Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization
Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/21/3454 |
| _version_ | 1797468498346115072 |
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| author | Yurong Guo Quan Shi Chiming Guo |
| author_facet | Yurong Guo Quan Shi Chiming Guo |
| author_sort | Yurong Guo |
| collection | DOAJ |
| description | Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm. |
| first_indexed | 2024-03-09T19:08:18Z |
| format | Article |
| id | doaj.art-5e946af8dd8140a68e76d4663eabb88c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T19:08:18Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5e946af8dd8140a68e76d4663eabb88c2023-11-24T04:24:12ZengMDPI AGElectronics2079-92922022-10-011121345410.3390/electronics11213454Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm OptimizationYurong Guo0Quan Shi1Chiming Guo2Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, ChinaShijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, ChinaShijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, ChinaSpare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm.https://www.mdpi.com/2079-9292/11/21/3454dynamic optimizationnonlinear programmingmeta-heuristicspare part managementinventory control policy |
| spellingShingle | Yurong Guo Quan Shi Chiming Guo Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization Electronics dynamic optimization nonlinear programming meta-heuristic spare part management inventory control policy |
| title | Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization |
| title_full | Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization |
| title_fullStr | Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization |
| title_full_unstemmed | Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization |
| title_short | Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization |
| title_sort | multi period spare parts supply chain network optimization under t s s inventory control policy with improved dynamic particle swarm optimization |
| topic | dynamic optimization nonlinear programming meta-heuristic spare part management inventory control policy |
| url | https://www.mdpi.com/2079-9292/11/21/3454 |
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