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Maintenance serves a critical role in manufacturing systems by ensuring that machines and other assets remain in a productive working condition. The primary objective of maintenance optimization is to determine when to conduct maintenance and which machines should be maintained. Recent advances in industrial maintenance have sought to use online information obtained from sources such as machine sensors and manufacturing execution system software to provide real-time decision support. Such predictive maintenance strategies combine abundant online manufacturing data with techniques in simulation, planning, and artificial intelligence to make effective maintenance decisions and support the overall performance of the system. In this dissertation we examine several challenges associated with adopting real-time maintenance decision support in complex manufacturing systems. One such challenge is that of modeling complex machine configurations that are often found in modern manufacturing systems. It can be difficult or impossible to model the behavior of these systems analytically without imposing unrealistic simplifying assumptions. One of the goals of this work is therefore to propose a method of maintenance optimization and planning that is generalizable to arbitrarily configured systems. We also introduce a discrete-event simulation package that has been developed as a part of this work and is capable of modeling these systems of interest. Additionally, real-world manufacturing systems are typically subject to constraints on available maintenance resources which limits the number of maintenance jobs that may be conducted simultaneously. In these settings, the maintenance planner must determine how to prioritize competing maintenance activities and allocate these limited resources throughout the system. This work addresses these challenges by proposing a simulation-based maintenance optimization and planning approach to seek an optimal maintenance policy and prioritize maintenance in complex systems. We formulate condition-based maintenance policy optimization as a discrete optimization via simulation problem and seek a solution using the Gaussian Markov Improvement Algorithm and a genetic algorithm. The result is a degradation threshold for each machine in the system that determines when a machine should request maintenance. To overcome the problem of capacity-constrained maintenance resources, we first propose a dynamic priority scheduling heuristic that aims to minimize throughput disruption due to downtime for maintenance. We then improve upon this scheduling heuristic by employing a reinforcement learning approach to seek the best maintenance action in each state of the system. We use Monte Carlo tree search to progressively build a search tree within the system state space and evaluate alternative sequences of actions in order to find that which maximizes the expected reward. We demonstrate that our proposed method of online prioritization results in improved system-level performance when compared to commonly used maintenance prioritization methods. Furthermore, we apply a case-based reasoning framework to retain and reuse relevant experience that improves the decision-making efficiency over time. In addition to improved system productivity, the proposed approach results in reduced time needed to identify optimal maintenance actions which is particularly important when critical maintenance decisions must be made quickly.
In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics. Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling. With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields. Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.
Monte Carlo Tree Search (MCTS) is an algorithmic technique utilized in reinforcement learning, a subfield of artificial intelligence, that combines tree-based search and random sampling for decision-making in uncertain environments. Although MCTS has been successfully used for playing complex games such as Chess and Go, without customizing the original algorithm using domain knowledge, it is unable to effectively solve complex supply chain problems. This study proposes a number of augmenting mechanisms for MCTS to be utilized in managing service level agreements. The results demonstrate that even in the case of non-stationary demand, where the majority of optimization methods reach their limitations, engaging the suggested augmentation mechanisms significantly improves the performance of MCTS.
This book constitutes the thoroughly refereed post-proceedings of the 5th International Conference on Computers and Games, CG 2006, co-located with the 14th World Computer-Chess Championship and the 11th Computer Olympiad. The 24 revised papers cover all aspects of artificial intelligence in computer-game playing. Topics addressed are evaluation and learning, search, combinatorial games and theory opening and endgame databases, single-agent search and planning, and computer Go.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Information technology (IT) is widely understood to be the enabling technology of the 21st century. IT has transformed, and continues to transform, all aspects of our lives: commerce and finance, education, energy, health care, manufacturing, government, national security, transportation, communications, entertainment, science, and engineering. IT and its impact on the U.S. economyâ€"both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)â€"continue to grow in size and importance. IT’s impacts on the U.S. economyâ€"both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)â€"continue to grow. IT enabled innovation and advances in IT products and services draw on a deep tradition of research and rely on sustained investment and a uniquely strong partnership in the United States among government, industry, and universities. Past returns on federal investments in IT research have been extraordinary for both U.S. society and the U.S. economy. This IT innovation ecosystem fuels a virtuous cycle of innovation with growing economic impact. Building on previous National Academies work, this report describes key features of the IT research ecosystem that fuel IT innovation and foster widespread and longstanding impact across the U.S. economy. In addition to presenting established computing research areas and industry sectors, it also considers emerging candidates in both categories.
With contributions by experts from around the world, the Handbook of Condition Monitoring provides comprehensive coverage of the four main techniques used in condition monitoring.
Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.