Routing of Heterogeneous Fleets for Flash Deliveries via Vehicle Group Assignment

Conference paper (2022)

Authors

M. Kronmüller Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Andres Fielbaum Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 M. Kronmüller, Andres Fielbaum, J. Alonso-Mora
DOI: https://doi.org/10.1109/ITSC55140.2022.9922062
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

This paper presents a novel approach to route heterogeneous fleets for flash delivery operations. Flash deliveries offer to serve customers' wishes in minutes. We investigate a scenario that allows to pick up orders at multiple depots with a heterogeneous vehicle fleet leveraging different modes of transportation. We propose the Heterogeneous Vehicle Group Assignment (HVGA) method, which, given a problem state, identifies potential pick-up locations, calculates potential trips for all modes of transportation and last chooses from the set of potential trips. Experiments to analyze the proposed method are executed using a fleet featuring two modes of transportation, trucks and drones. We compare to a state-of-the-art method. Results show that HVGA is able to serve more orders while requiring less total traveled distance. Further, the effects of the fleet size and fleet composition between drones and trucks are examined by simulating three hours of a flash delivery operation in the city center of Amsterdam.

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