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International Journal of Multimedia Computing, 2022, 3(4); doi: 10.38007/IJMC.2022.030404.

Super Heuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling


Yu Han

Corresponding Author:
Yu Han

Shenyang Jinbei Vehicle Manufacturing Co., LTD, Shenyang, Liaoning, China


The purpose of this paper is to propose a hybrid super heuristic genetic algorithm for solving a class of fuzzy flexible job shop scheduling problems of work pieces processing time represented by using triangular fuzzy numbers, to minimize the optimization goal and reduce the fuzzy completion time. At the same time, under the conditions of product customization trend, diversified development of process routes, provide enterprises with a small-scale customized production that can be realized in time and a solution to improve the flexible operation of production systems .This paper first considers the practical problems in the production process of fuzzy flexible job shop and establishes a multi-objective optimization model. Then carried out a lot of research and analysis on traditional genetic algorithm, finding that the standard genetic algorithm is easy to fall into the problems of local optimum, low search efficiency and infeasible solution when solving the problem of shop scheduling. Came up with the hybrid heuristic algorithm for this problem, the algorithm incorporates methods such as hybrid heuristics, making the generated initial population as much as possible in the solution space of the whole problem, and to ensure the diversity of solution. Finally, through a series of improvements to the traditional genetic algorithm, improve the way of coding and genetic operators based on the super heuristic genetic algorithm, combine elite retention strategies and niche technologies to further optimize the convergence and diversity of algorithms. Calculates the fitness of the chromosome by weight coefficient change method. Therefore, the results of experimental analysis show that the proposed algorithm can verify the effectiveness of the proposed sorting criterion and super heuristic genetic algorithm, and can play a good role in the actual production process, it can also fully reflect the target requirements of fuzzy flexible job shop scheduling in production.


Resource Scheduling, Super Heuristic Genetic Algorithm, Job Shop Scheduling, Flexible Job Shop, Multi-objective Scheduling

Cite This Paper

Yu Han. Super Heuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling. International Journal of Multimedia Computing  (2022), Vol. 3, Issue 4: 41-52. https://doi.org/10.38007/IJMC.2022.030404.


[1] Bae E Y, Mah J S. The role of industrial policy in the economic development of Uzbekistan. Post Communist Economies, 2018, 31:1-18.https://doi.org/10.1080/14631377.2018.1443252

[2] Steen M, Njøs R. Green restructuring, innovation, and transitions in Norwegian industry: The role of economic geography. Norsk Geografisk Tidsskrift / Norwegian Journal of Geography, 2019, 73(1):1-3.https://doi.org/10.1080/00291951.2018.1558281

[3] Yu H, Fang L, Sun B. The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil.. Plos One, 2018, 13(2):192305.https://doi.org/10.1371/journal.pone.0192305

[4] Gao K, Yang F, Zhou M C, et al. Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm. IEEE Transactions on Cybernetics, 2018, PP(99):1-12.

[5] Xie N, Chen N. Flexible job shop scheduling problem with interval grey processing time. Applied Soft Computing, 2018, 70:513-524.https://doi.org/10.1016/j.asoc.2018.06.004

[6] Syahputra M F, Apriani R, Sawaluddin, et al. Genetic algorithm to solve the problems of lectures and practicums scheduling. IOP Conference Series: Materials Science and Engineering, 2018, 30(8):12046.https://doi.org/10.1088/1757-899X/308/1/012046

[7] Sun L, Lin L, Gen M, et al. A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling. IEEE Transactions on Fuzzy Systems, 2019, PP(99):1-1.

[8] Wang C, Na T, Ji Z, et al. Multi-objective fuzzy flexible job shop scheduling using memetic algorithm. Journal of Statistical Computation & Simulation, 2017, 87(14):1-19.https://doi.org/10.1080/00949655.2017.1344846

[9] Zhang X, Hipel K W, Tan Y. Project portfolio selection and scheduling under a fuzzy environment. Memetic Computing, 2019,67(3):1-16.https://doi.org/10.1007/s12293-019-00282-5

[10] Donahoe E, Metzger M D. Artificial Intelligence and Human Rights. Journal of Democracy, 2019, 30(2):115-126.https://doi.org/10.1353/jod.2019.0029

[11] Zhang X, Wang Y, Liu C, et al. A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm. Journal of Power Sources, 2018, 376:191-199.https://doi.org/10.1016/j.jpowsour.2017.11.068

[12] Li W, Özcan E, John R. A Learning Automata-Based Multiobjective Hyper-Heuristic. IEEE Transactions on Evolutionary Computation, 2017, PP(99):1-1.

[13] Schlünz E B, Bokov P M, Vuuren J H V. Multiobjective in-core nuclear fuel management optimisation by means of a hyperheuristic. Swarm & Evolutionary Computation, 2018, 42:58-76.https://doi.org/10.1016/j.swevo.2018.02.019

[14] Xia W, Quek T Q S, Zhang J, et al. Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation. IEEE Transactions on Wireless Communications, 2019, 18(3):1-1.https://doi.org/10.1109/TWC.2019.2901684

[15] Wang J, Liu C, Li K. A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement. Automatika ‒ Journal for Control Measurement Electronics Computing and Communications, 2019, 60(2):172-180.https://doi.org/10.1080/00051144.2019.1603264

[16] Han Y, Li J Q, Gong D, et al. Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking. IEEE Access, 2019, 7(9):5946-5962.https://doi.org/10.1109/ACCESS.2018.2889373

[17] Vallejos-Cifuentes P, Ramirez-Gomez C, Escudero-Atehortua A, et al. Energy-Aware Production Scheduling in Flow Shop and Job Shop Environments Using a Multi-Objective Genetic Algorithm. Engineering Management Journal, 2019,43(1):1-16.https://doi.org/10.1080/10429247.2018.1544798

[18] Li Y, Yang Z, Zhao D, et al. Incorporating energy storage and user experience in isolated microgrid dispatch using a multi-objective model. IET Renewable Power Generation, 2019, 13(6):973-981.https://doi.org/10.1049/iet-rpg.2018.5862

[19] Li B, Yang X, Xuan H. A Hybrid Simulated Annealing Heuristic for Multistage Heterogeneous Fleet Scheduling with Fleet Sizing Decisions. Journal of Advanced Transportation, 2019, 2019(10):1-19.https://doi.org/10.1155/2019/1524178

[20] Wu W. HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recognition, 2018, 88:569-583.https://doi.org/10.1016/j.patcog.2018.12.022