Enforcing TSP-Optimality in Fair Vehicle Routing by Cutting Planes

TL;DR

This paper introduces a branch-price-and-cut method to solve the fair capacitated vehicle routing problem by enforcing TSP-optimality, significantly improving solution accuracy on benchmark instances.

Contribution

It develops a novel framework with TSP-optimality cuts and a lifting procedure to efficiently solve the problem, outperforming existing methods.

Findings

Nearly all benchmark instances solved to optimality.

Average gap of 0.27% on hardest configurations.

Method effectively enforces TSP-optimality constraints.

Abstract

We study the fair capacitated vehicle routing problem, in which a fleet of vehicles must serve a set of customers such that the difference between the longest and shortest route, the range, is minimized. A key challenge is that the range objective is non-monotonic: it can be reduced by artificially lengthening routes, leading to solutions that violate TSP-optimality of individual routes. Existing exact methods struggle to handle this efficiently. We propose a branch-price-and-cut framework that enforces TSP-optimality through TSP-optimality cuts, which forbid TSP-dominated arc sequences. We strengthen the cuts through a dedicated lifting procedure. Computational experiments on benchmark instances with up to 25 customers show the method solves nearly all instances to optimality, achieving an average gap of 0.27% on the hardest configurations.