Joint optimization of selective maintenance decision-making and maintenance personnel allocation under limited resources

Yang Jiao; Qiu-Xiang Tao; Hui Liu; Qi-Rui Peng; Zhi-Cheng Zhou; Yang Jiao; Qiu-Xiang Tao; Hui Liu; Qi-Rui Peng; Zhi-Cheng Zhou

doi:10.3934/jimo.2026043

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 2: 1168-1193. doi: 10.3934/jimo.2026043

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Research article Special Issues

Joint optimization of selective maintenance decision-making and maintenance personnel allocation under limited resources

1.
College of Economics and Management, Nanchang Hangkong University, Nanchang 330063, China
2.
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Received: 16 September 2025 Revised: 20 December 2025 Accepted: 22 January 2026 Published: 30 January 2026
90B25, 90B35

Integrating selective maintenance strategies with personnel allocation for equipment groups is essential to meet combat missions' demands and significantly boost overall combat effectiveness. Accordingly, this study aims to maximize the probability of mission completion for equipment groups by developing a joint optimization model under constrained resource conditions. An environmental coefficient is incorporated to represent the dynamic impact of varying combat environments on the degradation states of individual units. Using a nonhomogeneous Markov model, this study calculates the state transition probabilities of units throughout the mission to derive the mission completion probabilities for both the equipment group and the overall combat cycle. To solve this model, an adaptive quantum immune algorithm is applied to the case study. These findings demonstrate that the proposed model and algorithm enhance maintenance decision-making quality and clarify optimization patterns regarding resource efficiency and dynamic personnel allocation. Thus they offer both a theoretical foundation and practical guidance for battlefield maintenance support.
- selective maintenance,
- allocation of maintenance personnel,
- joint optimization,
- adaptive quantum immune algorithm
Citation: Yang Jiao, Qiu-Xiang Tao, Hui Liu, Qi-Rui Peng, Zhi-Cheng Zhou. Joint optimization of selective maintenance decision-making and maintenance personnel allocation under limited resources[J]. Journal of Industrial and Management Optimization, 2026, 22(2): 1168-1193. doi: 10.3934/jimo.2026043

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Abstract

Integrating selective maintenance strategies with personnel allocation for equipment groups is essential to meet combat missions' demands and significantly boost overall combat effectiveness. Accordingly, this study aims to maximize the probability of mission completion for equipment groups by developing a joint optimization model under constrained resource conditions. An environmental coefficient is incorporated to represent the dynamic impact of varying combat environments on the degradation states of individual units. Using a nonhomogeneous Markov model, this study calculates the state transition probabilities of units throughout the mission to derive the mission completion probabilities for both the equipment group and the overall combat cycle. To solve this model, an adaptive quantum immune algorithm is applied to the case study. These findings demonstrate that the proposed model and algorithm enhance maintenance decision-making quality and clarify optimization patterns regarding resource efficiency and dynamic personnel allocation. Thus they offer both a theoretical foundation and practical guidance for battlefield maintenance support.

