UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

AVMf: An Open-Source Framework and Implementation of the Alternating Variable Method

The Alternating Variable Method (AVM) has been shown to be a fast and effective local search technique for search-based software engineering. Recent improvements to the AVM have generalized the representations it can optimize and have provably reduced its running time. However, until now, there has been no general, publicly-available implementation of the AVM incorporating all of these developments. We introduce AVMf, an object-oriented Java framework that provides such an implementation. AVMf is available from http://avmframework.org for configuration and use in a wide variety of projects

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Trusting AI: Integrating Artificial Intelligence into the Army’s Professional Expert Knowledge

Integrating artificially intelligent technologies for military purposes poses a special challenge. In previous arms races, such as the race to atomic bomb technology during World War II, expertise resided within the Department of Defense. But in the artificial intelligence (AI) arms race, expertise dwells mostly within industry and academia. Also, unlike the development of the bomb, effective employment of AI technology cannot be relegated to a few specialists; almost everyone will have to develop some level of AI and data literacy. Complicating matters is AI-driven systems can be a “black box” in that humans may not be able to explain some output, much less be held accountable for its consequences. This inability to explain coupled with the cession to a machine of some functions normally performed by humans risks the relinquishment of some jurisdiction and, consequently, autonomy to those outside the profession. Ceding jurisdiction could impact the American people’s trust in their military and, thus, its professional standing. To avoid these outcomes, creating and maintaining trust requires integrating knowledge of AI and data science into the military’s professional expertise. This knowledge covers both AI technology and how its use impacts command responsibility; talent management; governance; and the military’s relationship with the US government, the private sector, and society.https://press.armywarcollege.edu/monographs/1955/thumbnail.jp

Breakthroughs and emerging insights from ongoing design science projects: Research-in-progress papers and poster presentations from the 11th international conference on design science research in information systems and technology (DESRIST) 2016. St. John, Newfoundland, Canada, May 23-25

This volume contains selected research-in-progress papers and poster presentations from DESRIST 2016 - the 11th International Conference on Design Science Research in Information Systems and Technology held during 24-25 May 2016 at St. John's, Newfoundland, Canada. DESRIST provides a platform for researchers and practitioners to present and discuss Design Science research. The 11th DESRIST built on the foundation of ten prior highly successful international conferences held in Claremont, Pasadena, Atlanta, Philadelphia, St. Gallen, Milwaukee, Las Vegas, Helsinki, Miami, and Dublin. This year's conference places a special emphasis on using Design Science to engage with the growing challenges that face society, including (but not limited to) demands on health care systems, climate change, and security. With these challenges in mind, individuals from academia and industry came together to discuss important ongoing work and to share emerging knowledge and ideas. Design Science projects often involve multiple sub-problems, meaning there may be a delay before the final set of findings can be laid out. Hence, this volume "Breakthroughs and Observations from Ongoing Design Science Projects" presents preliminary findings from studies that are still underway. Completed research from DESRIST 2016 is presented in a separate volume entitled "Tackling Society's Grand Challenges with Design Science", which is published by Springer International Publishing, Switzerland. The final set of accepted papers in this volume reflects those presented at DESRIST 2016, including 11 research-in-progress papers and 4 abstracts for poster presentations. Each research-in-progress paper and each poster abstract was reviewed by a minimum of two referees. We would like to thank the authors who submitted their research-in-progress papers and poster presentations to DESRIST 2016, the referees who took the time to construct detailed and constructive reviews, and the Program Committee who made the event possible. Furthermore we thank the sponsoring organisations, in particular Maynooth University, Claremont Graduate University, and Memorial University of Newfoundland, for their financial support. We believe the research described in this volume addresses some of the most topical and interesting design challenges facing the field of information systems. We hope that readers find the insights provided by authors as valuable and thought-provoking as we have, and that the discussion of such early findings can help to maximise their impact

Service Consistency in Vehicle Routing

This thesis studies service consistency in the context of multi-period vehicle routing problems (VRP) in which customers require repeatable services over a planning horizon of multiple days. Two types of service consistency are considered, namely, driver consistency and time consistency. Driver consistency refers to using the fewest number of different drivers to perform all of the visits required by a customer over a planning horizon and time consistency refers to visiting a customer at roughly the same time on each day he/she needs service. First, the multi-objective consistent VRP is defined to explore the trade-offs between the objectives of travel cost minimization and service consistency maximization. An improved multi-objective optimization algorithm is proposed and the impact of improving service consistency on travel cost is evaluated on various benchmark instances taken from the literature to facilitate managerial decision making. Second, service consistency is introduced for the first time in the literature to the periodic vehicle routing problem (PVRP). In the PVRP, customers may require multiple visits over a planning horizon, and these visits must occur according to an allowable service pattern. A service pattern specifies the days on which the visits required by a customer are allowed to occur. A feasible service pattern must be determined for each customer before vehicle routes can be optimized on each day. Various multi-objective optimization approaches are implemented to evaluate their comparative competitiveness in solving this problem and to evaluate the impact of improving service consistency on the total travel cost. Third, a branch-and-price algorithm is developed to solve the consistent vehicle routing problem in which service consistency is enforced as a hard constraint. In this problem, the objective is to minimize the total travel cost. New constraints are devised to enhance the original mixed integer formulation of the problem. The improved formulation outperforms the original formulation regarding CPLEX solution times on all benchmark instances taken from the literature. The proposed branch-and-price algorithm is shown to be able to solve instances with more than fourteen customers more efficiently than either the existing mixed integer formulation or the one we propose in this paper

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks