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This study integrates regional restrictions into the heterogeneous truck\u2013drone routing problem, presenting a mixed-integer programming model for cost minimization. To tackle complexity, we introduce an enhanced gray wolf optimization algorithm (EGWO), which improves the initial solution through partition scanning and a heuristic insertion algorithm. EGWO effectiveness is confirmed through enhancements in the standard test library. On average, the heterogeneous truck\u2013drone model achieves a 28.31% cost reduction compared to the single-type truck delivery model. Moreover, deep insights into the impacts of multi-type trucks, the number of no-fly zones and the number of restricted traffic zones on the performance of the heterogeneous truck\u2013drone system are discussed. <\/jats:p>","DOI":"10.1142\/s0217595924400165","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T06:24:06Z","timestamp":1719642246000},"source":"Crossref","is-referenced-by-count":7,"title":["Collaborative Routing Optimization for Heterogeneous Trucks\u2013Drones Under Urban Regional Restrictions"],"prefix":"10.1142","volume":"42","author":[{"given":"Jiaojiao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Economics and Management, Southwest Jiaotong University, Chengdu, P. R. China"}]},{"given":"Xiuping","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, P. R. 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