A Robust Approach to the Capacitated Vehicle Routing Problem with Uncertain Costs

We investigate a robust approach for solving the capacitated vehicle routing problem (CVRP) with uncertain travel times. It is based on the concept of K-adaptability, which allows one to calculate a set of k feasible solutions in a preprocessing phase before the scenario is revealed. Once a scenario occurs, the corresponding best solution may be picked out of the set of candidates. The aim is to determine the k candidates by hedging against the worst-case scenario, as is common in robust optimization. This idea leads to a min-max-min problem. In this paper, we propose an oracle-based algorithm for solving the resulting min-max-min CVRP, calling an exact algorithm for the deterministic problem in each iteration. Moreover, we adjust this approach such that heuristics for the CVRP can also be used. In this way, we derive a heuristic algorithm for the min-max-min problem, which turns out to yield good solutions in a short running time. All algorithms are tested on standard benchmark instances of the CVRP.