Joint optimization of resources inventory allocation under hyper-heuristic algorithm

doi:10.23919/JSEE.2026.000114

Abstract

Abstract:

In this paper, the system we consider has multiple inventory warehouses and multiple pieces of equipment with multiple repairable components, where the joint planning of spare components and maintenance workers with lateral and cross-echelon transshipment is studied. Firstly, the characteristics of inventory system is analyzed, and the scheduled relationship of maintenance resources is carded. Based on this, a total system cost model is proposed, incorporating holding, ordering, and maintenance costs under an average waiting time constraint. A hyper-heuristic algorithm is then introduced to efficiently solve larger-scale problems with improved computational speed, and is applied to derive an optimized inventory allocation plan for maintenance resources. Finally, a maintenance system is analyzed, comprising four local warehouses, three central warehouses, and one plant that serves five machine groups. Each group contains four machines, each warehouse supports one or two machines, and every machine includes five independently failing key components. By analyzing the effect on reducing total cost, improving maintenance demand satisfaction rate, the effectiveness of the proposed optimization approach is verified.

Key words: hyper-heuristic algorithm, maintenance resource, inventory allocation, joint planning

Bowen CUI, Xiaochuang TAO, Wenhui ZHAO, Yanbin YUAN, Huanzhen FAN. Joint optimization of resources inventory allocation under hyper-heuristic algorithm[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 993-1001.

