{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:31:39Z","timestamp":1781533899168,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"crossref","award":["4038408"],"award-info":[{"award-number":["4038408"]}],"id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"crossref"}]},{"award":["4038408"],"award-info":[{"award-number":["4038408"]}],"id":[{"id":"https:\/\/ror.org\/03sr1ma14","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>In the wake of an earthquake, severe infrastructure disruption and limited access to affected areas pose serious challenges to the relief process. Therefore, developing efficient models for vehicle allocation and routing plays a crucial role in reducing response time and improving operational efficiency. In this study, a multi-objective routing model is proposed for a hybrid ground\u2013air transportation system, where trucks are responsible for covering accessible areas and drones are deployed to serve inaccessible locations. The model\u2019s objectives include reducing service time, distance travel, total cost, and fuel consumption. To solve the model, the \u03b5-constraint (epsilon-constraint) approach is used for small-scale problems, and a heuristic approach combining the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the nearest neighbors concept is used for large-scale problems. The computational results show that the proposed hybrid system can reduce response time and significantly improve cost and fuel consumption compared to the ground fleet-only scenario through the optimal assignment of routes and drone missions. The proposed hybrid model resulted in a reduction of approximately 15% in total cost, 12% in service time, and nearly 10% in fuel consumption compared to using the ground fleet alone. These findings demonstrate the effectiveness and efficiency of the proposed framework in post-crisis relief operations.<\/jats:p>","DOI":"10.3390\/systems14040449","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:53:23Z","timestamp":1776696803000},"page":"449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing Post-Earthquake Relief with Combined Ground and Air Routing: \u03b5-Constraint and NSGAII-Nearest Neighbor Approaches"],"prefix":"10.3390","volume":"14","author":[{"given":"Sogol","family":"Mousavi","sequence":"first","affiliation":[{"name":"Department of Industrial Management, Faculty of Management, Tehran University, Tehran 14395-796, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadreza","family":"Taghizadeh-Yazdi","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, Faculty of Management, Tehran University, Tehran 14395-796, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2139-2053","authenticated-orcid":false,"given":"Seyed Mojtaba","family":"Sajadi","sequence":"additional","affiliation":[{"name":"Operations and Service Management Department, Aston Business School, Aston University, Birmingham B4 7ET, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/gj.5072","article-title":"A comprehensive review of remote sensing technologies for improved geological disaster management","volume":"60","author":"Kumari","year":"2025","journal-title":"Geol. 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