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Eng."],"published-print":{"date-parts":[[2026,3,30]]},"abstract":"<jats:p>In the context of multi-depot operations in the fast-moving consumer goods (FMCGs) industry, traditional logistics approaches increasingly fail to meet integrated requirements for cost, speed and environmental impact. Multi-depot collaboration, key to enhancing efficiency, faces challenges in route optimization, inventory management and carbon constraints. To balance economy and sustainability, this paper addresses multi-depot distribution challenges via the collaborative delivery vehicle routing problem with multi-depots and replenishment (CDVRP-MDR). It constructs a mixed-integer linear programming model with vehicle capacity and carbon constraints, then designs an adaptive large neighborhood search\u2013artificial bee colony algorithm (ALNS\u2013ABC). Through dynamic destruction-repair operator selection and gene reverse-order recombination, ALNS\u2013ABC optimizes local search to balance global exploration and local development. Benchmark experiments confirm its effectiveness in multi-depot optimization. For FMCG firms\u2019 centralized inventory issues (delayed transfers, high long-haul costs), it boosts inventory turnover and cross-depot response via collaborative transfers, offering a practical paradigm for green upgrading of multi-depot logistics systems.<\/jats:p>","DOI":"10.1142\/s0218194025500834","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T03:29:42Z","timestamp":1760671782000},"page":"567-593","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Depot Vehicle Routing Problem with Collaborative Replenishment Using ALNS\u2013ABC Algorithm"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4024-3372","authenticated-orcid":false,"given":"Bin","family":"Ye","sequence":"first","affiliation":[{"name":"Marketing Department (Logistics Center), China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou, Zhejiang 310000, 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