A Framework for Vehicle Routing Approximation Schemes in Trees

TL;DR

This paper introduces a versatile framework for creating polynomial-time approximation schemes for various vehicle routing problems in trees, simplifying solution structures to enable efficient near-optimal routing solutions.

Contribution

The paper presents a novel general framework that restricts potential solutions in vehicle routing problems on trees, enabling PTAS development for multiple problem variants.

Findings

Framework successfully applied to multiple vehicle routing problems.

Achieves polynomial-time approximation schemes with near-optimal solutions.

Extends to multiple depot scenarios.

Abstract

We develop a general framework for designing polynomial-time approximation schemes (PTASs) for various vehicle routing problems in trees. In these problems, the goal is to optimally route a fleet of vehicles, originating at a depot, to serve a set of clients, subject to various constraints. For example, in Minimum Makespan Vehicle Routing, the number of vehicles is fixed, and the objective is to minimize the longest distance traveled by a single vehicle. Our main insight is that we can often greatly restrict the set of potential solutions without adding too much to the optimal solution cost. This simplification relies on partitioning the tree into clusters such that there exists a near-optimal solution in which every vehicle that visits a given cluster takes on one of a few forms. In particular, only a small number of vehicles serve clients in any given cluster. By using these coarser building blocks, a dynamic programming algorithm can find a near-optimal solution in polynomial time. We show that the framework is flexible enough to give PTASs for many problems, including Minimum Makespan Vehicle Routing, Distance-Constrained Vehicle Routing, Capacitated Vehicle Routing, and School Bus Routing, and can be extended to the multiple depot setting.