Orienteering Problem

The Orienteering Problem (OP) is a combinatorial optimization challenge focusing on finding the most rewarding route within a given budget, often representing time or resources. Current research emphasizes extensions of the classic OP to handle stochasticity (e.g., uncertain travel times or rewards), dynamism (e.g., changing conditions), and multiple agents or vehicles, often employing techniques like Monte Carlo Tree Search, deep reinforcement learning, and advanced heuristics such as Large Neighborhood Search. These advancements are driving improvements in real-world applications like autonomous vehicle routing, sensor deployment, and resource allocation in dynamic environments, impacting fields ranging from logistics to environmental monitoring.