Solving Vehicle Routing Problem: A Big Data Analytic Approach
In the transport industry, the cost effectiveness relies heavily on the rational design of the transport routes. However, the traditional theories and methods on vehicle routing problem (VRP) cannot describe the dynamic features of travel time accurately. To solve the problem, this paper puts forwar...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8910558/ |
| _version_ | 1818330330436730880 |
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| author | Shaoqing Zheng |
| author_facet | Shaoqing Zheng |
| author_sort | Shaoqing Zheng |
| collection | DOAJ |
| description | In the transport industry, the cost effectiveness relies heavily on the rational design of the transport routes. However, the traditional theories and methods on vehicle routing problem (VRP) cannot describe the dynamic features of travel time accurately. To solve the problem, this paper puts forward a dynamic VRP model based on big data analysis on traffic flow, and solves it by the genetic algorithm (GA). It is assumed that the real-time traffic data are updated every 15mins in the transport network, and the customer demand is updated dynamically from time to time. The example analysis shows that my model and its route adjustment strategy can minimize the total transport cost by routing the vehicles from multiple depots under the soft time window. The research findings help transport enterprises to make effective use of vehicles and receive more profits. |
| first_indexed | 2024-12-13T13:02:14Z |
| format | Article |
| id | doaj.art-bd25f1a06adf4625a31bec8d091c9842 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:02:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bd25f1a06adf4625a31bec8d091c98422022-12-21T23:44:59ZengIEEEIEEE Access2169-35362019-01-01716956516957010.1109/ACCESS.2019.29552508910558Solving Vehicle Routing Problem: A Big Data Analytic ApproachShaoqing Zheng0https://orcid.org/0000-0003-3236-8662Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, ChinaIn the transport industry, the cost effectiveness relies heavily on the rational design of the transport routes. However, the traditional theories and methods on vehicle routing problem (VRP) cannot describe the dynamic features of travel time accurately. To solve the problem, this paper puts forward a dynamic VRP model based on big data analysis on traffic flow, and solves it by the genetic algorithm (GA). It is assumed that the real-time traffic data are updated every 15mins in the transport network, and the customer demand is updated dynamically from time to time. The example analysis shows that my model and its route adjustment strategy can minimize the total transport cost by routing the vehicles from multiple depots under the soft time window. The research findings help transport enterprises to make effective use of vehicles and receive more profits.https://ieeexplore.ieee.org/document/8910558/Big data analysisvehicle routing problem (VRP)genetic algorithm (GA)minimum transport costvehicle sharing |
| spellingShingle | Shaoqing Zheng Solving Vehicle Routing Problem: A Big Data Analytic Approach IEEE Access Big data analysis vehicle routing problem (VRP) genetic algorithm (GA) minimum transport cost vehicle sharing |
| title | Solving Vehicle Routing Problem: A Big Data Analytic Approach |
| title_full | Solving Vehicle Routing Problem: A Big Data Analytic Approach |
| title_fullStr | Solving Vehicle Routing Problem: A Big Data Analytic Approach |
| title_full_unstemmed | Solving Vehicle Routing Problem: A Big Data Analytic Approach |
| title_short | Solving Vehicle Routing Problem: A Big Data Analytic Approach |
| title_sort | solving vehicle routing problem a big data analytic approach |
| topic | Big data analysis vehicle routing problem (VRP) genetic algorithm (GA) minimum transport cost vehicle sharing |
| url | https://ieeexplore.ieee.org/document/8910558/ |
| work_keys_str_mv | AT shaoqingzheng solvingvehicleroutingproblemabigdataanalyticapproach |