Welcome to Francis Academic Press

Academic Journal of Business & Management, 2020, 2(5); doi: 10.25236/AJBM.2020.020511.

Research on Optimization of Cold Chain Logistics Distribution Path Based on P Company

Author(s)

Xinyan YU1, Daofang CHANG2,*, Xin SONG3

Corresponding Author:
Daofang CHANG
Affiliation(s)

1 Logistics Science and Engineering Research Institute, Shanghai Maritime University, Anshan, Liaoning, China
2 Logistics Science and Engineering Research Institute, Shanghai Maritime University, Fengqiu, Henan, China
3 Logistics Science and Engineering Research Institute, Shanghai Maritime University, Chizhou, Anhui, China
Email:dfchang@shmtu.edu.cn
*Corresponding Author

Abstract

With the development of cold chain technology and the improvement of people's living standards, the demand for cold chain logistics is becoming more and more prosperous. In view of the fact that most cold chain logistics companies still have many problems such as high distribution costs and untimely distribution. Considering the high cost of cold chain transportation, the total cost of the five costs of fixed cost, fuel consumption cost, refrigeration cost, cargo damage cost, and penalty cost is the minimum optimization goal, a cold chain logistics distribution route optimization model is constructed, and genetic algorithm and hybrid are designed. Particle swarm optimization solves the problem, and the optimized path effectively reduces the company's cold chain distribution time and cost, and improves the efficiency of cold chain transportation.

Keywords

Cold chain logistics, Distribution path optimization, Genetic algorithm, Hybrid particle swarm optimization

Cite This Paper

Xinyan YU, Daofang CHANG, Xin SONG. Research on Optimization of Cold Chain Logistics Distribution Path Based on P Companyl. Academic Journal of Business & Management (2020) Vol. 2, Issue 5: 102-113. https://doi.org/10.25236/AJBM.2020.020511.

References

[1] Wu Jiahuan. Research on the Optimization of Cold Chain Logistics Distribution Path of Shanghai Xinyi Company[D]. Donghua University, 2017.
[2] Mo Haixi, Gao Zhenhua, Chen Senfa. Location model of logistics distribution center based on AHP and target planning[J]. Highway Transportation Science and Technology, 2007, 24(5): 150-153.
[3] Guo Fu, Long Ying. Algorithm of vehicle routing problem with time window backhaul [J]. Journal of Northeastern University: Natural Science Edition (5): 575-578.
[4] Li Shuqin, Yang Bin, Zhao Lei, et al. Demand optimization of an environmentally-friendly multi-model combination distribution route with time windows[J]. Journal of Guangxi University (Natural Science Edition), 2013(02):154-160.
[5] Askin, Ronald G, Baffo, Ilaria, Xia, Mingjun. Multi-commodity warehouse location and distribution planning with inventory consideration[J]. International Journal of Production Research, 2014,52(7):1897-1910.
[6] Wu Lihong. Research on the retailer's online channel fresh product delivery method decision [D]. 2017.
[7] Chaug-Ing Hsu, Wei-Ting Chen, Wei-Jen Wu. Optimal delivery cycles for joint distribution of multi-temperature food [J]. Food Control, 2013, 34(01): 106-114.
[8] Liu Xiu. Research on Urban Agricultural Product Cold Chain Distribution Path Optimization from a Low Carbon Perspective [D]. Dalian Maritime University, 2018.
[9] Sinaide Nunes Bezerra, Sergio Ricardo de Souza, Marcone Jamilson Freitas Souza. A GVNS Algorithm for Solving the Multi-Depot Vehicle Routing Problem [J]. Electronic Notes in discrete Mathematics, 2018 (66): 167-174.
[10] (Japan) Xuan Guangnan, Cheng Runwei, Wang Dingwei, et al. Genetic algorithm and engineering design [M]. Beijing: Science Press, 2000.
[11] Li Minqiang. Basic theory and application of genetic algorithm [M]. Beijing: Science Press, 2002:35.
[12] Ye Runzhou. Optimization of last-mile express delivery based on improved hybrid particle swarm optimization algorithm [D]. Hefei University of Technology, 2019.