Third, a local search approach that carry out for key processes on the critical path which reduces the number of invalid transformations is proposed. For the HGA-VNS, using the orthogonal experiment approach, the best combination of parameters is provided. Then, the proposed HGA-VNS is tested on sets of extended instances based on the well-known benchmarks. Experimental results show that the HGA-VNS is effective, and its performance is significantly better than other algorithms in solving flexible job shop problems in a machining system. Finally, the proposed HGA-VNS is applied to optimize practical FJSP in enterprise F. Compared with the original scheduling scheme, the makespan of the optimal scheduling scheme is reduced by 14.92%, and HGA-VNS can obtain more efficient and economic solutions.