# A Sim-Learnheuristic for the Team Orienteering Problem: Applications to Unmanned Aerial Vehicles
## 2 Related Work
The learnheuristic approach involves training a neural network to predict the quality of solutions obtained by a metaheuristic algorithm and using this prediction to guide the search toward promising regions of the solution space. Focusing on the TOP, several articles have been published in recent years. Notably, [PERSON] et al. (2019) employed an adaptive algorithm based on the Markov decision process to decide whether to continue the route or take a shortcut given a specific deadline. [PERSON] et al. (2020) discussed sensor assignment and routing for UAVs to maximize intelligence gathering, considering constraints like a limited battery and sensor weight. They proposed a detailed plan using mathematical models and local search strategies, with column generation for efficiency in larger missions. Likewise, [PERSON] et al. (2021) presented a hybrid technique for solving the TOP in rescue operations using humanoid robots, aiming to optimize energy, task completion, and decision-making through a learning algorithm and an evolutionary multi-objective approach. [PERSON] et al. (2022) presented a machine learning approach to solving a stochastic TOP with time windows. The authors used a reinforcement learning algorithm, specifically the Q-learning algorithm, to learn how to make routing decisions for a deliveryman facing stochastic travel times and customer demands. Also, [PERSON] and [PERSON] (2023) presented a data-efficient deep reinforcement learning (DRL) approach to solve a multi-start TOP for UAV mission re-planning. The authors propose a method that learns to adapt to changing conditions by iteratively updating a policy network through experience gained from previous UAV mission planning problems. [PERSON] et al. (2024) discussed a novel application of electric vehicles in TOP systems, focusing on charging energy-critical sensors using mobile chargers. Due to charger limitations, visiting all sensors was restricted, and costs were assigned considering charger energy consumption and travel. Different vehicle types and their capabilities were also considered, with sensors possibly visited by multiple vehicles, affecting profit margins. An approximation algorithm was proposed initially and later refined for handling vehicles of the same type. In contrast, [PERSON] et al. (2025) introduced a concurrent multi-threaded branch-and-price algorithm with acceleration techniques for the TOP involving fixed-wing drones. They addressed the kinematic constraints limiting drones' maneuverability, particularly quick turns and minimum turn radii. While their approach achieved optimal solutions for most cases, its reliance on exact methods hindered real-time solutions, crucial for dynamic systems. [PERSON] et al. (2026) focused on the dynamic and stochastic orienteering problem in autonomous transportation systems. They introduced self-adaptive heuristic algorithms to optimize task execution for intelligent agents, maximizing rewards and ensuring timely returns to the starting location within a limited time frame. The orienteering problem enables agents to selectively visit spots and optimize routing sequences for reward maximization. The study proposed a hybrid simulated annealing-tabu search algorithm for initial routing plans and three multi-stage optimization strategies for real-time adjustments. Simulation experiments demonstrated the effectiveness of the algorithm, improving the solution quality and ensuring timely arrivals for agents. [PERSON] and [PERSON] (2017) proposed a novel multi-stage metaheuristic algorithm to efficiently tackle large orienteering problem instances. The metaheuristic partitions a potentially large set of candidate sites into smaller clusters, where a solver is used to find near-optimal solutions within a bounded time. These solutions are then merged and optimized to provide a final high-quality solution.