Solving Orienteering with Category Constraints Using Prioritized Search

TL;DR

This paper presents a prioritized search algorithm for solving rooted orienteering problems with category constraints, improving efficiency and solution quality in trip planning and logistics.

Contribution

It introduces a systematic expansion and prioritization method that reduces search space and can be adapted into a faster approximation algorithm with guaranteed bounds.

Findings

Outperforms state-of-the-art approaches in efficiency and solution quality

Provides an optimal solution algorithm for category-constrained orienteering

Offers a faster approximation method with quality guarantees

Abstract

We develop an approach for solving rooted orienteering problems with category constraints as found in tourist trip planning and logistics. It is based on expanding partial solutions in a systematic way, prioritizing promising ones, which reduces the search space we have to traverse during the search. The category constraints help in reducing the space we have to explore even further. We implement an algorithm that computes the optimal solution and also illustrate how our approach can be turned into an approximation algorithm, yielding much faster run times and guaranteeing lower bounds on the quality of the solution found. We demonstrate the effectiveness of our algorithms by comparing them to the state-of-the-art approach and an optimal algorithm based on dynamic programming, showing that our technique clearly outperforms these methods.