Abstract
A simulation model was established, minimizing the makespan and stability value, to solve the dynamic scheduling of flexible job-shop problems, and an improved hybrid multi-phase quantum particle swarm algorithm is proposed. Firstly, a double chain structure coding method, including a machine allocation chain and a process chain, is proposed. Secondly, a dynamic periodic and event-driven scheduling strategy is proposed. Finally, the novel method is applied to the Brandimarte set and a dynamic simulation is performed. Comparing the results with the results of existing algorithms demonstrates the effectiveness of the proposed hybrid multi-phase quantum particle swarm optimization algorithm and strategy for solving the dynamic scheduling of flexible job-shop problems.
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References
Bagheri A, Zandith M, Mahdavi I, Yanzdanni M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Fut Gen Comp Sys 26(4):533–541
Brandimarte P (1993) Routing and scheduling in a flexible workpiece shop by tabu search. Ann Oper Res 41(3):157–183
Fattahi P, Fallahi A (2010) Dynamic scheduling in flexible workpiece shop systems by considering simultaneously efficiency and stability. CIRP J Manufact Sci Technol 2(2):114–123
Ishibuchi H, Murata T (1998) A Multi-objective genetic local search algorithm and its application to flow shop scheduling. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):392–403
Jackson JR (1957) Simulation research on workpiece shop production. Naval Res Log Quart 4(3):287–295
Liu A, Y Yu, Xing Q et al (2011) Multi-population genetic algorithm in multi-objective fuzzy and flexible workpiece shop scheduling. Comput Integr Manuf Syst 17(9):1954–1961
Matsuura H, Tsubone H, Kanezashi M (1993) Sequencing, dispatching and switching in a dynamic manufacturing environment. Int J Prod Res 31(7):1671–1688
Ning T, Guo C, Chen R et al (2015) Simulation study on scheduling strategy of dynamic vehicle routing problem [J]. J Syst Simul 12:74–79
Rahmat SHA, Zandieh M, Yazdani M (2013) Developing two multi-objective evolutionary algorithms for the multi-objective flexible workpiece shop scheduling problem. Int J Adv Manuf Technol 64(5):915–932
Rajendran C, Holthaus O (1999) A comparative study of dispatching rules in dynamic flow shops and workpiece shops. Eur J Op Res 116:156–170
Shi J, Jiao H, Chen T (2012) Multi-objective pareto optimization on flexible job-shop scheduling problem about due punishment. J Mech Eng 48(12):188–196
Tang J (2012) Research of scheduling problems based on hybrid evolutionary algorithm. South China University of Technology, Guangzhou
Yang K, Liu X (2009) Multi-objective optimization for flow shop scheduling with the group technology assumption removed. Comput Integr Manuf Syst 2:348–357
Zhang J, Wang W, Xu X et al (2012) Hybrid particle-swarm optimization for multi- objective flexible job-shop scheduling problem. Control Theory Appl 29(6):30–37
Acknowledgments
This work was partly supported by the National Natural Science Foundation, China (No. 51579024), the Talented Young Scholars Growth Plan of Liaoning Province Education Department, China (No. LJQ2013048), the Scientific Research Project of Liaoning Province Education Department, China (No. L2014183), the Project of Liaoning BaiQianWan Talents Program, China (No. 2014921062).
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Ning, T., Huang, M., Liang, X. et al. A novel dynamic scheduling strategy for solving flexible job-shop problems. J Ambient Intell Human Comput 7, 721–729 (2016). https://doi.org/10.1007/s12652-016-0370-7
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DOI: https://doi.org/10.1007/s12652-016-0370-7