Abstract: Aiming at the problem of poor generality of heuristic algorithm, a multi-objective flexible job shop green scheduling model was established, and a hyper-heuristic genetic algorithm was designed to solve the problem. Firstly, the flexible job shop green scheduling model was established with the goal of maximum completion time and minimum energy consumption, and a hyper-heuristic genetic algorithm was designed to optimize the model.Then, for the high-level heuristic strategy, the genetic algorithm was used to randomly generate the initial population, and the population was selected, crossed and mutated. On the basis of conventional operators, combining with the characteristics of flexible job shop scheduling, nine algorithms were designed to adapt to the problem. At the same time, the greedy initialization method was used to generate the low-level problem domain population. Finally, the efficiency of the algorithm was verified by a benchmark example, and the performance of the algorithm was verified by a practical example.The results show that comparing with the reference algorithms, the algorithm using greedy initialization to generate initial population has faster convergence speed, higher operation efficiency, and is not easy to fall into local optimum.The minimum value of the maximum completion time of the solutions obtained by the hyper-heuristic genetic algorithm is 64, and the minimum energy consumption is 647. The quality of the solution obtained by hyper-heuristic genetic algorithm is as good as that of other algorithms, that is,the hyper-heuristic genetic algorithm has a good versatility.

Key words: flexible job-shop;green scheduling; hyper-heuristic algorithm(HHA);genetic algorithm

QU Xin-huai, JI Fei, MENG Guan-jun, et al. Green scheduling of flexible jobshop based on hyper heuristic genetic algorithm［J］.Journal of Mechanical & Electrical Engineering, 2022,39(2):255-261.