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Smart Agriculture ›› 2021, Vol. 3 ›› Issue (3): 139-151.doi: 10.12133/j.smartag.2021.3.3.202108-SA004

• Intelligent Management and Control • Previous Articles     Next Articles

Time-Varying Heterotypic-Vehicle Cold Chain Logistics Distribution Path Optimization Model

LIU Siyuan1,2(), CHEN Tian'en2(), CHEN Dong2, ZHANG Chi2, WANG Cong2   

  1. 1.School of Computer, Electronics & Information, Guangxi University, Nanning 530004, China
    2.National Engineering Research Center for Information Technology in Agriculture(NERCITA), Beijing 10097, China
  • Received:2021-08-06 Revised:2021-09-15 Online:2021-06-30 Published:2021-10-29
  • corresponding author: CHEN Tian'en E-mail:siyuan.liu@st.gxu.edu.cn;chente@nercita.org.cn

Abstract:

In view of the problems of constant speed and single carbon emission calculation method in the distribution model of fresh agricultural products in the transportation link of agricultural supply chain, combined with the time-varying characteristics of road network and the new multi vehicle carbon emission calculation method, this study put forward the distribution route optimization model of fresh agricultural products with four optimization objectives, which were the distribution distance, multi vehicle carbon emission, goods loss and vehicle fixed cost. In this model, the calculation of fuel consumption and carbon emission in the model would be affected by many factors, among which the load is the most important factor: Firstly, the average fuel consumption per 100 km of different trucks was calculated, then the CO2 emission factors of various trucks were calculated according to the carbon balance principle, and finally the average value of the results of each truck was taken as the carbon emission factor of the vehicle. According to those characteristics of the model, an improved double strategies co-evolutionary ant colony system (DC-ACS) was proposed. In this study, the main method was used to transform the problem into a solvable single objective problem. Then, the ant colony algorithm combined the coevolution mechanism, adaptive pheromone update strategy and local search mechanism were used to improve the solution effect of the algorithm. Finally, an appropriate fitness calculation method and stagnation avoidance strategy were designed to enhance the ability of the algorithm to jump out of local optimization. The C105 example of Solomon dataset was solved by using the improved ant colony algorithm. The optimal solutions on the four optimization objectives were 937.94 km, 4961.48 CNY, 4081.78 CNY and 7500.87 CNY respectively, which proved the effectiveness of the model proposed in this study. Based on the effectiveness of the model, the experimental results showed that the total distribution cost of the improved ant colony algorithm reduced by more than 14% on average compared with the basic ant colony algorithm on the four optimization objectives, which proved that the improved ant colony algorithm had more advantages. The improved ant colony algorithm was used to solve large-scale examples with different distributions: centralized, random and mixed. The optimal total costs were 19939.53 CNY, 24095 CNY and 24397.58 CNY, respectively. To sum up, the proposed model and algorithm could provide a good reference for the urban distribution path decision-making of cold chain logistics enterprises, a new idea to improve the distribution path optimization model and optimization method of smart agricultural supply chain, and a reference for enterprises to further expand their scale.

Key words: cold chain logistics, path optimization, real-time information, ant colony optimization, Solomon dataset

CLC Number: