A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services |
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| Institution: | 1. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China;2. School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China;1. Montpellier Business School, France;2. MRM, University of Montpellier, France;3. KEDGE Business School, France;4. Olin Business School, Washington University in St. Louis, USA;5. College of AI, National Yang Ming Chiao Tung University, Taiwan;6. Miin Wu School of Computing, National Cheng Kung University, Taiwan;7. China Medical University Hospital, Taiwan;1. Newcastle Business School, Northumbria University, Newcastle upon Tyne, UK;2. School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;3. Management School, University of Liverpool, Liverpool L69 3BX, UK;4. Business School, Newcastle University, Newcastle upon Tyne NE1 4SE, UK;5. Department of Operations and Information Systems, University of Graz, Graz, Austria;1. Computer Science and Information Systems Department, University of Limerick, Limerick, Ireland;2. Université de Lorraine, CNRS, LORIA, Metz F-57000, France;3. Université de Lorraine, LCOMS, Metz F-57000, France;4. Huawei Technologies, France Research Center, Boulogne-Billancourt, France;1. Harvard Medical School, Massachusetts General Hospital, 125 Nashua Street, #260, Boston, MA 02114, USA;2. Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA |
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| Abstract: | This study investigates a multi-visit flexible-docking vehicle routing problem that uses a truck and drone fleet to fulfill pickup and delivery requests in rural areas. In this collaborative truck–drone system, each drone may serve multiple customers per trip (multi-visit services), dock to the same or different truck from where it launched (flexible docking), and perform simultaneous pickup and delivery. These characteristics complicate the temporal, spatial, and loading synchronization for trucks and drones, making the decisions of order allocation and vehicle routing highly interdependent and intractable. This problem is formulated as a mixed-integer linear programming model and solved by a tailored adaptive large neighborhood search metaheuristic. Numerical experiments are conducted on sparse rural networks to demonstrate the efficiency of the proposed method. We observe that the proposed truck–drone system shows an average cost saving of 34% compared to the truck-only case. Moreover, deep insights into the impacts of multi-visit services, flexible docking, and simultaneous pickup and delivery on the performance of the truck–drone system are discussed. |
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