Vehicle Routing with Stochastic Demands and Partial Reoptimization

TL;DR

This paper develops a branch-cut-and-price algorithm for the vehicle routing problem with stochastic demands, focusing on a switch policy that allows limited reordering during route execution, and demonstrates its effectiveness through numerical experiments.

Contribution

It introduces a novel branch-cut-and-price algorithm for VRPSD with a switch reordering policy and provides insights into the value of reoptimization in stochastic vehicle routing.

Findings

Switch policy yields significant cost savings over deterministic approaches.

The algorithm efficiently solves instances with up to 50 customers.

Reoptimization benefits are context-dependent, with potential for further improvements.

Abstract

We consider the vehicle routing problem with stochastic demands (VRPSD), a problem in which customer demands are known in distribution at the route planning stage and revealed during route execution upon arrival at each customer. A long-standing open question on the VRPSD concerns the benefits of allowing, during route execution, partial reordering of the planned customer visits. Given the practical importance of this question and the growing interest on the VRPSD under optimal restocking, we study the VRPSD under a recourse policy known as the switch policy. The switch policy is a canonical reoptimization policy that permits only pairs of successive customers to be reordered. We consider this policy jointly with optimal preventive restocking and introduce a branch-cut-and-price algorithm to compute optimal a priori routing plans. This algorithm features pricing routines where value functions represent the expected cost-to-go along planned routes for all possible states and reordering decisions. To ensure pricing tractability, we adopt a strategy that combines elementary pricing with completion bounds of varying complexity, and solve the pricing problem without relying on dominance rules. Our numerical experiments demonstrate the effectiveness of the algorithm for solving instances with up to 50 customers. Notably, they also give us new insights into the value of reoptimization. The switch policy enables significant cost savings over optimal restocking when the planned routes come from an algorithm built on a deterministic approximation of the data, an important scenario given the difficulty of finding optimal VRPSD solutions. The benefits are smaller when comparing optimal a priori VRPSD solutions obtained for both recourse policies. As it appears, further cost savings may require joint reordering and reassignment of customer visits among vehicles when the context permits.