收藏切换
Facility location of material reserve bases for large railway projects under uncertainty
收藏切换
Jin ZHANG1, 2, 3, Wenguang YANG1, Wenjie SUN1, 2, 3, Hao SHEN1, 2, 3, **, Zhichao HONG1, 2, 3, Guoqi LI1, 2, 3
China Safety Science Journal | 2024, 34(6) : 197 - 206
Less
收藏切换
China Safety Science Journal | 2024, 34(6): 197-206
• Emergency technology and management •
Facility location of material reserve bases for large railway projects under uncertainty
Full
Jin ZHANG1, 2, 3, Wenguang YANG1, Wenjie SUN1, 2, 3, Hao SHEN1, 2, 3, **, Zhichao HONG1, 2, 3, Guoqi LI1, 2, 3
Affiliations
  • 1 School of Transportation and Logistics,Southwest Jiaotong University,Chengdu Sichuan 611756,China
  • 2 National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu Sichuan 611756,China
  • 3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu Sichuan 611756,China
Published: 2024-06-28 doi: 10.16265/j.cnki.issn1003-3033.2024.06.0814
Outline
收藏切换

In order to improve the reliability of the logistics facility network of railway construction projects in complex environments,scenario reduction techniques were used to generate a minimum subset of disruption scenarios and their disruption probabilities to describe the disruption scenarios of transport channels. The polyhedral uncertainty sets were used to describe the uncertainty of logistics demand. To minimize the combined costs of transport,construction,operation and penalty costs,a two-stage stochastic and robust optimisation technique was applied to construct an uncertainty optimisation model for the location of material reserves bases. The model was solved based on a C&CG algorithm. The validity of the model and the algorithm was verified by taking a C railway construction project in a complex environment as an example. The results show that the cost variation coefficient of the model-acquired solutions is 4.3% of the traditional model in the random disruption scenario,and the cost fluctuation of the model-acquired solutions can be up to 38% of that of the traditional model in the extreme demand fluctuation. The two-stage uncertainty optimisation model given in this paper can effectively reduce the cost variation of the logistics facility network resulting from the disruption of transport channels and demand fluctuations.

railway projects  /  material reserve bases  /  facility location  /  engineering logistics  /  scenario reduction  /  robust optimization  /  column and constraint generation (C&CG) algorithm
张锦, 杨文广, 孙文杰, 申皓, 洪治潮, 李国旗. 不确定条件下大型铁路建设工程的物资储备基地选址优化. 中国安全科学学报, 2024 , 34 (6) : 197 -206 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.0814
Jin ZHANG, Wenguang YANG, Wenjie SUN, Hao SHEN, Zhichao HONG, Guoqi LI. Facility location of material reserve bases for large railway projects under uncertainty[J]. China Safety Science Journal, 2024 , 34 (6) : 197 -206 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.0814
Year 2024 volume 34 Issue 6
1019
472
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.06.0814
  • Receive Date:2023-12-12
  • Online Date:2025-07-09
  • Published:2024-06-28
Article Data
Affiliations
History
  • Received:2023-12-12
  • Revised:2024-03-15
Funding
Affiliations
    1 School of Transportation and Logistics,Southwest Jiaotong University,Chengdu Sichuan 611756,China
    2 National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu Sichuan 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu Sichuan 611756,China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2024.06.0814
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT