"Delivery drones may become widespread over the next five to ten years, particularly for what is known as the "last-mile" logistics of small, light items. Companies like Amazon, Google, the United Parcel Service, DHL, and Alibaba have been running high-profile experiments testing drone delivery systems, and the development of such systems reached a milestone when the first commercial drone delivery approved by the Federal Aviation Administration took place on July 17, 2015. In the future, drones could augment, or even replace, truck fleets and could have important implications for energy consumption, public safety, personal privacy, air pollution, city noise, air traffic management, road congestion, urban planning, and goods and service consumption patterns in urban areas. To support developing issues in this domain, the RAND Corporation launched an exploratory study that brings together RAND's expertise in unmanned aerial vehicle operations, transportation research, systems analysis, and behavioral analysis and applies it to this emerging and underexplored research area. In this report, we provide a simple simulation of the total energy-use impact of shifting the most suitable (lightest total weight) 20 percent of the United Parcel Service (UPS) package delivery stops in Minneapolis from traditional UPS trucks to delivery drones. The reduced number of stops would allow for a smaller truck delivery fleet delivering to fewer service areas"--Publisher's description.

This book provides an introduction to the use of unmanned aerial vehicles (UAVs) for the geographic observation and spatial analysis of urban areas. The velocity of urban change necessitates observation platforms that not only enhance situational awareness for planning and allied analytical efforts, but also provide the ability to rapidly and inexpensively collect data and monitor change. UAVs can accomplish both of these tasks, but their use in urban environments is loaded with social, operational, regulatory and technical challenges that must be addressed for successful deployments. The book provides a resource for educators and students who work with geographic information and are seeking to enhance these data with the use of unmanned aerial vehicles. Topics covered include, 1) a primer on UAVs and the many different ways they can be used for geographic observation, 2) a detailed overview on the use of aviation maps and charts for operating UAVs in complex urban airspace, 3) techniques for integrating UAV-derived data with more traditional geographic information, 4) application of spatial analytical tools for urban and environmental planning, and 5) an exploration of privacy and public safety issues associated with UAV operation.

This book investigates Unmanned Aircraft Systems (UAS) with a payload capacity of one metric ton for transportation. The authors provide a large variety of perspectives–from economics to technical realization. With the focus on such heavy-lift cargo UAS, the authors consider recently established methods for approval and certification, which they expect to be disruptive for unmanned aviation. In particular, the Specific Operations Risk Assessment (SORA) and its impact on the presented technological solutions and operational concepts are studied. Starting with the assumption of an operation over sparsely populated areas and below common air traffic, diverse measures to further reduce operational risks are proposed. Operational concepts derived from logistics use-cases set the context for an in-depth analysis including aircraft and system design, safe autonomy as well as airspace integration and datalinks. Results from simulations and technology demonstrations are presented as a proof of concept for solutions proposed in this book.

Land transportation is generally considered one of the most expensive, polluting and least efficient parts of the logistics chain. Due to these issues, using unmanned aerial vehicles such as drones for package delivery in last-mile logistics becomes increasingly attractive. However, there are several significant obstacles in terms of technical aspects and performance capabilities of drones like limited flight coverage. In addition, supply chains are exposed to a broad range of uncertainties some of which may cause disruptions in the whole supply chain system. To hedge against these issues, a well-designed reliable network is a top priority. Most existing models for optimization within logistics chain are deterministic, lack reliability, or they are not computationally efficient for larger problems. This dissertation aims to improve the reliability and efficiency of the supply chain network through the development of stochastic optimization models and methods to help address important problems related to delivery of products using drones. To achieve this goal, this study has developed a generalized optimization model that captures the dynamic and stochastic nature of problems by using stochastic optimization and stochastic control. At first, this study addresses issues bordering on capacitated supply chain problems, specifically on how reliable supply chain networks can be designed in the face of random facility disruptions and uncertain demand. The proposed multi-period capacitated facility location and allocation problem is modeled as a two-stage stochastic mixed-integer formulation that minimizes the total establishing and transportation cost. To overcome the complexity of the model, the L-shaped method of stochastic linear programming is applied by integrating two types of optimality and feasibility cuts for solving the stochastic model. This research improves the proposed algorithm in two ways: replacing the single-cut approach with a multi-cut and showing relatively complete recourse in the stochastic model by reformulating the original model. According to computational results, the proposed solution algorithm solves large-scale problems while avoiding long run times as well. It is also demonstrated that substantial improvements in reliability of the system can often be possible with minimal increases in facility cost. Next, this research aims to construct a feasible delivery network consisting of warehouses and recharging stations through the development of a stochastic mixed-integer model, resulting in improving the coverage and reliability of the supply chain network. Due to the computational complexity of the scenario-based mixed-integer model, this research improves the performance of the genetic algorithm by considering each scenario independently in one of the steps of the algorithm to significantly improve the computational time need to find the solutions. Computational results demonstrate that the proposed algorithm is efficiently capable of solving large-scale problems. Finally, this dissertation analyzes tradeoffs related to charging strategies for recharging stations which can be viewed as warehouses in last-mile logistics with capacity constraints and stochastic lead times. To enhance delivery time, this research assumes that extra batteries are available at the recharging station where individual drones land when they run out of power and swap empty batteries with fully charged ones. Stochastic Markov decision models are formulated to handle stochasticity in the problem and determine the optimal policy for decision-makers by applying a policy iteration algorithm. To overcome of computational challenges, a novel approximation method called the decomposition-based approach is proposed to split the original Markov decision problem for the system with N states into N independent Markov chain processes. Through numerical studies, this dissertation demonstrates that the proposed solution algorithm is not only capable of solving large-scale problems, but also avoids long run times. It is also demonstrated how different stochastic rate like flight or demand, and inventory and backorder costs can affect the optimal decisions.

This book gathers the proceedings of the 14th International Conference on Management Science and Engineering Management (ICMSEM 2020). Held at the Academy of Studies of Moldova from July 30 to August 2, 2020, the conference provided a platform for researchers and practitioners in the field to share their ideas and experiences. Covering a wide range of topics, including hot management issues in engineering science, the book presents novel ideas and the latest research advances in the area of management science and engineering management. It includes both theoretical and practical studies of management science applied in computing methodology, highlighting advanced management concepts, and computing technologies for decision-making problems involving large, uncertain and unstructured data. The book also describes the changes and challenges relating to decision-making procedures at the dawn of the big data era, and discusses new technologies for analysis, capture, search, sharing, storage, transfer and visualization, as well as advances in the integration of optimization, statistics and data mining. Given its scope, it will appeal to a wide readership, particularly those looking for new ideas and research directions.