Freight-on-Transit for urban last-mile deliveries: A strategic planning approach. (March 2023)

• Record Type: Journal Article
• Authors: Delle Donne, Diego; Alfandari, Laurent; Archetti, Claudia; Ljubić, Ivana
• Is Part Of: Transportation research. Volume 169(2023)
• Page Start: 53
• Page End: 81
• Publication Date: 2023-03
• Subjects: Freight-on-Transit -- Network flow -- Column generation -- Mixed integer linear programming; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2023.01.004
• Languages: English
• ISSNs: 0191-2615

Abstract

Abstract: We study a delivery strategy for last-mile deliveries in urban areas which combines freight transportation with mass mobility systems with the goal of creating synergies contrasting negative externalities caused by transportation. The idea is to use the residual capacity on public transport means for moving freight within the city. In particular, the system is such that parcels are first transported from origins (central distribution centers) to drop-in stations, which are stop locations on public vehicle itineraries. Then, they are transported on public vehicles to drop-out stations, from where they are delivered to destination by green vehicles (such as bikes, drones, porters, etc.). The system is known as Freight-On-Transit (FOT). In this paper, we focus on the strategic decisions related to defining the public transportation network that will take part in the delivery system, i.e., which public vehicle lines and stop locations will be included (and thus equipped for the service). We propose different formulations for the problem and effective heuristic solution approaches based on column generation. We perform exhaustive tests aimed at providing managerial insights on the performance and the efficiency of the system. Highlights: Freight on Transit (FOT) is a novel logistics concept integrating goods and passengers in public transport. We propose three MIP formulations for finding exact solutions of the problem. We design an effective MIP heuristic based on a column generation approach. Managerial insights are drawn from real-world PTS data (from Orleans in France).