Author(s)

Yunju Qu ¹, Huizhong Dong ¹, Zongting Liu ¹

Affiliation(s)

¹ Business School, Shandong University of Technology, Zhangdian, Zibo, China

Corresponding Author

Yunju Qu

ABSTRACT

This study focuses on the perspective of low carbon economy and enterprise cost under multiple distribution center. Based on the method of operation research, based on the perspective of enterprise distribution cost, the dairy cold chain distribution path optimization model is built from the fixed cost, transportation and refrigeration cost, cargo loss cost, customer satisfaction cost and carbon tax cost, and the genetic simulation degradation algorithm is designed to conduct the result simulation. The research results are as follows: (1) compared with genetic simulation algorithms and simulated annealing algorithms that overcome the problems of population diversity and local convergence, and their operation time and optimization degree are greatly improved.(2) Comparing the results of the three multi-center distribution schemes, there is little optimization difference between administrative distribution and focus zoning distribution, and the joint distribution mode under the integration of distribution resources is better than other schemes under the total cost or segmentation cost.(3) Changes in carbon trading parameters have a significant impact on the path optimization under the joint distribution mode: carbon price changes lead to higher distribution cost and lower distribution mileage; carbon quota changes under the fixed carbon price are characterized by three stages of carbon cost pressure—carbon cost transition—carbon trading dividend. In short, enterprises should pay attention to the balance between cost, customer and environment, and the joint distribution mode has more cost pressure.

KEYWORDS

Dairy delivery, Carbontrading, Multi-distribution center, Genetic simulation annealing algorithm

CITE THIS PAPER

Yunju Qu, Huizhong Dong, Zongting Liu, Study on Dairy Distribution Path Optimization Considering Multicenter and Carbon Trading Mechanism. Industrial Engineering and Innovation Management (2022) Vol. 5: 41-57. DOI: http://dx.doi.org/10.23977/ieim.2022.050906.

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