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The battery electric truck (BET) has emerged as a promising solution to reduce greenhouse gas emissions in urban logistics, given the current strict environmental regulations. This research explores the formulation and solution of the bi-objective BET dispatching problem with backhauls and time windows, aiming to simultaneously reduce environmental impacts and enhance the efficiency of urban logistics. From the sustainability perspective, one of the objectives is to minimize total energy costs, which include energy consumption and battery replacement expenses. On the other hand, from an economic perspective, the other objective is the minimization of labor costs. To solve this bi-objective BET dispatching problem, we propose an innovative approach, integrating an adaptive large neighborhood search-based metaheuristics algorithm with a multi-objective optimization strategy. This integration enables the exploration of the trade-off between fleet energy expenses and labor costs, optimizing the dispatching decisions for BETs. To validate the proposed dispatching strategy, extensive experiments were conducted using real-world fleet operations data from a logistics fleet in Southern California. The results demonstrated that the proposed approach yields a set of Pareto solutions, showcasing its effectiveness in finding a balance between energy efficiency and labor costs in urban logistics systems. The findings of this research contribute to advancing sustainable urban logistics practices and provide valuable insights for fleet operators in effectively managing BET fleets to reduce environmental impacts while maintaining economic efficiency.
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Energy-Efficient Dispatching of Battery Electric Truck Fleets with Backhauls and Time Windows
The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This article addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries, where the deliveries need to be made following the last-in-first-out principle, and backhaul customers requiring pickups. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers while considering factors such as limited driving range, payload capacity of BETs, and the possibility of en route recharging. We formulate the problem as a mixed-integer linear programming model and propose an algorithm that combines adaptive large neighborhood search and simulated annealing metaheuristics to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 11% to 40%, while maintaining reasonable computational time. In addition, the proposed strategy provides solutions that are better than or comparable with those obtained by other metaheuristics. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a novel approach for dispatching BET fleets. The findings emphasize the potential of deploying BETs to achieve energy savings and advance the goal of green logistics.
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- Award ID(s):
- 2152258
- PAR ID:
- 10510618
- Publisher / Repository:
- SAE International
- Date Published:
- Journal Name:
- SAE International Journal of Electrified Vehicles
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2691-3747
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The emergence of battery electric trucks (BETs) in recent years has shown great promise in reducing greenhouse gas (GHG) emissions in urban freight logistics. However, designing a customer-oriented dispatching strategy for a BET fleet is more complex than traditional vehicle routing problems (VRP) due to several constraints, such as limited driving range, potential need for en route recharging, and long recharging times. Also, in practice, the uncertain travel times in urban transportation network may lead to the violation of scheduled customer time windows and impact overall energy consumption. To better utilize the BET fleet, this paper introduces a robust BET dispatching problem with backhauls and time windows under travel time uncertainty, which aims to minimize the overall fleet energy consumption while also minimizing the risk of violating customer time window. A mathematical optimization model based on novel route-related sets is developed, and an adaptive large neighborhood search (ALNS) metaheuristic algorithm is used to find robust dispatching solutions. Based on real-world data from a truck fleet in San Bernardino County, California, a simulation study is conducted to demonstrate the robustness of the solutions obtained by the proposed method. Moreover, a sensitivity analysis with respect to uncertainty parameters is performed to assess the trade-off between the overall fleet energy consumption and the robustness of the solutions.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.4271/14-13-01-0009
