Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions
Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to...
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MDPI AG
2025-01-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/1/49 |
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| author | Shuiwang Zhang Chuansheng Zhou |
| author_facet | Shuiwang Zhang Chuansheng Zhou |
| author_sort | Shuiwang Zhang |
| collection | DOAJ |
| description | Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience. |
| format | Article |
| id | doaj-art-e11dd1032aa14c0facf29ceff33820e0 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-e11dd1032aa14c0facf29ceff33820e02025-01-24T13:50:37ZengMDPI AGSystems2079-89542025-01-011314910.3390/systems13010049Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought ConditionsShuiwang Zhang0Chuansheng Zhou1School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, ChinaSchool of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, ChinaGrain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience.https://www.mdpi.com/2079-8954/13/1/49grain supply-chain resiliencecomplex scenario analysisnode identificationBayesian networkD–S evidence theory |
| spellingShingle | Shuiwang Zhang Chuansheng Zhou Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions Systems grain supply-chain resilience complex scenario analysis node identification Bayesian network D–S evidence theory |
| title | Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions |
| title_full | Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions |
| title_fullStr | Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions |
| title_full_unstemmed | Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions |
| title_short | Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions |
| title_sort | identifying key nodes and enhancing resilience in grain supply chains under drought conditions |
| topic | grain supply-chain resilience complex scenario analysis node identification Bayesian network D–S evidence theory |
| url | https://www.mdpi.com/2079-8954/13/1/49 |
| work_keys_str_mv | AT shuiwangzhang identifyingkeynodesandenhancingresilienceingrainsupplychainsunderdroughtconditions AT chuanshengzhou identifyingkeynodesandenhancingresilienceingrainsupplychainsunderdroughtconditions |