Large Neighborhood Search for Periodic Electric Vehicle Routing Problem
| Autor: | Tayeb Oulad Kouider, Ammar Oulamara, Wahiba Ramdane Cherif-Khettaf |
|---|---|
| Přispěvatelé: | OPTImisation Methods for Integrated SysTems (OPTIMIST), Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Electric Vehicle
Mathematical optimization 021103 operations research business.product_category Computer science Total cost 0211 other engineering and technologies Time horizon 02 engineering and technology [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] 7. Clean energy Large Neighborhood Search Set (abstract data type) Charging station Electric vehicle 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Large neighborhood search 020201 artificial intelligence & image processing Routing (electronic design automation) business Periodic Vehicle Routing ComputingMilieux_MISCELLANEOUS Charging Station |
| Zdroj: | 8th International Conference on Operations Research and Enterprise Systems 8th International Conference on Operations Research and Enterprise Systems, Feb 2019, Prague, France. pp.169-178, ⟨10.5220/0007409201690178⟩ 8th International Conference on Operations Research and Enterprise Systems, Feb 2019, Prague, Czech Republic. pp.169-178, ⟨10.5220/0007409201690178⟩ ICORES |
| DOI: | 10.5220/0007409201690178⟩ |
| Popis: | International audience; In this paper, we address the Periodic Electric Vehicle Routing Problem, named PEVRP. This problem is motivated by a real-life industrial application and it is defined by a planning horizon of several periods typically ”days”, in which each customer has a set of allowed visit days and must be served once in the time horizon. The whole demand of each customer must be fulfilled all together. A limited fleet of electric vehicles is available at the depot. The EVs could be charged during their trips at the depot and in the available external charging stations. The objective of the PEVRP is to minimize the total cost of routing and charging over the time horizon. We propose a Large Neighborhood Search (LNS) framework for solving the PEVRP. Different implementation schemes of the proposed method including customer and station insertion strategies, three destroy operators and three insertion operators are tested on generalized benchmark instances. The computational results show that LNS prod uces competitive results compared to results obtained in previous studies. An analysis of the performance of the proposed operators is also presented. |
| Databáze: | OpenAIRE |
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