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The efficient management of last-mile delivery is one of the main challenges faced by on-line retailers and logistic companies. The main aim is to offer personalized delivery services, that meet speed, flexibility, and control requirements and try to reduce environmental impacts as well. Crowd-sourced shipping is an emerging strategy that can be used to optimize the last-mile delivery process. The main idea is to deliver packages to customers with the aid of non-professional couriers, called occasional drivers. In this paper, we address the vehicle routing problem with occasional drivers, time window constraints and multiple deliveries. To handle this problem, we design some greedy randomized adaptive search procedures (GRASP). In order to assess the behaviour of the proposed algorithms, computational experiments are carried out on benchmark instances and new generated test sets. A comparison with previous published approaches, tailored for the problem at hand, is also provided. The numerical results are very encouraging and highlight the superiority, in terms of both efficiency and effectiveness, of the proposed GRASP algorithms.

Solution approaches for the vehicle routing problem with occasional drivers and time windows