Content area

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.

Details

Title
Multi-objective cold chain logistic distribution center location based on carbon emission
Author
Li Xinguang 1 ; Zhou, Kang 2 

 Qingdao University of Technology, School of Mechanical and Automobile, Qingdao, China (GRID:grid.412609.8) (ISNI:0000 0000 8977 2197) 
 Northeast Forestry University, Traffic School, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091) 
Pages
32396-32404
Publication year
2021
Publication date
Jul 2021
Publisher
Springer Nature B.V.
ISSN
09441344
e-ISSN
16147499
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2546405749
Copyright
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021.