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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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ANALYZING EQUITY IN TRUCK-DRONE COOPERATIVE DELIVERY FOR RURAL AREAS
Given the surge in rural logistics services and the disparities between urban and rural delivery services, a compelling necessity emerges to explore innovative drone-based delivery solutions. The challenges inherent in truck-drone delivery due to technological and physical barriers affect service quality for some rural customers, thus magnifying concerns about delivery fairness. To investigated delivery equity, we present a truck-drone cooperative delivery model to analyze rural customers’ accessibility to such innovative delivery technology. This model accommodates rural residents’ delivery preferences while optimizing truck routes. Drones are dispatched from designated trucks to serve customers within their flight distance. Our proposed heuristic algorithm, founded on graph-based truck-drone delivery preferences, solves this intricate problem efficiently. Numerical experiments underscore the efficacy of our approach, highlighting substantial reductions in delivery costs and an impressive 20% increase in drone deliveries on a large-scale network. Through sensitivity analyses exploring drone operational costs and flight distances–affected by government policies and technological advancements–we devise an equity metric that gauges the efficiency and accessibility of rapid rural delivery services under the truck-drone delivery framework. Our research contributes to equity analysis, addressing challenges faced by logistics companies and rural residents. Moreover, it bridges the gap between urban and rural logistics, fostering an inclusive and equitable delivery ecosystem benefiting all customers, regardless of their location.
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- Award ID(s):
- 2200506
- PAR ID:
- 10528340
- Publisher / Repository:
- the 103rd Transportation Research Board Annual Meeting
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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