Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms used to solve Markov models. Providing a unified treatment of Markov chains and Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers supplies a highly detailed description of the construction and solution of Markov models that facilitates their application to diverse processes. Organized around Markov chain structure, the book begins with descriptions of Markov chain states, transitions, structure, and models, and then discusses steady state distributions and passage to a target state in a regular Markov chain. The author treats canonical forms and passage to target states or to classes of target states for reducible Markov chains. He adds an economic dimension by associating rewards with states, thereby linking a Markov chain to a Markov decision process, and then adds decisions to create a Markov decision process, enabling an analyst to choose among alternative Markov chains with rewards so as to maximize expected rewards. An introduction to state reduction and hidden Markov chains rounds out the coverage. In a presentation that balances algorithms and applications, the author provides explanations of the logical relationships that underpin the formulas or algorithms through informal derivations, and devotes considerable attention to the construction of Markov models. He constructs simplified Markov models for a wide assortment of processes such as the weather, gambling, diffusion of gases, a waiting line, inventory, component replacement, machine maintenance, selling a stock, a charge account, a career path, patient flow in a hospital, marketing, and a production line. This treatment helps you harness the power of Markov modeling and apply it to your organization’s processes.

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms u

Discusses all the major tools and techniques for Decision Support System supported by examples Techniques are explained considering their deterministic and stochastic aspects Covers network tools including GERT and Q-GERT Explains application of both probability and fuzzy orientation in the pertinent techniques Includes a number of relevant case studies along with a dedicated chapter on software

Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students.

Discover techniques for inferring unknown variables and quantities with the second volume of this extraordinary three-volume set.

The second volume of this successful handbook represents varied perspectives on the fast-expanding field of Service Science. The novel work collected in these chapters is drawn from both new researchers who have grown-up with Service Science, as well as established researchers who are adapting their frames for the modern service context. The first Handbook of Service Science marked the emergence of Service Science when disciplinary studies of business-to-customer service systems intertwined to meet the needs of a new era of business-to-business and global service ecosystems. Today, the evolving discipline of Service Science involves advanced technologies, such as smartphones, cloud, social platforms, big data analytics, and artificial intelligence. These technologies are reshaping the service landscape, transforming both business models and public policy, ranging from retail and hospitality to transportation and communications. By looking through the eyes of today’s new Service Scientists, it is anticipated that value and grand challenges will emerge from the integration of theories, methods, and techniques brought together in the first volume, but which are now rooted more deeply in service-dominant logic and systems thinking in this second volume. The handbook is divided into four parts: 1) Service Experience--On the Human-centered Nature of Service; 2) Service Systems–On the Nature of Service Interactions; 3) Service Ecosystems–On the Broad Context of Service; 4) Challenges–On Rethinking the Theory and Foundations of Service Science. The chapters add clarity on how to identify, enable, and measure service, thus allowing for new ideas and connections made to physics, design, computer science, and data science and analytics for advancing service innovation and the welfare of society. Handbook of Service Science, Volume II offers a thorough reference suitable for a wide-reaching audience including researchers, practitioners, managers, and students who aspire to learn about or to create a deeper scientific foundation for service design and engineering, service experience and marketing, and service management and innovation.

Operations Research: A Practical Introduction is just that: a hands-on approach to the field of operations research (OR) and a useful guide for using OR techniques in scientific decision making, design, analysis and management. The text accomplishes two goals. First, it provides readers with an introduction to standard mathematical models and algorithms. Second, it is a thorough examination of practical issues relevant to the development and use of computational methods for problem solving. Highlights: All chapters contain up-to-date topics and summaries A succinct presentation to fit a one-term course Each chapter has references, readings, and list of key terms Includes illustrative and current applications New exercises are added throughout the text Software tools have been updated with the newest and most popular software Many students of various disciplines such as mathematics, economics, industrial engineering and computer science often take one course in operations research. This book is written to provide a succinct and efficient introduction to the subject for these students, while offering a sound and fundamental preparation for more advanced courses in linear and nonlinear optimization, and many stochastic models and analyses. It provides relevant analytical tools for this varied audience and will also serve professionals, corporate managers, and technical consultants.