Bi-Level Fleet Dispatching Strategy for Battery-Electric Trucks: A Real-World Case Study

Dongbo Peng (), Zhouqiao Zhao, Guoyuan Wu and Kanok Boriboonsomsin
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Dongbo Peng: Department of Electric and Computer Engineering, University of California Riverside, Riverside, CA 92521, USA
Zhouqiao Zhao: Department of Electric and Computer Engineering, University of California Riverside, Riverside, CA 92521, USA
Guoyuan Wu: Center for Environmental Research & Technology, University of California Riverside, Riverside, CA 92507, USA
Kanok Boriboonsomsin: Center for Environmental Research & Technology, University of California Riverside, Riverside, CA 92507, USA

Sustainability, 2023, vol. 15, issue 2, 1-15

Abstract: Driven by new regulations concerning greenhouse gas (GHG) emissions in the transportation sector, battery-electric trucks (BETs) are considered one of the sustainable freight transportation solutions. In this paper, a dispatching problem of the BET fleet is formulated as a capacitated electric vehicle routing problem (VRP) with pick-up and delivery. As the BET dispatching problem is NP-hard, the performance of existing approaches deteriorates in large instance problems, especially when the customers have different preferences and constraints. This article proposes a bi-level strategy that incorporates routing zone partitioning and metaheuristic-based vehicle routing to solve the large-scale BET dispatching problem, considering the delivery types, limited travel distances, and cargo payloads. We apply this strategy to a real-world fleet dispatching scenario with around 300 customer positions for pickups and drop-offs. The experimental results demonstrate that the proposed bi-level strategy can reduce total travel distance and travel time by 24–31%, compared to the baseline strategy implemented in the real world.

Keywords: sustainable urban freight transportation; meta-heuristic algorithms; truck routing problems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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