A Course on Queueing Models

Discusses the basics in queueing theory for Markovian and regenerative non-Markovian models, statistical inference, simulation and some computational procedures, network and discrete-time queues, algebraic and combinatorial methods, and optimization. This book also covers queues, duality in queues, and advancements on computational methods.

The application of engineering principles in divergent fields such as management science and communications as well as the advancement of several approaches in theory and computation have led to growing interest in queueing models, creating the need for a comprehensive text. Emphasizing Markovian structures and the techniques that occur in different models, A Course on Queueing Models discusses recent developments in the field, different methodological tools - some of which are not available elsewhere - and computational techniques.

While most books essentially address the classical methods of queueing theory, this text covers a broad range of methods both in theory and in computation. The first part of the textbook exposes you to many fundamental concepts at an introductory level and provides tools for practitioners. It discusses the basics in queueing theory for Markovian and regenerative non-Markovian models, statistical inference, simulation and some computational procedures, network and discrete-time queues, algebraic and combinatorial methods, and optimization. The second part delves deeper into the topics examined in the first part by presenting more advanced methods. This part also includes general queues, duality in queues, and recent advancements on computational methods and discrete-time queues. Each chapter contains a discussion section that summarizes material and highlights special features.

Incorporating different queueing models, A Course on Queueing Models achieves an ideal balance between theory and practice, making it compatible for advanced undergraduate and graduate students, applied statisticians, and engineers.