Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm
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 cal...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/6623563 |
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| _version_ | 1832559765462450176 |
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| author | Haiou Xiong |
| author_facet | Haiou Xiong |
| author_sort | Haiou Xiong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ac6fc62996a44c4adf4317c46258c47 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5ac6fc62996a44c4adf4317c46258c472025-02-03T01:29:20ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/66235636623563Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization AlgorithmHaiou Xiong0College of Port and Shipping Management, Guangzhou Maritime University, Guangzhou 510725, ChinaThe 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.http://dx.doi.org/10.1155/2021/6623563 |
| spellingShingle | Haiou Xiong Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm Discrete Dynamics in Nature and Society |
| title | Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm |
| title_full | Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm |
| title_fullStr | Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm |
| title_full_unstemmed | Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm |
| title_short | Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm |
| title_sort | research on cold chain logistics distribution route based on ant colony optimization algorithm |
| url | http://dx.doi.org/10.1155/2021/6623563 |
| work_keys_str_mv | AT haiouxiong researchoncoldchainlogisticsdistributionroutebasedonantcolonyoptimizationalgorithm |