A Simulation-Optimization Approach for the Management of the On-Demand Parcel Delivery in Sharing Economy

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 8; pp. 10570 - 10582
• Main Authors: Perboli, Guido, Rosano, Mariangela, Wei, Qu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bicycles; Companies; Context; Crowdsourcing; Delivery services; Green products; Logistics; Monte Carlo simulation; Optimization; parcel delivery; Route planning; Simulation; stochastic and dynamic VRPTW; Stochastic models; Stochastic processes; Urban areas; Vehicle dynamics; Vehicle routing; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3094851

This paper investigates a dynamic and stochastic vehicle routing problem with time windows that considers the use of multiple delivery options and crowd drivers, reflecting the synchromodality in the urban context. We propose a multi-stage stochastic model, and we solve the problem by using a simulation-optimization strategy. It relies on a Monte Carlo simulation and a large neighborhood search (LNS) heuristic for optimization. We conduct a case study in the medium-sized city of Turin (Italy) to measure the potential impact of integrating cargo bikes and crowd drivers in parcel delivery. Experimental results show that combining crowd drivers and green carriers with the traditional van to manage the parcel delivery is beneficial in terms of economic and environmental cost-saving, while the operational efficiency decreases. Besides, the green carriers and crowd drivers are promising delivery options to deal with online customer requests in the context of stochastic and dynamic parcel delivery. The resulting set of policies are part of the outcomes of the Logistics and Mobility Plan 2019-2021 in the Piedmont region.