Industrial Internet of Things-based vehicle path planning under the mixed pick-and-send mode : case study of express industry
Recent years have witnessed profound progress in the economy and technology in China. Fast-paced life has prompted offline retail to go online, and E-commerce has become the mainstream way of shopping. These changes have promoted the development of the express delivery industry, and the end delivery...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
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Nanyang Technological University
2022
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| 在線閱讀: | https://hdl.handle.net/10356/154905 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Recent years have witnessed profound progress in the economy and technology in China. Fast-paced life has prompted offline retail to go online, and E-commerce has become the mainstream way of shopping. These changes have promoted the development of the express delivery industry, and the end delivery service has also attracted more and more attention. It is said that pick-up and delivery are the "first mile" and "last-mile" of express delivery services separately, the management level, response time, and service level of the company directly affect customer satisfaction. Since the operation cost of pick-and-send services accounts for a relatively high proportion of the total logistics cost, the optimization of the pick-and-send process is of great significance to the logistics industry.
The development of the Industrial Internet of Things and the improvement of 5G, RFID, and other technologies have provided support for the development of the express delivery industry. Based on these technical backgrounds, this dissertation constructs a mixed pick-and-send vehicle routing model based on the consideration of dynamic changes in pick-up demand and converts the dynamic problem into multiple static sub-problems by dividing sub-time period. Finally, this dissertation uses an adaptive large neighborhood search heuristic algorithm to solve the problem. The feasibility of the model and algorithm is verified by comparing and evaluating the original path plan of the case and the optimized route. The results show that the model can help reduce logistics costs, improve vehicle utilization, and achieve efficient real-time logistics services. |
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