Stochastic Models

One of the central problems in operations research and management science is how to quantify the effects of uncertainty about the future. This, the second volume in a series of handbooks, is devoted to models where chance events play a major role. The thirteen chapters survey topics in applied probability that have been particularly useful in operations research and management science. Each chapter was written by an expert, both in subject matter and in its exposition.

The chapters fall into four groups. The first four cover the fundamentals of stochastic processes, and lay the foundation for the following chapters. The next three chapters are concerned with methods of getting numbers. This includes numerical solution of models, parameter estimation for models, and simulation of models. Chapters 8 and 9 describe the fundamentals of dynamic optimization. The last four chapters are concerned with the most important structured models in operations research and management science; queues, queueing networks, inventories, and reliability.

Point Processes (R.F. Serfozo). Markov Processes (A.F. Karr). Martingales and Random Walks (H.M. Taylor). Diffusion Approximations (P.W. Glynn). Computational Methods in Probability Theory (W.K. Grassmann). Statistical Methods (J. Lehoczky). Simulation Experiments (B. Schmeiser). Markov Decision Processes (M.L. Puterman). Controlled Continuous-Time Markov Processes (R. Rishel). Queueing Theory (R.B. Cooper). Queueing Networks (J. Walrand). Stochastic Inventory Theory (E.L. Porteus). Reliability and Maintainability (M. Shaked, J.G. Shanthikumar). Subject Index.