The increasing use of e-commerce introduces major challenges for last-mile delivery, which is critical to ensure smooth functioning of supply chains. Besides the speed and cost of delivery, which are the most important factors for last-mile logistics, environmental and social sustainability factors are taking on an increasingly important role, especially in urban areas. In this perspective, last-mile delivery by drones could be a good choice both in terms of transport speed and sustainability. Being electrically powered, drones can reduce environmental impact; moreover, they enable the reduction of road congestion since they do not interfere with land infrastructures. The goal of this study is to analyse the viability of drones for last-mile delivery in the Italian city of Milan. A stated preference survey was carried out to assess end-user propensity towards drones. Using these results, a goods delivery service by drones was designed. Furthermore, a preliminary financial analysis was performed to evaluate the benefits for a company that would provide such a transport service. Findings show that last-mile delivery by drones can be successfully used to deliver small and light packages, reducing environmental and social impacts, and ensuring profits for the transport provider.
This paper proposes an optimization approach for sizing port rail networks and planning railway shunting operations by adopting a discrete-time model of the overall system. First, a mixed-integer linear mathematical programming problem is defined in order to optimize shunting operations to be performed on the considered network by satisfying certain arrivals and departures of import and export flows. Moreover, the proposed procedure can be used to evaluate the capacity of a port rail network, in terms of maximum number of trains that can be managed over a certain time horizon, and to carry out what-if analyses aimed at testing different scenarios. The effectiveness of the proposed approach is shown by applying the optimization problem to a real case study referred to the port rail network of La Spezia Container Terminal located in Northern Italy. A computational analysis realized by varying the dimension and complexity of the problem instances is also reported in this paper to discuss the computational performance of the proposed model
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