Truck-multidrone same-day delivery strategies: On-road resupply vs depot return

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
David Sanchez-Wells , José L. Andrade-Pineda , Pedro L. Gonzalez-R
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引用次数: 0

Abstract

This paper explores an enhanced two-waved same-day delivery (SDD) system that leverages a mothership truck equipped with multiple drones supported by an auxiliary “resupply” truck. Under standard SDD operations, this mothership truck, also capable of performing deliveries, must return to the depot to reload, incurring extra travel time and mileage. In contrast, the proposed resupply strategy enables the second delivery wave by dispatching a secondary vehicle to meet the mothership truck on-road, reloading parcels without interrupting ongoing deliveries by the drones. A single unified routing framework, the Genetic Algorithm with Iterated Estimations for Resupply (GAIER), is presented to optimise both strategies under two selectable criteria: minimising total service time or total truck mileage.
In tests with benchmark networks of different sizes (20, 50, and 75 nodes), incorporating a resupply truck reduced every selected criterion when compared to the strategy where the mothership vehicle returns to the depot. Subsequent comparative analysis points an average reduction of 17 % in service time and 21 % in truck mileage while statistical analyses support the strategy choice significancy, confirming resupply strategy’s potential for cost savings and reduced environmental impact. These findings bolster our proposition that incorporating a resupply truck into hybrid truck-multidrone systems enhances flexibility in drone delivery scheduling and improves the system’s ability to meet urban demand.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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