ArticleEnvironmental science and pollution research international2021
Multi-objective cold chain logistic distribution center location based on carbon emission.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Location optimization of cold chain logistics parks based on Bayesian probability theory and K-means clustering analysis in China.Scientific reports · 2025Article
- Optimization of cold chain logistics distribution path considering traffic condition and replenishment along the way.PloS one · 2025Article
- Systematic transformation of urban cold chain networks: From cross-regional dependencies to sustainable local excellence.PloS one · 2025Article
- Introducing a Novel multi-criteria Ranking of Alternatives with Weights of Criterion (RAWEC) model.MethodsX · 2024Review
- Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The issues of food safety and environmental protection are attracting more and more attention. Everyday, a large number of cold chain products are delivered from suppliers to customers. The cold chain products require refrigeration equipment in delivery and should be delivered to customers as soon as possible. Therefore, the challenge of reducing carbon emission and improving the customer satisfaction should be solved. This study presents the impact of carbon emission, customer satisfaction, construction cost, and operation cost on the location of cold chain logistics distribution center. A multi-objective location model for cold chain logistics distribution center considering carbon emission is established. The carbon emission equivalent cost model considers the dynamic carbon emission during transportation and the static carbon emission of the distribution center. The penalty cost under the time window is introduced into the penalty cost model of customer satisfaction, which represents a multi-objective mixed-integer linear programming problem. A non-dominated sorting genetic algorithm II (NSGA-II) is used to design the program through double-layer composite coding. NSGA-II uses a fast non-dominated sorting approach to reduce the computational complexity of non-dominated sorting. This algorithm uses the elitist control strategy, which does not need to share parameters and is more efficient in the multi-objective optimization process. The numerical results show that the proposed algorithm can generate appropriate Pareto solutions for all objectives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.