New measures of proximity for the assignment algorithms in the MDVRPTW This paper proposes new proximity measures for assignment algorithms for the Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW). Given the intrinsic difficulty of this problem class, two-step approximation methods of the type `cluster first, route second’ seem to be the most promising for practical size problems. The focus is on the clustering phase, the assignment of customers to depots. Our approach is to extend the existing metrics with time windows. New measures that include time windows and distances are added to two assignment heuristics, that previously only used distance to evaluate proximity between customers and depots. A computational study of their performance is presented, which shows that the inclusion of time windows in the measures of proximity gives better results, in terms of routing, than only using the distance.