In April 2016, Amazon won a patent for a flying warehouse that deploys unmanned aerial vehicles (UAVs) to deliver parcels to customers. In this system, the airborne fulfillment center (AFC) remains or moves at a high altitude, and UAVs are deployed from the AFC to deliver orders to customers. Smaller airships, also known as shuttles, may be used to replenish the AFC with inventory, UAVs, fuel, supplies, and staff. Amazon’s AFC delivery system is a novel and challenging system that can convert same-day deliveries into 10 min deliveries and provide a wide range of UAV delivery services for Amazon Prime members. However, to operate the AFC delivery system efficiently, a quantitative operation methodology needs to be developed, which considers the related regulations and consumable replenishment. In this study, mixed-integer linear programming is developed to derive optimal operation schedules for AFC, shuttles, and UAVs simultaneously. The proposed model can be used in real time to address system changes and derive new operation schedules. The effects of system components are analyzed via a numerical experiment to provide managerial guidelines for the implementation of the AFC delivery system. Furthermore, cooperation between the new AFC delivery system and the existing stationary UAV delivery service was investigated. The results demonstrate the possibility of complementary cooperation between the two systems.

Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system’s restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO₂ emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO₂ efficient in all ranges of the GV speeds investigated.

In reverse logistic networks, end-of-use products are collected and transported to the factory for remanufacturing. The economic and environmental values of a reverse network depend significantly on the quality of the end-of-use products and the remanufacturing policies. Therefore, it is desirable to consider the minimum quality level of the remanufacturing and the remanufacturing policy during the initial design stage. A redesign of the reverse logistics network is addressed based on decisions made regarding the location of the collection facility, the allocation, the minimum quality level and the remanufacturing policies. Based on the known continuous distribution for the quality of an end-of-use product, a bi-objective mathematical model was developed. A mathematical model was then transformed into mixed integer programmes using quadratic terms in the constraints through the development of a discretisation method. To obtain Pareto solutions of a bi-objective model, a quality constraint method was developed by relaxing the non-linearity and complexity of the original epsilon constraint method. The proposed mathematical model and algorithm were able to redesign reverse logistics network throughout the entire processes of collection, transportation and remanufacturing. The validity of the model and accessibility for a large size problem demonstrated through numerical experiments are described herein.