ABSTRACT Motivated by real‐world applications in logistics and post‐disaster relief, this study introduces a novel variant of the orienteering problem, termed the orienteering problem with heterogeneous drones (OP‐HD). In this problem, a single truck collaborates with multiple heterogeneous drones to serve a subset of customers. While the truck is stationed at a customer location, multiple drones can be launched simultaneously, each capable of performing multiple trips. The objective is to maximize the total collected profit within a limited time horizon. We formulate the OP‐HD as a mixed‐integer linear programming model and discuss potential extensions to accommodate broader application scenarios. To solve the problem efficiently, we develop an adaptive large neighborhood search (ALNS) heuristic with customized destroy and repair operators. Computational experiments based on classical Solomon benchmark instances are conducted to evaluate the effectiveness of the proposed ALNS algorithm, assess the advantages of the profit‐oriented OP‐HD model, and analyze the impact of key parameter settings. The results demonstrate that the truck‐drone cooperative system can serve more customers and achieve higher profits within a limited time horizon compared to a truck‐only system. Furthermore, the advantage of employing heterogeneous drones becomes more significant in scenarios where customers are spatially clustered. The results further show that time is the primary operational constraint, and that improving battery capacity, energy efficiency, and drone speed enhances system profitability. Finally, a case study based on delivery areas in North Carolina is conducted to demonstrate the practical applicability of the proposed model.
Cheng et al. (Thu,) studied this question.