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Therefore, green vehicle routing problem (GVRP), aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%.<\/jats:p>","DOI":"10.1155\/2020\/8210630","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T18:35:15Z","timestamp":1603996515000},"page":"1-14","source":"Crossref","is-referenced-by-count":17,"title":["An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows"],"prefix":"10.1155","volume":"2020","author":[{"given":"Zixuan","family":"Yu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Synthetic Automation for Process Industries, Department of Intelligent Data and Systems Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetic Automation for Process Industries, Department of Intelligent Data and Systems Engineering, Northeastern University, Shenyang 110819, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2942-3544","authenticated-orcid":true,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetic Automation for Process Industries, Department of Intelligent Data and Systems Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Business School, Liaoning University, Shenyang, China"}]},{"given":"Min","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetic Automation for Process Industries, Department of Intelligent Data and Systems Engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.12802"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1080\/2287108x.2014.956976"},{"key":"3","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/978-3-319-17175-3_7","article-title":"Green vehicle routing","volume-title":"Green Transportation Logistics","author":"T. 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