{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T06:05:20Z","timestamp":1771999520914,"version":"3.50.1"},"reference-count":46,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2025,1,24]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs\u2019 advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&amp;G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem\u2019s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&amp;G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-06-2024-0518","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T08:49:16Z","timestamp":1731660556000},"page":"627-662","source":"Crossref","is-referenced-by-count":3,"title":["An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle"],"prefix":"10.1108","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6669-1690","authenticated-orcid":false,"given":"Kai","family":"Li","sequence":"first","affiliation":[]},{"given":"Cheng","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0690-9787","authenticated-orcid":false,"given":"Jianjiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Junhui","family":"Gao","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"issue":"6","key":"key2025012407523250200_ref001","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1109\/mce.2021.3063945","article-title":"Architecture, classification, and applications of contemporary unmanned aerial vehicles","volume":"10","year":"2021","journal-title":"IEEE Consumer Electronics Magazine"},{"key":"key2025012407523250200_ref002","first-page":"457","article-title":"Decentralized UAV swarm control\u00a0for\u00a0multitarget tracking using approximate dynamic programming","year":"2021"},{"issue":"6","key":"key2025012407523250200_ref003","doi-asserted-by":"publisher","first-page":"2853","DOI":"10.1109\/taes.2018.2831138","article-title":"Multi-UAV task assignment with parameter and time-sensitive uncertainties using modified two-part Wolf pack search algorithm","volume":"54","year":"2018","journal-title":"IEEE Transactions\u00a0on Aerospace and Electronic Systems"},{"issue":"5","key":"key2025012407523250200_ref004","doi-asserted-by":"publisher","first-page":"3032","DOI":"10.1109\/tii.2020.3004816","article-title":"Placement and routing optimization for automated inspection with unmanned aerial vehicles: a study in offshore wind farm","volume":"17","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"key2025012407523250200_ref047","volume-title":"Unmanned Systems Integrated Roadmap Fy2011 - 2036","year":"2015"},{"key":"key2025012407523250200_ref005","unstructured":"Department of Defenses (2012), \u201cThe role of autonomy in DoD systems[R]\u201d, in D. 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