Research on Integrated Scheduling of Production and Maintenance with Machine Dynamics and Processing Speed Selection

doi:10.3901/JME.2025.14.362

Abstract

Abstract: For the dynamic processing environment, the existing research on dynamic disturbance events mainly focuses on new job arrival and machine random failure, and rarely considers the influence of dynamic changes in the total number of machines (such as increasing and decreasing the number of machines). In order to make up for this shortcoming, a multi-objective flexible job shop integrated scheduling problem including new machine insertion, old machine scrap, processing speed selection and predictive maintenance(PdM) is studied, which includes new machine insertion, old machine scrap, processing speed selection and predictive maintenance(PdM). The specific research contents include: ① a multi-phase-multi-threshold PdM policy with processing speed selection, variable PdM thresholds and sixteen discrete inspection policies is designed based on the accelerated degradation Gamma process; ② an adaptive rescheduling strategy(ARS) is constructed for new machine insertion, old machine scrap and the scale of production scheduling problem; and ③ an adaptive bi-population cooperative multi-objective evolutionary algorithm (ABCMOEA) is proposed to address the concerned problem. In the numerical simulation, the effect of parameter settings on ABCMOEA algorithm is firstly investigated. Secondly, the effectiveness of proposed cooperative initialization method and three local search mechanisms is demonstrated by literature comparisons. Thirdly, the superiority of proposed ABCMOEA algorithm, PdM policy and ARS strategy is separately verified by comparing with other intelligent algorithms, PdM policy and rescheduling strategies. Finally, through sensitivity analysis, it is found that the selectable range of processing speed and maintenance level has a significant impact on the research of integrated optimization, and the former has a greater impact.

Key words: flexible job-shop scheduling, new machine insertion, old machine scrap, processing speed selection, maintenance level selection, predictive maintenance, multi-objective evolutionary algorithm

CLC Number:

TP273

AN Youjun, LIU Cheng, CHEN Xiaohui, GAO Kaizhou, DONG Yuanfa, MENG Ronghua. Research on Integrated Scheduling of Production and Maintenance with Machine Dynamics and Processing Speed Selection[J]. Journal of Mechanical Engineering, 2025, 61(14): 362-382.

