Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems

TL;DR

This paper introduces a novel genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problems, significantly improving solution quality in uncertain, multi-vehicle routing scenarios.

Contribution

It proposes a new collaborative solution construction procedure and a GP-evolved routing policy specifically designed for UCARP, addressing vehicle collaboration under uncertainty.

Findings

Outperforms state-of-the-art algorithms on benchmark problems.

Shows greater benefits on larger task and vehicle instances.

Demonstrates vehicle collaboration effectively handles uncertainty.

Abstract

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real-world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This paper proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (\emph{route failure}), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.