A survey of the orienteering problem: model evolution, algorithmic advances, and future directions

TL;DR

This survey comprehensively reviews recent developments in the orienteering problem, highlighting model variants, solution methods, and future research directions, with a focus on AI and scalability.

Contribution

It introduces a unified taxonomy for OP variants and categorizes solution approaches, integrating recent advances in AI and identifying open challenges.

Findings

Unified framework for OP variants

Emphasis on AI and reinforcement learning methods

Identification of open challenges in robustness and sustainability

Abstract

The orienteering problem (OP) is a combinatorial optimization problem that seeks a path visiting a subset of locations to maximize collected rewards under a limited resource budget. This article presents a systematic PRISMA-based review of OP research published between 2017 and 2025, with a focus on models and methods that have shaped subsequent developments in the field. We introduce a component-based taxonomy that decomposes OP variants into time-, path-, node-, structure-, and information-based extensions. This framework unifies classical and emerging variants -- including stochastic, time-dependent, Dubins, Set, and multi-period OPs -- within a single structural perspective. We further categorize solution approaches into exact algorithms, heuristics and metaheuristics, and learning-based methods, with particular emphasis on matheuristics and recent advances in artificial intelligence, especially reinforcement learning and neural networks, which enhance scalability in large-scale and information-rich settings. Building on this unified view, we discuss how different components affect computational complexity and polyhedral properties and identify open challenges related to robustness, sustainability, and AI integration. The survey thus provides both a consolidated reference for existing OP research and a structured agenda for future theoretical and applied work.