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Operations research.

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This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car. Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.

Operations research. --- Decision making. --- Management science. --- Probabilities. --- Operations Research/Decision Theory. --- Operations Research, Management Science. --- Probability Theory and Stochastic Processes.

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This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car. Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.

Operations research. --- Decision making. --- Management science. --- Probabilities. --- Operations Research/Decision Theory. --- Operations Research, Management Science. --- Probability Theory and Stochastic Processes.

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Industrial management. --- Operations research. --- Business logistics.

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Sharing accurate and timely supply and demand information throughout a supply chain can yield significant performance improvements to all members of the supply chain. Despite the benefits, many firms are reluctant to share information with their supply chain partners due to an unequal distribution of risks, costs, and benefits among the partners. Thus, incentive mechanisms must be in place to induce communication, cooperation, and collaboration among all members of a supply chain. The issue of information exchange/sharing has been examined by various researchers over the last 15-20 years. However, there is no research book that compiles various approaches, analyses, key implications, as well as future development of this area. This book will serve as a handbook for researchers who are interested in learning the state of the art of the line of research in this area and explore open research topics in this area. Chapter authors, all leading researchers, have contributed 18 chapters broken into four distinct sections covering the Value of Information Sharing, Contracting and Information, Information Signaling, and Incentives for Information Sharing.

Operations research. --- Decision making. --- Industrial procurement. --- Operations Research/Decision Theory. --- Procurement.