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2759. Park-and-Multi-Loop Routing for Last-Mile Delivery

Invited abstract in session WA-64: Last-Mile Delivery, stream VeRoLog - Vehicle Routing and Logistics.

Wednesday, 8:30-10:00
Room: S16 (building: 101)

Authors (first author is the speaker)

1. Emanuele Manni
Department of Engineering, University of Salento
2. Tommaso Adamo
Engineering for Innovation, University of Salento
3. Gianpaolo Ghiani
Dipartimento di Ingegneria dell' Innovazione, University of Salento
4. Emanuela Guerriero
University of Lecce
5. Gianluca Solazzo
Engineering for Innovation, University of Salento

Abstract

This paper deals with the park-and-multi-loop routing problem, in the context of same-day delivery, in which a fleet of traditional vehicles equipped with several autonomous delivery robots (ADRs) leave from a depot to service a set of customer requests characterized by a delivery location and a time window. Each delivery route involves a main tour of a traditional vehicle, which may stop at one or more parking slots, from which one or more subtours are carried out by the driver and the ADRs on the pedestrian network. The goal is to minimize the overall distance traveled by vehicles for traditional deliveries, as well as the walking distance covered by drivers and ADRs. For this problem, we propose an Adaptive Large Neighborhood Search (ALNS), in which the destroy and repair operators are parameterized, and the most appropriate parameter setting is constantly updated. The update phase is carried out by a reasoner, which analyzes the solutions and, according to some performance measures, identifies the most suitable routes to be destroyed and the parameterization of the repair operator. Computational results on a set of instances of varying size, derived from real data of several major cities, certify the quality of the approach, compared to an ALNS with classic destroy and repair operators.

Acknowledgement
The work is supported by the research program "Sustainable Mobility Center" (Centro Nazionale per la Mobilità Sostenibile - CN MOST), project code CN00000023, Spoke 7

Keywords

Status: accepted

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