2020/02/08 by Suttinee Sawadsitang, Dusit Niyato, Sawadsitang, Suttinee +7 · 1 citation
Business, Management and Accounting · Engineering · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Mathematics #Facility Location and Emergency Management #Optimization and Control (math.OC) #Transportation and Mobility Innovations #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2002.03118
openalex publication_date 2020/02/08 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
With the recent technological innovation, unmanned aerial vehicles, known as\ndrones, have found numerous applications including package and parcel delivery\nfor shippers. Drone delivery offers benefits over conventional ground-based\nvehicle delivery in terms of faster speed, lower cost, more\nenvironment-friendly, and less manpower needed. However, most of existing\nstudies on drone delivery planning and scheduling focus on a single shipper and\nignore uncertainty factors. As such, in this paper, we consider a scenario that\nmultiple shippers can cooperate to minimize their drone delivery cost. We\npropose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD)\nframework. The framework is composed of three functions, i.e., package\nassignment, shipper cooperation formation and cost management. The\nuncertainties of drone breakdown and misbehavior of cooperative shippers are\ntaken into account by using multistage stochastic programming optimization and\ndynamic Bayesian coalition formation game. We conduct extensive performance\nevaluation of the BCoSDD framework by using customer locations from Solomon\nbenchmark suite and a real Singapore logistics industry. As a result, the\nframework can help the shippers plan and schedule their drone delivery\neffectively.\n