References

[1]	S. B. Yin, L. Zhou, M. Zhao, Z. H. Ren, Research on replacement and maintenance decision of degraded equipment based on maintenance effectiveness, Mil. Oper. Res. Assess., 35 (2021), 36–42. https://doi.org/10.19949/j.ams.mora.20210330.01 doi: 10.19949/j.ams.mora.20210330.01
[2]	N. Gupta, I. Ali, A. Bari, Fuzzy goal programming approach in selective maintenance reliability model, Pak. J. Stat. Oper. Res., 9 (2013), 321–331. https://doi.org/10.18187/pjsor.v9i3.654 doi: 10.18187/pjsor.v9i3.654
[3]	I. Ali, S. S. Hasan, Bi-criteria optimization technique in stochastic system maintenance allocation problem, Am. J. Oper. Res., 3 (2013), 17–29. https://dx.doi.org/10.4236/ajor.2013.31002 doi: 10.4236/ajor.2013.31002
[4]	J. F. Wang, G. W. Jia, J. C. Lin, Z. Hou, Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm, J. Cent. South Univ., 27 (2020), 432–448. https://doi.org/10.1007/s11771-020-4307-0 doi: 10.1007/s11771-020-4307-0
[5]	Z. Y. Jia, J. Q. Yu, X. L. Ai, X. Xu, D. Yang, Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm, Aerosp. Sci. Technol., 76 (2018), 112–125. https://doi.org/10.1016/j.ast.2018.01.025 doi: 10.1016/j.ast.2018.01.025
[6]	A. Khatab, C. Diallo, U. Venkatadri, Z. Liu, E. H. Aghezzaf, Optimization of the joint selective maintenance and repairperson assignment problem under imperfect maintenance, Comput. Ind. Eng., 125 (2018), 413–422. https://doi.org/10.1016/j.cie.2018.09.012 doi: 10.1016/j.cie.2018.09.012
[7]	E. H. Aghezzaf, C. Diallo, A. Khatab, U. Venkatadri, A joint selective maintenance and multiple repairperson assignment problem, Proc. 7th Int. Conf. Ind. Eng. & Syst. Manag., (2017), 317–322.
[8]	X. Y. Wang, Y. Tian, X. M. Qiang, J. Qian, Mission assignment model for anti-missile combat based on cooperative efficiency, J. Air Force Eng. Univ., 14 (2013), 27–31. https://doi.org/10.3969/j.issn.1009-3516.2013.04.007 doi: 10.3969/j.issn.1009-3516.2013.04.007
[9]	X. N. Zhu, X. P. Zhu, R. Yan, R. Peng, Optimal routing, aborting and hitting strategies of UAVs executing hitting the targets considering the defense range of targets, Rel. Eng Syst. Safety, 215 (2021), 107811. https://doi.org/10.1016/j.ress.2021.107811 doi: 10.1016/j.ress.2021.107811
[10]	C. Diallo, U. Venkatadri, A. Khatab, Z. Liu, E. H. Aghezzaf, Optimal joint selective imperfect maintenance and multiple repairpersons assignment strategy for complex multicomponent systems, Int. J. Prod. Res., 57 (2019), 4098–4117. https://doi.org/10.1080/00207543.2018.1505060 doi: 10.1080/00207543.2018.1505060
[11]	T. Jiang, Y. Liu, Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty, Rel. Eng. & Syst. Safety, 193 (2020), 106632. https://doi.org/10.1016/j.ress.2019.106632 doi: 10.1016/j.ress.2019.106632
[12]	Z. Y. Zhen, P. Zhu, Y. X. Xue, Y. X. Ji, Distributed intelligent self-organized mission planning of multi-UAV for dynamic targets cooperative search-attack, Chin. J. Aeronaut., 32 (2019), 2706–2716. https://doi.org/10.1016/j.cja.2019.05.012 doi: 10.1016/j.cja.2019.05.012
[13]	M. H. Wang, Z. C. Zhou, R. Zhang, G. S. Chen, Firepower distribution model of amphibious attack ship for air self-defense combat, Fire Contr. Command Contr., 45 (2020), 127–131. https://doi.org/10.3969/j.issn.1002-0640.2020.12.024 doi: 10.3969/j.issn.1002-0640.2020.12.024
[14]	W. N. Ma, Q. W. Hu, J. Chen, X. S. Jia, Joint optimization of selective maintenance decision-making and task allocation for equipment groups, Acta Armamentarii, 45 (2024), 407–416. https://doi.org/10.12382/bgxb.2022.0649 doi: 10.12382/bgxb.2022.0649
[15]	A. Amjadian, R. O'Neil, A. Khatab, J. Chen, U. Venkatadri, C. Diallo, Optimising resource-constrained fleet selective maintenance with asynchronous maintenance breaks, Int. J. Prod. Res., 63 (2025), 2385–2407. https://doi.org/10.1080/00207543.2024.2403114 doi: 10.1080/00207543.2024.2403114
[16]	Y. P. Zhang, Z. J. Su, L. Shi, M. Y. Zhang, Optimization method for maintenance decision of complex systems based on nested particle swarm optimization. Comput. Integr. Manuf. Syst., 29 (2023), 3800–3811. https://doi.org/10.13196/j.cims.2021.0399 doi: 10.13196/j.cims.2021.0399
[17]	K. Chaabane, A. Khatab, C. Diallo, E. H. Aghezzaf, U. Venkatadri, Integrated imperfect multimission selective maintenance and repairpersons assignment problem, Rel. Eng. Syst. Safety, 199 (2020), 106895. https://doi.org/10.1016/j.ress.2020.106895 doi: 10.1016/j.ress.2020.106895
[18]	K. Moghaddam, J. Usher, A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems, Eng. Optim., 43 (2011), 701–719. https://doi.org/10.1080/0305215X.2010.512084 doi: 10.1080/0305215X.2010.512084
[19]	R. O'Neil, A. Khatab, C. Diallo, A. Saif, Joint selective maintenance and mission abort decisions for mission-critical systems, Rel. Eng. Syst. Safety, 264 (2025), 111358. https://doi.org/10.1016/j.ress.2025.111358 doi: 10.1016/j.ress.2025.111358
[20]	Y. Liu, Reliability modelling and maintenance decision-making for multi-state complex systems, Univ. Electron. Sci. Technol. China, (2010). https://doi.org/10.7666/d.Y1851647 doi: 10.7666/d.Y1851647
[21]	L. Cao, Z. T. Wang, L. Chen, H. S. Li, S. Gao, Z. L. Zhang, Neural network ensemble based on improved quantum immune algorithm, Comput. Eng. Appl., 56 (2020), 142–147. https://doi.org/10.3778/j.issn.1002-8331.2005-0357 doi: 10.3778/j.issn.1002-8331.2005-0357
[22]	Y. Y. Li, L. C. Jiao, Quantum immune clone multi-objective optimization algorithm, J. Electron. Inf. Technol., 6 (2008), 1367–1371. https://doi.org/10.3724/SP.J.1146.2006.01709 doi: 10.3724/SP.J.1146.2006.01709
[23]	Y. Jing, Y. K. Liu, M. K. Bi, Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency, Cluster Comput., 22 (2019), 545–554. https://doi.org/10.1007/s10586-017-1292-7 doi: 10.1007/s10586-017-1292-7
[24]	X. H. Liu, M. Y. Shan, R. L. Zhang, L. H. Zhang, Green vehicle routing optimization based on carbon emission and multi-objective hybrid quantum immune algorithm, Math. Probl. Eng., 4 (2018), 1–9. https://doi.org/10.1155/2018/8961505 doi: 10.1155/2018/8961505
[25]	T. Zhang, Y. S. Liu, Integrated task priority scheduling using genetic particle swarm algorithm, Radar ECM, 45 (2025), 1–6. https://doi.org/10.19341/j.cnki.1009-0401.2025.03.001 doi: 10.19341/j.cnki.1009-0401.2025.03.001
[26]	D. Ni, Research on a hybrid metaheuristic scheduling algorithm for multi-station robot handling of lithium batteries, Electron. Meas. Technol., 48 (2025), 128–135. https://doi.org/10.19651/j.cnki.emt.2518027 doi: 10.19651/j.cnki.emt.2518027

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