References

[1] WAN J，CHEN B，WANG S，et al. Fog computing for energy-aware load balancing and scheduling in smart factory[J]. IEEE Transactions on Industrial Informatics，2018，14(10):4548-4556.
[2] WANG X，GAO L，ZHANG C，et al. A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem[J]. The International Journal of Advanced Manufacturing Technology，2010，51:757-767.
[3] GAO K，YANG F，ZHOU M C，et al. Flexible job-shop rescheduling for new job insertion by using discrete Jaya algorithm[J]. IEEE Transactions on Cybernetics，2018，49(5):1944-1955.
[4] 卢超，田禾子，李新宇，等. 基于多目标海鸥算法的分布式焊接节能调度[J]. 机械工程学报，2024，60(22):192-203. LU Chao，TIAN Hezi，LI Xinyu，et al. Energy-efficient distributed welding shop scheduling based on multi- objective seagull algorithm[J]. Journal of Mechanical Engineering，2024，60(22):192-203.
[5] 吕岩，徐正军，李聪波，等. 考虑扰动事件的机械加工工艺参数与车间动态调度综合节能优化[J]. 机械工程学报，2022，58(19):242-255. LÜ Yan，XU Zhengjun，LI Congbo，et al. Comprehensive energy saving optimization of processing parameters and job shop dynamic scheduling considering disturbance events[J]. Journal of Mechanical Engineering，2022，58(19):242-255.
[6] GHALEB M，TAGHIPOUR S，ZOLFAGHARINIA H. Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance [J]. Journal of Manufacturing Systems，2021，61:423-449.
[7] QIAO F，MA Y M，ZHOU M C，et al. A novel rescheduling method for dynamic semiconductor manufacturing systems[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2018，50(5):1679-1689.
[8] WANG Z，ZHANG J，YANGY S. An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals[J]. Swarm and Evolutionary Computation，2019，51:100594.
[9] FRAMINAN J M，FERNANDEZ-VIAGAS V，PEREZ-GONZALEZ P. Using real-time information to reschedule jobs in a flowshop with variable processing times[J]. Computers & Industrial Engineering，2019，129:113-125.
[10] AN Y，CHEN X，GAO K，et al. A hybrid multi-objective evolutionary algorithm for solving an adaptive flexible job-shop rescheduling problem with real-time order acceptance and condition-based preventive maintenance [J]. Expert Systems with Applications，2023，212:118711.
[11] ZHANG G，LU X，LIU X，et al. An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown[J]. Expert Systems with Applications，2022，203:117460.
[12] SANG Y，TAN J，LIU W. A new many-objective green dynamic scheduling disruption management approach for machining workshop based on green manufacturing[J]. Journal of Cleaner Production，2021，297:126489.
[13] HAMZADAYI A，YILDIZ G. Event driven strategy based complete rescheduling approaches for dynamic m identical parallel machines scheduling problem with a common server[J]. Computers & Industrial Engineering，2016，91:66-84.
[14] GHALEB M，ZOLFAGHARINIA H，TAGHIPOUR S. Real-time production scheduling in the Industry-4.0 context:Addressing uncertainties in job arrivals and machine breakdowns[J]. Computers & Operations Research，2020，123:105031.
[15] DUAN J，WANG J. Robust scheduling for flexible machining job shop subject to machine breakdowns and new job arrivals considering system reusability and task recurrence[J]. Expert Systems with Applications，2022，203:117489.
[16] AN Y，CHEN X，HU J，et al. Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection[J]. Reliability Engineering & System Safety，2022，220:108269.
[17] LI X，PENG Z，DU B，et al. Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems[J]. Computers & Industrial Engineering，2017，113:10-26.
[18] AN Y，CHEN X，GAO K，et al. Multiobjective flexible job-shop rescheduling with new job insertion and machine preventive maintenance[J]. IEEE Transactions on Cybernetics，2023，53(5):3101-3113.
[19] CHANSOMBAT S，PONGCHAROEN P，HICKS C. A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry[J]. International Journal of Production Research，2019，57(1):61-82.
[20] XU H，LI X，RUIZ R，et al. Group scheduling with nonperiodical maintenance and deteriorating effects[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2019，51(5):2860-2872.
[21] 刘琼，刘嘉豪，刘佳良. 基于改进人工蜂群算法的预防性维修与柔性作业车间成组调度集成优化[J]. 机械工程学报，2023，59(12):89-96. LIU Qiong，LIU Jiahao，LIU Jialiang. Integrated optimization of preventive maintenance and flexible job shop group scheduling based on an ABC-AN algorithm[J]. Journal of Mechanical Engineering，2023，59(12): 89-96.
[22] WANG H，YAN Q，ZHANG S. Integrated scheduling and flexible maintenance in deteriorating multi-state single machine system using a reinforcement learning approach[J]. Advanced Engineering Informatics，2021，49:101339.
[23] SHARIFI M，TAGHIPOUR S. Optimal production and maintenance scheduling for a degrading multi-failure modes single-machine production environment[J]. Applied Soft Computing，2021，106:107312.
[24] UIT HET BROEK M A J，TEUNTER R H，DE JONGE B，et al. Condition-based production planning:Adjusting production rates to balance output and failure risk[J]. Manufacturing & Service Operations Management，2020，22(4):792-811.
[25] DEB K，PRATAP A，AGARWAL S，et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation，2002，6(2):182-197.
[26] DEB K，JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach，Part I:Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation，2013，18(4):577-601.
[27] XIANG Y，ZHOU Y，YANG X，et al. A many-objective evolutionary algorithm with Pareto-adaptive reference points[J]. IEEE Transactions on Evolutionary Computation，2019，24(1):99-113.
[28] CHENG R，JIN Y，OLHOFER M，et al. A reference vector guided evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation，2016，20(5):773-791.
[29] FU Y，ZHOU M C，GUO X，et al. Scheduling dual-objective stochastic hybrid flow shop with deteriorating jobs via bi-population evolutionary algorithm[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2019，50(12):5037-5048.
[30] LI F，GAO L，SHEN W，et al. Surrogate-assisted multi-objective evolutionary optimization with a multi-offspring method and two infill criteria[J]. Swarm and Evolutionary Computation，2023，79:101315.
[31] KIJIMA M，MORIMURA H，SUZUKI Y. Periodical replacement problem without assuming minimal repair[J]. European Journal of Operational Research，1988，37(2):194-203.
[32] KHATAB A，DIALLO C，AGHEZZAF E H，et al. Condition-based selective maintenance for stochastically degrading multi-component systems under periodic inspection and imperfect maintenance[J]. Proceedings of the Institution of Mechanical Engineers，Part O:Journal of Risk and Reliability，2018，232(4):447-463.
[33] LIU B，WU S，XIE M，et al. A condition-based maintenance policy for degrading systems with age-and state-dependent operating cost[J]. European Journal of Operational Research，2017，263(3):879-887.
[34] ROSS S，Stochastic processes[M]. New York:John Wiley & Sons，1996.
[35] ZAKARIA Z，PETROVIC S. Genetic algorithms for match-up rescheduling of the flexible manufacturing systems[J]. Computers & Industrial Engineering，2012，62(2):670-686.
[36] LI X，GAO L，PAN Q，et al. An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2018，49(10):1933-1945.
[37] NOWICKI E，SMUTNICKI C. A fast taboo search algorithm for the job shop scheduling problem[J]. Management Science，1996，42(6):797-813.
[38] ZHANG G，GAO L，SHI Y. An effective genetic algorithm for the flexible job-shop scheduling problem[J]. Expert Systems with Applications，2011，38(4):3563-3573.