{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T10:56:30Z","timestamp":1787914590509,"version":"build-2784847793"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh\/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh\/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation\/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process.<\/jats:p>","DOI":"10.3390\/systems13030206","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T07:49:57Z","timestamp":1742197797000},"page":"206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in a Two-Echelon Cold Supply Chain"],"prefix":"10.3390","volume":"13","author":[{"given":"Asl\u0131","family":"Acerce","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Engineering Faculty, Sakarya University, 54050 Sakarya, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-0087","authenticated-orcid":false,"given":"Berrin","family":"Denizhan","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Engineering Faculty, Sakarya University, 54050 Sakarya, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"ref_1","first-page":"148","article-title":"Web Integration Levels of Companies in Supply Processes and Demand Management and Their Effects on Performance: The Case of Mersin Free Zone","volume":"5","author":"Demir","year":"2019","journal-title":"Int. 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