Joint optimization of location and allocation for spare parts depots under ($ r, Q $) inventory policy

Yaojun Liu; Li Jia; Ping Wang; Xiaolin Song; Yaojun Liu; Li Jia; Ping Wang; Xiaolin Song

doi:10.3934/nhm.20240046

Networks and Heterogeneous Media

2024, Volume 19, Issue 3: 1038-1057. doi: 10.3934/nhm.20240046

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Research article

Joint optimization of location and allocation for spare parts depots under ($ r, Q $) inventory policy

1.
Wuhu State-Owned Factory of Machining, Wuhu 241000, China
2.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210002, China
3.
China Academy of Launch Vehicle Technology Beijing, Beijing 100076, China

Received: 05 July 2024 Revised: 02 August 2024 Accepted: 10 September 2024 Published: 29 September 2024

The ability to replace failed spare parts in time directly affects the supportability level of equipment systems. The selection of spare parts' depot locations, inventory mode, and allocation are often separate and independent operations. However, in these situations, the total supply cost is usually relatively high with the consideration of spare parts shortage and maintenance delays. Therefore, this article dealt with a depot location-inventory-allocation problem based on the $ (r, Q) $ inventory method and analyzed a combined network of centralized spare part depot locations, inventory, and allocation. Meanwhile, considering the convenience and speed of spare parts transportation brought about by the improvement of transportation capacity, a network is proposed to adopt a centralized storage and point-to-point allocation strategy for parts replacement, which reduces supportability costs without affecting supply efficiency. An optimization model has been developed that reduces the overall cost of support, including inventory, construction, transportation, and logistics. Three equipment support efficiency metrics were used as constraints in this model to assess the location of open depots: selection availability, fill rate, and predicted downtime. Additionally, due to the knowledge asymmetry, there are some shortage issues which always lead to extra expenditure. The model also introduces uncertain distribution to demand measurement and adopts a genetic algorithm for model solving. Ultimately, a numerical instance was developed so as to verify our results.
- location-inventory-allocation optimization,
- spare parts supply,
- system supportability,
- genetic algorithm,
- uncertainty theory
Citation: Yaojun Liu, Li Jia, Ping Wang, Xiaolin Song. Joint optimization of location and allocation for spare parts depots under ($ r, Q $) inventory policy[J]. Networks and Heterogeneous Media, 2024, 19(3): 1038-1057. doi: 10.3934/nhm.20240046

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Abstract

The ability to replace failed spare parts in time directly affects the supportability level of equipment systems. The selection of spare parts' depot locations, inventory mode, and allocation are often separate and independent operations. However, in these situations, the total supply cost is usually relatively high with the consideration of spare parts shortage and maintenance delays. Therefore, this article dealt with a depot location-inventory-allocation problem based on the $ (r, Q) $ inventory method and analyzed a combined network of centralized spare part depot locations, inventory, and allocation. Meanwhile, considering the convenience and speed of spare parts transportation brought about by the improvement of transportation capacity, a network is proposed to adopt a centralized storage and point-to-point allocation strategy for parts replacement, which reduces supportability costs without affecting supply efficiency. An optimization model has been developed that reduces the overall cost of support, including inventory, construction, transportation, and logistics. Three equipment support efficiency metrics were used as constraints in this model to assess the location of open depots: selection availability, fill rate, and predicted downtime. Additionally, due to the knowledge asymmetry, there are some shortage issues which always lead to extra expenditure. The model also introduces uncertain distribution to demand measurement and adopts a genetic algorithm for model solving. Ultimately, a numerical instance was developed so as to verify our results.

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