The Piggyback Transportation Problem: Transporting drones launched from a flying warehouse

• Published in: European journal of operational research Vol. 296; no. 2; pp. 504 - 519
• Main Authors: Wang, Kai, Pesch, Erwin, Kress, Dominik, Fridman, Ilia, Boysen, Nils
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-01-2022
• Subjects: Approximation algorithm; Logistics; Scheduling; Work sharing; Scheduling; Work sharing; Approximation algorithm; Logistics
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2021.03.064

•We formulate a novel transportation problem including two types of vehicles.•Small delivery vehicles are brought close to the customers by a large vehicle.•We present several theoretical insights regarding complexity and approximability.•We provide a simple, yet very efficient heuristic solution procedure.•From a managerial perspective, we explore how to best allocate investments.
This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.