A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands
This paper copes with a joint Location-Allocation-Inventory problem in a three-echelon base-level spare part support system with epistemic uncertainty in uncertain demands of bases. The aim of the paper is to propose an optimization model under the uncertainty theory to minimize the total cost, whic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/1/46 |
| _version_ | 1797446137441943552 |
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| author | Peixuan Li Meilin Wen Tianpei Zu Rui Kang |
| author_facet | Peixuan Li Meilin Wen Tianpei Zu Rui Kang |
| author_sort | Peixuan Li |
| collection | DOAJ |
| description | This paper copes with a joint Location-Allocation-Inventory problem in a three-echelon base-level spare part support system with epistemic uncertainty in uncertain demands of bases. The aim of the paper is to propose an optimization model under the uncertainty theory to minimize the total cost, which integrates crucial characterizations of the inventory control decisions and the location-allocation scheme arrangement under a periodic review order-up-to-S (T, S) policy. Uncertainty theory is introduced in this paper to characterize epistemic uncertainty, where demands are treated as uncertain variables and stockout loss is represented by value-at-risk in uncertain measurement. To solve the original uncertain optimization model, an equivalent deterministic model is derived and addressed by an improved bilevel genetic algorithm. Moreover, the proposed models and algorithm are encoded into numerical examples for supply chain programming. The results highlight the applicability of the model and the algorithm’s effectiveness in approaching the optimal solution compared with traditional genetic algorithm. Sensitivity analyses are further made for the impacts of review time and inventory capacity on different cost components. |
| first_indexed | 2024-03-09T13:35:54Z |
| format | Article |
| id | doaj.art-44ef70cbb65f4191b1c32137f12c43cb |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-09T13:35:54Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-44ef70cbb65f4191b1c32137f12c43cb2023-11-30T21:11:34ZengMDPI AGAxioms2075-16802023-01-011214610.3390/axioms12010046A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain DemandsPeixuan Li0Meilin Wen1Tianpei Zu2Rui Kang3School of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaScience and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaThis paper copes with a joint Location-Allocation-Inventory problem in a three-echelon base-level spare part support system with epistemic uncertainty in uncertain demands of bases. The aim of the paper is to propose an optimization model under the uncertainty theory to minimize the total cost, which integrates crucial characterizations of the inventory control decisions and the location-allocation scheme arrangement under a periodic review order-up-to-S (T, S) policy. Uncertainty theory is introduced in this paper to characterize epistemic uncertainty, where demands are treated as uncertain variables and stockout loss is represented by value-at-risk in uncertain measurement. To solve the original uncertain optimization model, an equivalent deterministic model is derived and addressed by an improved bilevel genetic algorithm. Moreover, the proposed models and algorithm are encoded into numerical examples for supply chain programming. The results highlight the applicability of the model and the algorithm’s effectiveness in approaching the optimal solution compared with traditional genetic algorithm. Sensitivity analyses are further made for the impacts of review time and inventory capacity on different cost components.https://www.mdpi.com/2075-1680/12/1/46Location-Allocation-Inventorybase-level spare partuncertainty theorybilevel genetic algorithm |
| spellingShingle | Peixuan Li Meilin Wen Tianpei Zu Rui Kang A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands Axioms Location-Allocation-Inventory base-level spare part uncertainty theory bilevel genetic algorithm |
| title | A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands |
| title_full | A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands |
| title_fullStr | A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands |
| title_full_unstemmed | A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands |
| title_short | A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands |
| title_sort | joint location allocation inventory spare part optimization model for base level support system with uncertain demands |
| topic | Location-Allocation-Inventory base-level spare part uncertainty theory bilevel genetic algorithm |
| url | https://www.mdpi.com/2075-1680/12/1/46 |
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