**Lectures in Applied Mathematics**

Volume: 33;
1997;
399 pp;
Softcover

MSC: Primary 93; 90; 60;
Secondary 62

**Print ISBN: 978-0-8218-0755-2
Product Code: LAM/33**

List Price: $89.00

Individual Member Price: $71.20

# Mathematics of Stochastic Manufacturing Systems

Share this page *Edited by *
*G. George Yin; Qing Zhang*

This volume presents the proceedings of the 26th AMS-SIAM
Summer Seminar in Applied Mathematics, “The Mathematics of
Stochastic Manufacturing Systems”, held in June 1996 at the College
of William and Mary (Williamsburg, VA).

Manufacturing is facing rapidly growing challenges in the global
marketplace. As an ever-growing discipline, its research involves a
wide spectrum of techniques that go far beyond traditional applied
mathematics. Manufacturing research cuts across the disciplines of
operations research, management science, industrial engineering,
systems theory, and applied mathematics. At the forefront of this
interdisciplinary area, research in mathematical and computational
sciences has become indispensable in the development of new technology
and the improvement of existing techniques and management
practices.

In this volume, leading experts in mathematical manufacturing
research and related fields review and update recent advances in
mathematics of stochastic manufacturing systems and attempt to bridge
the gap between theory and applications. The topics covered include
scheduling and production planning, modeling of manufacturing systems,
hierarchical control for large and complex systems, Markov chains,
queuing networks, numerical methods for system approximations,
singular perturbed systems, risk-sensitive control, stochastic
optimization methods, discrete event systems, and statistical quality
control.

This book presents research problems, techniques for dealing with
problems, and future directions. The interdisciplinary nature is of
great advantage to the applied mathematics and manufacturing research
communities.

#### Table of Contents

# Table of Contents

## Mathematics of Stochastic Manufacturing Systems

#### Readership

Graduate students and research mathematicians interested in applied mathematics, applied probability, operations research, operations management, control theory, engineering and for researchers and practitioners in manufacturing and related fields.