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Stochastic Modelling in Innovative Manufacturing

Proceedings, Cambridge, U.K., July 21–22, 1995

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Anthony H. Christer

    1. Department of Mathematics and Computer Science, University of Salford, Lancs., UK

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  2. Shunji Osaki

    1. Department of Industrial and Systems Engineering, Hiroshima University, Higashi-Hiroshima, Japan

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    You can also search for this editor in PubMed   Google Scholar

  3. Lyn C. Thomas

    1. Department of Business Studies, The University of Edinburgh, Edinburgh, UK

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Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 445)

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Table of contents (27 papers)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. An Optimal Running Policy for Buffered Continuous Production Processes Subject to Random Breakdown

    • Russell C. H. Cheng
    Pages 1-18

  3. On the Greedy Algorithm for Stochastic Optimization Problems

    • Masaaki Kijima, Akihisa Tamura
    Pages 19-29

  4. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications

    • E. J. Collins
    Pages 30-45

  5. Stochastic Petri Net Model of an Ethernet-Based Manufacturing System

    • Qun Jin, Yoneo Yano, Yoshio Sugasawa
    Pages 46-57

  6. Games of Production Development in Manufacturing

    • Yoshinobu Teraoka, Yasuyoshi Yamada
    Pages 58-67

  7. Voting Procedure on Stopping Games of Markov Chain

    • Krzysztof Szajowski, Masami Yasuda
    Pages 68-80

  8. Risk and Return Analysis of a Multi-Period Strategic Planning Problem

    • C. Lucas, E. Messina, G. Mitra
    Pages 81-96

  9. Input/Output Backlog Control and Dynamic Capacity Planning in Versatile Manufacturing Companies

    • Brian G. Kingsman
    Pages 97-122

  10. Algorithms for Allocating Resources to Multi-Stage Pharmaceutical Research Projects

    • John Gittins
    Pages 123-139

  11. An Optimal Selection Problem on a Partially Observable Markov Chain

    • Tōru Nakai
    Pages 140-154

  12. Generalizing the Secretary Problem with Rank-dependent Rejection Probability

    • Mitsushi Tamaki
    Pages 155-165

  13. Performance Evaluation of Nonparametric Age Replacement Methods —Survey and Comparison —

    • Tadashi Dohi, Takashi Aoki, Naoto Kaio, Shunji Osaki
    Pages 166-187

  14. Replacement Policies for a Shock Model with Two Kinds of Damage

    • Takashi Satow, Toshio Nakagawa
    Pages 188-195

  15. Stochastic maintenance modelling of high-tech steel production plant

    • A. H. Christer, W. Wang, J. M. Sharp, R. D. Baker
    Pages 196-214

  16. An optimal maintenance policy for a deteriorating server of an M/G/1 queueing system

    • Junji Koyanagi, Hajime Kawai
    Pages 215-224

  17. Growth and Innovation through Reliability Testing

    • J. I. Ansell, J. Quigley, L. A. Walls
    Pages 225-235

  18. Optimal Times of Burn-In Tests for Multilevel Assembly Lines

    • Kodo Ito, Kazuyuki Teramoto, Toshio Nakagawa
    Pages 236-245

  19. Markovian Software Availability Modeling for Performance Evaluation

    • Koichi Tokuno, Shigeru Yamada
    Pages 246-256

  20. Software Reliability Growth Models for Discrete and Incomplete Testing

    • Rose Baker
    Pages 257-271
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Keywords

About this book

This monograph contains some ofthe papers presented at a UK-Japanese Workshop on Stochastic Modelling in Innovative Manufacturing held at Churchill College, Cambridge on July 20 and 21st 1995, sponsored jointly by the UK Engineering and Physical Science Research Council and the British Council. Attending were 19 UK and 24 Japanese delegates representing 28 institutions. The aim of the workshop was to discuss the modelling work being done by researchers in both countries on the new activities and challenges occurring in manufacturing. These challenges have arisen because of the increasingly uncertain environment of modern manufacturing due to the commercial need to respond more quickly to customers demands, and the move to just-in-time manufacturing and flexible manufacturing systems and the increasing requirements for quality. As well as time pressure, the increasing importance of the quality of the products, the need to hold the minimum stock of components, and the importance of reliable production systems has meant that manufacturers need to design production systems that perform well in randomly varying conditions and that their operating procedures can respond to changes in conditions and requirements. This has increased the need to understand how manufacturing systems work in the random environments, and so emphasised the importance of stochastic models of such systems.

Editors and Affiliations

  • Department of Mathematics and Computer Science, University of Salford, Lancs., UK

    Anthony H. Christer

  • Department of Industrial and Systems Engineering, Hiroshima University, Higashi-Hiroshima, Japan

    Shunji Osaki

  • Department of Business Studies, The University of Edinburgh, Edinburgh, UK

    Lyn C. Thomas

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