{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:39:10Z","timestamp":1774640350235,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Foundation of China","award":["21BGL176"],"award-info":[{"award-number":["21BGL176"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Effective emergency logistics facility site selection is vital for ensuring prompt and fair food supply during crises. This study tackles the intricate task of choosing optimal sites for emergency food logistics facilities by considering varying urgency levels of needs, uncertain demands, and potential facility interruptions. A novel weighted Mahalanobis distance\u2013gray relational analysis\u2013TOPSIS method is devised to evaluate demand urgency and guide site selection decisions. The proposed location model aims to minimize total cost and unmet demand while integrating discrete scenario strategies to address interruption events. Leveraging the Social Network Search (SNS) algorithm, the model is solved, and its effectiveness is validated through a case study analysis. The results highlight the accuracy of the urgency level determination method in capturing demand characteristics and the model\u2019s provision of an objective and practical framework for formulating rational facility location strategies. This approach holds significant promise for enhancing the promptness and fairness of food supply assurance during emergencies.<\/jats:p>","DOI":"10.3390\/systems12070241","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T12:30:59Z","timestamp":1720182659000},"page":"241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Facilities Sites Selection Optimization for Food Emergency Logistics to Meet Urgent Demands"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8654-7335","authenticated-orcid":false,"given":"Xiaoqing","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","first-page":"148","article-title":"Integrated \u201cCapability-Timeliness-Decision\u201d Emergency Response Effectiveness Pre-Assessment Model","volume":"30","author":"Ni","year":"2020","journal-title":"China Saf. 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