Abstract

In this paper, we present the Time-Dependent Vehicle Routing Problem with Fleet and Crew Assignment (TD-VRPFCA), which aims to design efficient delivery routes in urban environments considering Time-Dependent Travel Times (TD-TT) for the road network and Time-Dependent Service Times (TD-ST) for in-building deliveries.The problem also involves assigning heterogeneous crews, composed of drivers, delivery personnel, and multifunctional delivery personnel, who can drive and deliver. To solve the TD-VRPFCA, we propose a Mixed-Integer Linear Programming (MILP) formulation for small-size instances and Black Widow Optimization (BWO) metaheuristic for medium and large-scale cases. The search intensification procedures. Computational experiments demonstrate that for small instances, the BWO obtains the same solutions as the MILP model with a fraction of the computational time, while for large instances it outperforms Genetic Algorithm and Tabu Search approaches according to Friedman and Nemenyi statistical tests, providing better fitness values and lower CPU times. Finally, a real-world case study based on an urban delivery company in Santiago, Chile, confirms the applicability of the proposed approach for realistic routing and crew planning under variable traffic conditions. The results show that the TD-VRPFCA provides an effective framework for supporting last-mile logistics decisions with complex workforce configurations. © 2013 IEEE.