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 JAMP  Vol.7 No.3 , March 2019
Optimization Model of Cold-Chain Logistics Network for Fresh Agricultural Products —Taking Guangdong Province as an Example
Abstract: Cold-chain demand of fresh agricultural products is increasing in China, while network layout of cold-chain logistics is in disorder and its cost is huge. To address this problem, this paper casts an optimal model of cold-chain logistics network and tackles it with genetic algorithms. This optimal model takes running total cost of logistics network as the objective, and embeds a nonlinear mixed integer programming including two assignment issues. The model determines optimal layout and logistics management for pre-cooling stations and logistics center for fresh agricultural products. Our main contribution is to consider construction cost and operation cost of cold chain logistics simultaneously. Case study illustrates the effectiveness of the model.
Cite this paper: Liang, K. , Zhang, W. and Zhang, M. (2019) Optimization Model of Cold-Chain Logistics Network for Fresh Agricultural Products —Taking Guangdong Province as an Example. Journal of Applied Mathematics and Physics, 7, 476-485. doi: 10.4236/jamp.2019.73034.
References

[1]   Wang, S. and Wei, C. (2018) Analysis of Cold Chain Logistics in the Implementation of Rural Revitalization Strategy in Guangdong Province. Xiamen, China.

[2]   Chen, B. and Zhou, A. (2016) An Analysis of the Cold Chain Logistics Concerning Agricultural Products in Chongqing City. Asian Agricultural Research, 8, 19-21.

[3]   Xie, T. and Zhao, M. (2016) Research on Cold Chain Logistics Joint Distribution Model Based on Cloud Logistics. Xi’an, China.

[4]   Harris, I., Mumford, C.L. and Naim, M.M. (2014) A Hybrid Multi-Objective Approach to capacitated Facility Location with Flexible Store Allocation for Green Logistics Modeling. Transportation Research Part E: Logistics and Transportation Review, 66, 1-22.
https://doi.org/10.1016/j.tre.2014.01.010

[5]   Zheng, J.G., Li, K. and Wu, D.Q. (2017) Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm. Journal of Donghua University (English Edition), 34, 533-539.

[6]   Shen, X., Liu, L. and Huang, Z. (2013) Investigation and Analysis of Agricultural Cold Chain Logistics and Its Development Strategy in Heilongjiang Province. Asian Agricultural Research, 5, 80-82.

[7]   Chen, L., Peng, J., Zhang, B. and Rosyida, I. (2017) Diversified Models for Portfolio Selection Based on Uncertain Semivariance. International Journal of Systems Science, 48, 637-648.
https://doi.org/10.1080/00207721.2016.1206985

[8]   Zhang, B., Peng, J., Li, S. and Chen, L. (2016) Fixed Charge Solid Transportation Problem in Uncertain Environment and Its Algorithm. Computers & Industrial Engineering, 102, 186-197.
https://doi.org/10.1016/j.cie.2016.10.030

[9]   Yan, W. (2017) Study on the Path Selection of Low-Carbon Distribution in Cold Chain Based on Multi-Objective Programming. Chengdu, China.

[10]   Li, L.H., Fu, Z., Zhou, H.P., et al. (2013) Discrete Logistics Network Design Model under Interval Hierarchical OD Demand Based on Interval Genetic Algorithm. Journal of Central South University, 20, 2625-2634.
https://doi.org/10.1007/s11771-013-1777-3

[11]   Wang, L. (2016) Cold Chain Logistics Display Tremendous Growth Momentum. China’s Foreign Trade, No. 5, 48-49.

[12]   Tsai, R. and Wang, Y.Q. (2015) Analysis on the Service Performance in Third-Party Logistics Distribution. London, UK.

[13]   Zhang, Z.C. (2014) Design and Implementation of Distribution Routing Optimization for Warehousing Logistics Based on GIS. Shenzhen, China.

[14]   Liu, N., Chen, Y.G. and Li, Y.M. (2007) A Comprehensive Decision-Making Method for Optimal Location of Logistics Hub. Journal of Southeast University (English Edition), S1, 71-75.

[15]   Werner-Lewandowska, K. and Kosacka-Olejnik, M. (2018) Logistics Maturity Model for Service Company—Theoretical Background. Procedia Manufacturing, 17, 791-802.
https://doi.org/10.1016/j.promfg.2018.10.130

[16]   Olejnik, M.K. and Werner-Lewandowska, K. (2018) The Reverse Logistics Maturity Model: How to Determine Reverse Logistics Maturity Profile? Method Proposal. Procedia Manufacturing, 17, 1112-1119.
https://doi.org/10.1016/j.promfg.2018.10.027

[17]   Liu, S., Zhang, G. and Wang, L. (2018) IoT-Enabled Dynamic Optimisation for Sustainable Reverse Logistics. Procedia CIRP, 69, 662-667.
https://doi.org/10.1016/j.procir.2017.11.088

 
 
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