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Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains

This paper presents a 0–1 programming model aimed at obtaining the optimal inventory policy and transportation mode for maintenance spare parts of high-speed trains. To obtain the model parameters for occasionally-replaced spare parts, a demand estimation method based on the maintenance strategies o...

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Detalles Bibliográficos
Autores principales: Lin, Boliang, Wang, Jiaxi, Wang, Huasheng, Wang, Zhongkai, Li, Jian, Lin, Ruixi, Xiao, Jie, Wu, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417615/
https://www.ncbi.nlm.nih.gov/pubmed/28472097
http://dx.doi.org/10.1371/journal.pone.0176961
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author Lin, Boliang
Wang, Jiaxi
Wang, Huasheng
Wang, Zhongkai
Li, Jian
Lin, Ruixi
Xiao, Jie
Wu, Jianping
author_facet Lin, Boliang
Wang, Jiaxi
Wang, Huasheng
Wang, Zhongkai
Li, Jian
Lin, Ruixi
Xiao, Jie
Wu, Jianping
author_sort Lin, Boliang
collection PubMed
description This paper presents a 0–1 programming model aimed at obtaining the optimal inventory policy and transportation mode for maintenance spare parts of high-speed trains. To obtain the model parameters for occasionally-replaced spare parts, a demand estimation method based on the maintenance strategies of China’s high-speed railway system is proposed. In addition, we analyse the shortage time using PERT, and then calculate the unit time shortage cost from the viewpoint of train operation revenue. Finally, a real-world case study from Shanghai Depot is conducted to demonstrate our method. Computational results offer an effective and efficient decision support for inventory managers.
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spelling pubmed-54176152017-05-14 Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains Lin, Boliang Wang, Jiaxi Wang, Huasheng Wang, Zhongkai Li, Jian Lin, Ruixi Xiao, Jie Wu, Jianping PLoS One Research Article This paper presents a 0–1 programming model aimed at obtaining the optimal inventory policy and transportation mode for maintenance spare parts of high-speed trains. To obtain the model parameters for occasionally-replaced spare parts, a demand estimation method based on the maintenance strategies of China’s high-speed railway system is proposed. In addition, we analyse the shortage time using PERT, and then calculate the unit time shortage cost from the viewpoint of train operation revenue. Finally, a real-world case study from Shanghai Depot is conducted to demonstrate our method. Computational results offer an effective and efficient decision support for inventory managers. Public Library of Science 2017-05-04 /pmc/articles/PMC5417615/ /pubmed/28472097 http://dx.doi.org/10.1371/journal.pone.0176961 Text en © 2017 Lin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lin, Boliang
Wang, Jiaxi
Wang, Huasheng
Wang, Zhongkai
Li, Jian
Lin, Ruixi
Xiao, Jie
Wu, Jianping
Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title_full Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title_fullStr Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title_full_unstemmed Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title_short Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
title_sort inventory-transportation integrated optimization for maintenance spare parts of high-speed trains
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417615/
https://www.ncbi.nlm.nih.gov/pubmed/28472097
http://dx.doi.org/10.1371/journal.pone.0176961
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