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Mathematics of Stochastic Manufacturing Systems
Edited by: G. George Yin, Wayne State University, Detroit, MI, and Qing Zhang, University of Georgia, Athens

Lectures in Applied Mathematics
1997; 399 pp; softcover
Volume: 33
ISBN-10: 0-8218-0755-2
ISBN-13: 978-0-8218-0755-2
List Price: US$89
Member Price: US$71.20
Order Code: LAM/33
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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.


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.

Table of Contents

  • F. Avram -- Optimal control of fluid limits of queuing networks and stochasticity corrections
  • J. S. Baras and N. S. Patel -- Robust control of semiconductor manufacturing processes
  • E. K. Boukas and J. P. Kenne -- Maintenance and production control of manufacturing systems with setups
  • W. K. Ching and X. Y. Zhou -- Optimal \((s,S)\) production policies with delivery time guarantees
  • T. E. Duncan -- Identification and control of a stochastic manufacturing system with noisy demand
  • K. B. Ensor and P. W. Glynn -- Stochastic optimization via grid search
  • J. A. Filar and A. Haurie -- Optimal ergodic control of singularly perturbed hybrid stochastic systems
  • S. Hadjihassan, L. Pronzato, E. Walter, and I. Vuchkov -- Robust design for quality improvement by ellipsoidal bounding
  • C. Humes, Jr. -- Linear programming derived functional bounds for closed queueing networks: A primal approach
  • M. Lefebvre and R. Labib -- Risk sensitive optimal control of wear processes
  • S. Meyn -- Stability and optimization of queueing networks and their fluid models
  • B. Pasik-Duncan -- Stochastic adaptive control and manufacturing systems
  • G. Ch. Pflug -- Coupling, ergodicity and sensitivity of Markov processes
  • E. L. Presman, S. P. Sethi, and W. Suo -- Optimal feedback controls in dynamic stochastic jobshops
  • L. Pronzato, H. P. Wynn, and A. A. Zhigljavsky -- Using Renyi entropies to measure uncertainty in search problems
  • R. Rishel -- The role of information in scheduling machines to supply a production line
  • S. P. Sethi -- Some insights into near-optimal plans for stochastic manufacturing systems
  • M. S. Yang, L. Lee, and Y.-C. Ho -- On stochastic optimization and its applications to manufacturing
  • D. D. Yao and L. Zhang -- Stochastic scheduling via polymatroid optimization
  • J. J. Ye -- Dynamic programming and the maximum principle for control of piecewise deterministic Markov processes
  • N. F. Zhang -- Autocorrelation analysis of some linear transfer function models and its applications in the dynamic process systems
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