Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization
This paper aims to solve the last-mile distribution of rural e-commerce logistics (RECL) for the survival of third-party logistics enterprise. Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.12179-12187 |
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| description | This paper aims to solve the last-mile distribution of rural e-commerce logistics (RECL) for the survival of third-party logistics enterprise. Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on five test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network. |
| doi_str_mv | 10.1109/ACCESS.2020.2964328 |
| format | Article |
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Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on five test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. 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The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network.</description><subject>Analytical models</subject><subject>Ant colony optimization</subject><subject>ant colony optimization (ACO)</subject><subject>Datasets</subject><subject>Electronic commerce</subject><subject>last-mile distribution</subject><subject>Logistics</subject><subject>Logistics management</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Route optimization</subject><subject>Rural e-commerce logistics (RECL)</subject><subject>Subsidies</subject><subject>Task analysis</subject><subject>Vehicles</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpVUctKBDEQHERBUb_AS8DzrHlNHsd1XB-wIvg4h95JIllmN2syc9CvNzoi2pduuquqC6qqzgieEYL1xbxtF09PM4opnlEtOKNqrzqiROiaNUzs_5kPq9Oc17iUKqtGHlX9YxwHhx52Q9iEDxhC3CIfE1pCHur70Dt0FfKQwmr8PkWPHscEPVrUbdxsXOocWsbXAgldRpeQnUUFNt8OqI193L7_Uz6pDjz02Z3-9OPq5Xrx3N7Wy4ebu3a-rDuO1VADlk3DtNfSW2ed6IRQtnOYCrqyhMqG8IZoyiVw6YntrF0JCaCJtV5KLdlxdTfp2ghrs0thA-ndRAjmexHTq4FUHPfOEMwZE85zpTBXHoBoDJxYKBZWStKidT5p7VJ8G10ezDqOaVvsG8obLomgUhUUm1Bdijkn53-_Emy-UjJTSuYrJfOTUmGdTazgnPtlKN1gjgn7BGbyjZU</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Liu, Wei</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on five test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.2964328</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-5801-4523</orcidid><oa>free_for_read</oa></addata></record> |
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| source | IEEE Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; DOAJ开放获取期刊资源库 |
| subjects | Analytical models Ant colony optimization ant colony optimization (ACO) Datasets Electronic commerce last-mile distribution Logistics Logistics management Mathematical models Optimization Parameters Route optimization Rural e-commerce logistics (RECL) Subsidies Task analysis Vehicles |
| title | Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization |
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