Optimal scheduling and quantitative analysis for multi-flying warehouse scheduling problem: Amazon airborne fulfillment center

Abstract

The outstanding advantages of unmanned aerial vehicles (UAVs) have led to an intensive study of the UAV system over the past decade. A variety of potential UAV applications are emerging nowadays, and UAV delivery is one in the limelight. However, one of the critical issues for UAV delivery service is how UAVs can be persistently used within their fundamental limited battery capacity. One recent approach to alleviate the weaknesses is using a cooperative system of UAVs with movable stations for the delivery logistics. The movable stations move across the field of operation, providing persistent delivery service using UAVs by efficiently replenishing the consumables of UAVs. In this study, one of the compelling cooperative delivery services called the flying warehouse system is investigated. The concept of the flying warehouse delivery service has firstly filed as a patent by the e-commerce giant Amazon. The service utilizes airborne fulfillment centers (AFCs), an airship that floats at an altitude of around 45,000. The airship has stocked with inventory, and when a customer places an order, UAVs fly down and deliver the package. The AFC is a novel delivery system that allows delivery in under 10 min and expands Amazon Prime members' access to UAV shipping. On the other hand, AFC is a challenging system that must extract the simultaneous operating schedule of the different system elements. For this purpose, mixed integer linear programming (MILP) is developed. The proposed MILP enables the derivation of service schedules of multiple AFCs and their components. The validity of the MILP is tested through case studies and quantitatively investigated system managerial issues. In addition, we proposed a clustering-based solution approach to provide computational efficiency for real-world case problems.