{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:56:04Z","timestamp":1760234164673,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In order to rationally lay out the location of automobile maintenance service stations, a method of location selection of maintenance service stations based on vehicle trajectory big data is proposed. Taking the vehicle trajectory data as the demand points, the demand points are divided according to the region by using the idea of zoning, and the location of the second-level maintenance station is selected for each region. The second-level maintenance stations selected in the whole country are set as the demand points of the first-level maintenance stations. Considering the objectives of the two dimensions of cost and service level, the location model of the first-level maintenance stations under two-dimensional programming is established, and the improved particle swarm optimization algorithm and immune algorithm, respectively, are used to solve the problem. In this way, the first-level maintenance stations in each region are obtained. The example verification shows that the location selection results for the maintenance stations using the vehicle trajectory big data are reasonable and closer to the actual needs.<\/jats:p>","DOI":"10.3390\/e23050495","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T12:47:07Z","timestamp":1619009227000},"page":"495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on Multi-Dimensional Optimal Location Selection of Maintenance Station Based on Big Data of Vehicle Trajectory"],"prefix":"10.3390","volume":"23","author":[{"given":"Shoujing","family":"Zhang","sequence":"first","affiliation":[{"name":"Xi\u2019an Key Laboratory of Modern Intelligent Textile Equipment, College of Mechanical and Electrical Engineering, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Fujiao","family":"Tong","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Modern Intelligent Textile Equipment, College of Mechanical and Electrical Engineering, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Mengdan","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Modern Intelligent Textile Equipment, College of Mechanical and Electrical Engineering, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Shoufeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Modern Intelligent Textile Equipment, College of Mechanical and Electrical Engineering, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}]},{"given":"Zhixiong","family":"Li","sequence":"additional","affiliation":[{"name":"Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"},{"name":"School of Engineering, Ocean University of China, Qingdao 266100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.cie.2016.09.001","article-title":"Multi-period distribution center location and scale decision in supply chain network","volume":"101","author":"Zhuge","year":"2016","journal-title":"Comput. 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