A Research on the Adaptive Dynamic Scheduling Based on Scenario Deduction
Journal: Architecture Engineering and Science DOI: 10.32629/aes.v3i4.1099
Abstract
Aiming at the uncertainty of the intelligent manufacturing system, this paper reveals the evolution mechanism of the manufacturing system and its internal and external environment from a data-driven perspective. And it proposes an adaptive dynamic scheduling method based on scenario deduction, which provides the basis for the intelligent manufacturing support in an uncertain environment. Firstly, the paper uses the ontology to describe the scenario status, production activities, market environment and production subject of manufacturing system. And then builds the scenario deduction model based on the dynamic fuzzy cognitive map, establishing the data-driven manufacturing scenario network structure. Through the dynamic fuzzy cognitive map, it presents the market and production scenario evolution and accordingly guides the adaptive choice of dynamic scheduling strategy in uncertain production environment. The results show that the scenario deduction model is basically consistent with the manufacturing system evolution process in terms of time inference and demand prediction, and it verifies the adaptability and effectiveness of the proposed dynamic dispatching method through examples.
Keywords
scenario deduction, data-driven, adaptive dynamic scheduling, fuzzy cognitive map, intelligent manufacturing
Funding
National Natural Science Foundation of China (Grant No.71861031 and Grant No.72061029)
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[2] Blackstone J H, Phillips D T, Hogg G L. A state-of-theart survey of dispatching rules for manufacturing job shop operations[J]. Int J of Production Research, 1982, 20(1):27-45.
[3] Yarong Chen, Shuchen Guan, Chengjun Huang,et al.An adaptive dynamic scheduling for bi-objective parallel multi-processor open shop by usingsimulation[J]. Journal of System Simulation,2021(11):1-13.
[4] Xanthopoulos A S, Koulouriotis D E, Tourassis V D, et al.Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups[J]. Applied Soft Computing, 2013, 13(12): 4704-4717.
[5] Lee K K. Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment[J]. Applied Soft Computing, 2008, 28(8): 1295-1304.
[6] Li Congbo, Kou Yang, Lei Yanfei, et al. Flexible job shoprescheduling optimization method for energy-saving based ondynamic events[J]. Computer Integrated ManufacturingSystems, 2020, 26(2):288-299.
[6] Abreu L R, Cunha J O, Prata B A, et al. A Genetic Algorithm for Scheduling Open Shops with Sequence-dependent Setup Times[J]. Computers & Operations Research(S0305-0548),2020,113:104793.
[7] Kosko B. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence [M]. Prentice Hall, 1992.
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