Combining variable neighborhood search and machine learning to solve the vehicle routing problem with crowd-shipping

Crowd-shipping is an innovative delivery model, based on the sharing economy concept. In this framework, delivery operations are carried out by using existing underused resources, i.e., ordinary people who usually travel on the roads with their own vehicles and have empty space to share, in addition to the company's conventional vehicles. We refer to these non-professional couriers as "occasional drivers". Occasional drivers are not company's employees: they are common people who may decide to perform a delivery service during their free time, for a small compensation. Usually, this process is possible thanks to a crowd-shipping platform, which connects the company, the occasional drivers, and the customers. In this paper, we tackle the crowd-shipping model, by developing an approach inspired to variable neighborhood search (VNS) approach, where several machine learning techniques are used to explore the most promising areas of the search space. VNS is a well-known meta-heuristic already used in crowd-shipping applications. In this paper, the learning strategies embedded into the framework have shown to improve the effectiveness of the basic framework.