Abstract
Electric vehicles (EVs) are anticipated to play a critical role in the green transportation of the future. Logistics companies have started several projects operating with EVs in road transportation. However, routing decisions for EVs must take limited driving ranges into account. Previous related research on electric vehicle location routing problems (EVLRP) has investigated intra route facilities that support the energy supply network. Contrarily, this paper studies a new type of EVLRP with a restricted distance, where EVs are used for route planning in reverse flow logistics. The model is formulated from a real case problem in agriculture that combines both locating multiple depots and determining routing paths with a limited distance constraint. An adaptive large neighborhood search (ALNS) algorithm has been extended into four combinations and is proposed here for solving the problem. The computational results indicate that the ALNS algorithm can obtain quality solutions in short processing time when compared with software using exact methods. Furthermore, the proposed ALNS algorithm is applied to a case study problem to provide suitable locations and vehicle routes with a minimized total cost.
Highlights
Road transportation represents a major activity in the logistics of the agricultural sector
The proposed adaptive large neighborhood search (ALNS) algorithm was coded in Visual Studio 2019 with the mathematical model prepared by Lingo version 11 on a laptop with an Intel Core i5-4210U 2.70 GHz CPU with 6 GB
The best solution and lower bound obtained by Lingo were presented and compared with the solutions that were obtained by the proposed algorithms
Summary
Road transportation represents a major activity in the logistics of the agricultural sector It plays an important role in stakeholder satisfaction [1] and it is necessary for economic development. It has an enormous impact on human health and the environment. Conventional vehicles use fuel engines, which are crucial sources of CO2 , N2 O, greenhouse gases (GHGs) [2], and negative externalities, including air pollution, noise, accidents, traffic congestions, climate change risk, and resource consumption [3]. According to Arias et al (2018) [4], non-renewable energy sources that release GHGs account for 14% of global pollution. Many logistics companies have started projects for the implementation of EVs in their operations, such as UPS and DHL [5]
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