Locating depots for capacitated vehicle routing

• Authors: Inge Li Gørtz;Viswanath Nagarajan
• Venue: APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
• Year: 2011

We study a location-routing problem in the context of capacitated vehicle routing. The input to k-LocVRP is a set of demand locations in a metric space and a fleet of k vehicles each of capacity Q. The objective is to locate k depots, one for each vehicle, and compute routes for the vehicles so that all demands are satisfied and the total cost is minimized. Our main result is a constant-factor approximation algorithm for k-LocVRP. To achieve this result, we reduce k-LocVRP to the following generalization of k median, which might be of independent interest. Given a metric (V, d), bound k and parameter ρ ∈ R+, the goal in the k median forest problem is to find S ⊆ V with |S| = k minimizing: Σu∈v d(u, S) + ρ ċ d(MST(V/S)), where d(u, S) = minw∈S d(u, w) and MST(V/S) is a minimum spanning tree in the graph obtained by contracting S to a single vertex. We give a (3+ε)-approximation algorithm for k median forest, which leads to a (12+ε)-approximation algorithm for k-LocVRP, for any constant ε 0. The algorithm for k median forest is t-swap local search, and we prove that it has locality gap 3 + 2/t; this generalizes the corresponding result for k median [3]. Finally we consider the k median forest problem when there is a different (unrelated) cost function c for the MST part, i.e. the objective is Σu∈V d(u, S) + c(MST(V/S)),. We show that the locality gap for this problem is unbounded even under multi-swaps, which contrasts with the c = d case. Nevertheless, we obtain a constant-factor approximation algorithm, using an LP based approach along the lines of [12].