Abstract
The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.
Highlights
With the increasing exploitation of the modern economy, advanced science, and information technology, the existing online shopping paradigm has gradually become an indispensable way of life for people. is shopping mode greatly facilitates people’s life and drives the mushroom growth of the emerging logistics industry paradigm
Logistics distribution broadly refers to the logistics campaigns of selecting, processing, casing, partitive, and gathering materials within a certain area according to the needs of users and delivers them to the places designated by users on time [4, 5]
Aiming at the existing problems in the current research, this paper proposes an improved ant colony optimization (IACO) algorithm-based cold chain logistics distribution scheduling and optimization method to reduce cold chain logistics transportation cost and improve transportation efficiency from the perspective of the transport time factor, transport cooling factor, and mean road patency factor
Summary
College of Port and Shipping Management, Guangzhou Maritime University, Guangzhou 510725, China. E traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. E simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. We have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
