{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:35:32Z","timestamp":1768833332991,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem faces challenges, such as coordinating the allocation and distribution of essential resources while considering the severity of the disaster, population density, and accessibility. This study proposes an optimized disaster relief management model, including distribution center placement, demand point prediction, prohibited route mapping, and efficient relief goods distribution. A dynamic model predicts the location of post-disaster distribution centers using the K-Means method based on impacted demand points\u2019 positions. Artificial Neural Networks (ANN) aid in predicting assistance requests around formed distribution centers. The forbidden route model maps permitted and prohibited routes while considering constraints to enhance relief supply distribution efficacy. The objective function aims to minimize both cost and time in post-disaster aid distribution. The model deep location routing problem (DLRP) effectively handles mixed nonlinear multi-objective programming, choosing the best forbidden routes. The combination of these models provides a comprehensive framework for optimizing disaster relief management, resulting in more effective and responsive disaster handling. Numerical examples show the model\u2019s effectiveness in solving complex humanitarian logistics problems with lower computation time, which is crucial for quick decision making during disasters.<\/jats:p>","DOI":"10.3390\/a16100468","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:58:57Z","timestamp":1696420737000},"page":"468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Decision-Making Model to Determine Dynamic Facility Locations for a Disaster Logistic Planning Problem Using Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Lili","family":"Tanti","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan 20155, Indonesia"}]},{"given":"Syahril","family":"Efendi","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan 20155, Indonesia"}]},{"given":"Maya Silvi","family":"Lydia","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan 20155, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-9415","authenticated-orcid":false,"given":"Herman","family":"Mawengkang","sequence":"additional","affiliation":[{"name":"Faculty of Math and Science, Universitas Sumatera Utara, Medan 20155, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"ref_1","first-page":"1105839","article-title":"Critical Decision-Making Issues in Disaster Relief Supply Management: A Review","volume":"2022","author":"Ma","year":"2022","journal-title":"Comput. 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