EURO 2024 Copenhagen
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3252. Application of Genetic Network Programming to optimize logistics services in mountain areas using drones

Invited abstract in session WD-56: Last mile delivery with drones, stream Transportation.

Wednesday, 14:30-16:00
Room: S04 (building: 101)

Authors (first author is the speaker)

1. Alessia Grosso
DEAMS - Department of Economics, Business, Mathematics and Statistics, University of Trieste
2. Caterina Caramuta
DEAMS, University of Trieste
3. Giovanni Longo
DEAMS - Department of Economics, Business, Mathematics and Statistics, University of Trieste

Abstract

In recent years, an increasing number of companies have been introducing drones into the transport sector, mainly to cope with last-mile distribution issues, being these means of transport more cost effective, sustainable and efficient than traditional modes. Notably, their use in remoted areas can increase accessibility and provide support in case of emergency.
Existing research studies mainly focus on drone components and on routing optimization problems, but there seems to be a lack of papers dealing with the design of distribution chains implementing drones for last-mile logistics services.
This research proposes an application of genetic network programming to optimize the logistics network configuration, with the aim of defining the optimal combination of transport modes serving the implementation of drones in the last-mile distribution. In particular, a multi-criteria method is used to evaluate the fitness of the candidate solutions by combining environmental impacts, transportation performances and costs. Moreover, an ad-hoc crossover operator is developed to preserve the most promising structures during the evolution process.
The methodology has been tested through an application to a case study in a mountain area in the North-East of Italy.

Keywords

Status: accepted

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