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References 30

1	KEIZER M, TEUNTER R H. Clustering condition-based maintenance for a multi-unit system with aperiodic inspections. Proc. of the International Conference, 2015: 983–991.
2	SHAO S, NIU K Z, QI X Y, et al Human resource allocation status and equity research of Centers for Disease Control and Prevention in China from 2016 to 2020. Frontiers in Public Health, 2024, 12, 1382343. doi: 10.3389/fpubh.2024.1382343
3	FENG T, LIU B, REN H R, et al A resource bi-level optimization model of water-energy-food-carbon nexus in a regional agricultural system under multi-stakeholder conditions. Renewable Energy, 2025, 196 (1): 1- 12.
4	FROGER A, GENDREAU M, MENDOZA J, et al Maintenance scheduling in the electricity industry: a literature review. European Journal of Operational Research, 2016, 251 (3): 695- 706. doi: 10.1016/j.ejor.2015.08.045
5	ZHANG X, ZENG J Joint optimization of condition-based opportunistic maintenance and spare components provisioning policy in multiunit systems. European Journal of Operational Research, 2017, 262, 1- 10. doi: 10.1016/j.ejor.2017.03.019
6	WEB J Machine learning, healthcare resource allocation, and patient consent. The New Bioethics, 2024, 30 (3): 206- 227. doi: 10.1080/20502877.2024.2416858
7	MEISSNER J, SENICHEVA O V Approximate dynamic programming for lateral transshipment problems in multi-location inventory systems. European Journal of Operational Research, 2017, 265 (1): 46- 64. doi: 10.1016/j.ejor.2017.06.049
8	VAN WIJK A, ADAN J, VAN HOUTUM G, et al Approximate evaluation of multi-location inventory models with lateral transshipments and hold back level. European Journal of Operational Research, 2012, 218 (3): 624- 635. doi: 10.1016/j.ejor.2011.12.004
9	ZHA J, JIN H, ZHU Y, et al. Multi-objective integrated optimization problem of preventive maintenance planning and flexible job-shop scheduling. Proc. of the 23rd International Conference on Industrial Engineering and Engineering Management , 2017: 137–141.
10	RAHIMI-GHAHROODI S, AL HANBALI A, ZIJM W H M, et al Integrated planning of spare components and service engineers with partial backlogging. OR Spectrum, 2017, 39 (3): 711- 748. doi: 10.1007/s00291-017-0473-3
11	SMIDT-DESTOMBES D, KARIN S, HEIJDEN V D, et al Joint optimization of spare part inventory, maintenance frequency and repair capacity for k-out-of-N systems. International Journal of Production Economics, 2009, 118 (1): 260- 268. doi: 10.1016/j.ijpe.2008.08.058
12	DE SMIDT-DESTOMBES K S D, HEIJDEN M C V D, HARTEN A V, et al Availability of k-out-of-N systems under block replacement sharing limited spares and repair workers. International Journal of Production Economics, 2007, 107 (2): 404- 421. doi: 10.1016/j.ijpe.2006.08.013
13	DE JONGE B, KLINGENBERG W, TEUNTER R, et al Reducing cost by clustering maintenance activities for multiple critical units. Reliability Engineering and System Safety, 2016, 145, 93- 103. doi: 10.1016/j.ress.2015.09.003
14	JIN T D, TIAN Z G, XIE M, et al A game-theoretical approach for optimizing maintenance, spares and service capacity in performance contracting. International Journal of Production Economics, 2015, 161, 31- 43. doi: 10.1016/j.ijpe.2014.11.010
15	OTHMAN S B, ZGAYA H, DOTOLI M, et al An agent-based decision support system for resources’ scheduling in emergency supply chains. Control Engineering Practice, 2017, 59, 27- 43. doi: 10.1016/j.conengprac.2016.11.014
16	WANG X Y, CHOI T M, LIU H K, et al A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster scenarios. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2018, 48 (4): 545- 556. doi: 10.1109/TSMC.2016.2606440
17	HUA J G, TIAN M, ZHAO Y, et al Study on the mitigation effect and promotion mechanism of agricultural digitalization on the agricultural land resource mismatch. Agriculture, 2024, 14 (6): 913. doi: 10.3390/agriculture14060913
18	YU J S, ZHOU J, CHEN R, et al Agroecological risk assessment based on coupling of water and land resources-a case of Heihe River Basin. Land, 2023, 12 (4): 794. doi: 10.3390/land12040794
19	HARTANTO W, DIRK V O, DIRK C Two-echelon multi-item spare components systems with emergency supply flexibility and waiting time constraints. IIE Transactions, 2007, 39 (11): 1- 13. doi: 10.1080/07408170601091873
20	AMELIA V, SINAGA S, BHERMANA A, et al. Land resource management on environment and sustained basis for agricultural land use planning using landform and land evaluation approach (a case study in North Barito District, Central Kalimantan Province). Proc. of the 3rd International Symposium on Tropical Forestry and Environmental Sciences, 2023: 012001.
21	WONG H, VAN H G, CATTRYSSE D, et al Efficient heuristics for multi-item multi-location spare components systems with lateral transshipments and waiting time constraints. Journal of the Operational Research Society, 2005, 56, 1419- 1430. doi: 10.1057/palgrave.jors.2601952
22	VAN H G, KRANENBURG B. Spare components inventory control under system availability constraints. Berlin: Springer, 2015.
23	AVSAR Z, ZIJM W, RODOPLU U, et al An approximate model for base-stock-controlled assembly systems. IIE Transactions, 2009, 41 (3): 260- 274. doi: 10.1080/07408170802510382
24	HOEN KM, GÜLLÜ R, VAN HOUTUM G, et al A simple and accurate approximation for the order fill rates in lost-sales assemble-to-order systems. International Journal of Production Economics, 2011, 133 (1): 95- 104. doi: 10.1016/j.ijpe.2009.12.012
25	MANZINI R, ACCORSI R, CENNERAZZO T, et al. The scheduling of maintenance. A resource-constraints mixed integer linear programming model. Computers & Industrial Engineering, 2015, 87: 561–568.
26	MOGHADDAM K S Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming. International Journal of Production Economics, 2013, 146 (2): 704- 716. doi: 10.1016/j.ijpe.2013.08.027
27	MENG Y K, WANG Z, FAN J L, et al Scheduling optimization of uncertain carrier aircraft support based on tabu algorithm. Journal of System Simulation, 2021, 10, 1- 8.
28	LIU S Y, QI X G, LIU L F, et al Multi-objective joint optimization with maintenance resource allocation and task scheduling. Acta Ordnance Engineering, 2024, 45 (7): 2442- 2450.
29	ZHU X D, ZHAO Y, FAN J L, et al A one-stop joint optimization method of resource performance index based on hierarchical optimization strategy. Chinese Ships Research, 2024, 17 (5): 92- 103.
30	LIU Q, FAN Z W, ZHANG C Y, et al Mixed model assembly line scheduling problem based on multi-objective cat group. Computers & Industrial Engineering, 2020, 12, 131- 139.

i	λ_i	MTTR/min	Storage cost
1	0.029	6	3
2	0.062	5	4
3	0.026	6	3
4	0.091	3	6
5	0.065	4	5

Type	Quantity	Hired factor	Hired cost
General	10	0.22	12
Skilled	8	0.18	15

N	SMP	P	SRD	CDC	max Ti	v_x	v_y
200	5	5	0.15	4	200	50	30

Optimization algorithm	Total cost/yuan	Average waiting time/s	Running time/s
Greedy algorithm	36756.52	3.32	45.63
Cat group algorithm	35029.93	4.23	28.32
Hyper-heuristic algorithm	31752.45	3.52	13.98

Number of repairable component	Number of the local inventory depots
Number of repairable component	4	5	6	7	8
5	13.98	73.14	469.83	1686.34	6210.27
8	28.68	187.38	917.36	3678.39	16829.12
15	67.23	328.21	1563.81	7839.33	24829.37