Authors: Luigi De Giovanni, Nicola Gastaldon, Ivano Lauriola, and Filippo Sottovia
Abstract: We consider a multi-attribute vehicle routing problem arising in a freight transportation company owning a fleet of heterogeneous trucks with different capacities, loading facilities and operational costs.

The company receives short- and medium-haul transportation orders consisting of pick-up and delivery with soft or hard time windows falling in the same day or in two consecutive days. Vehicle routes are planned on a daily basis taking into account constraints and preferences on capacities, maximum duration, number of consecutive driving hours and compulsory drivers rest periods, route termination points, order aggregation.

The objective is to maximize the difference between the revenue from satisfied orders and the operational costs. We propose a two-levels local search heuristic: at the first level, a variable neighborhood stochastic tabu search determines the order-to-vehicle assignment, the second level deals with intra-route optimization.

The algorithm provides the core of a decision support tool used at the planning and operational stages, and computational results validated on the field attest for an estimated 9% profit improvement with respect to the current policy based on human expertise.