Production Planning in Production Networks

Production Planning in Production Networks

Pierluigi Argoneto Giovanni Perrone Paolo Renna Giovanna Lo Nigro Manfredi Bruccoleri Sergio Noto La Diega Production Planning in Production Networks Models for Medium and Short-term Planning 123

Pierluigi Argoneto, Dr. Ing. Paolo Renna, Dr. Ing. Dipartimento di Ingegneria e Fisica dell Ambiente (DIFA) Università degli Studi della Basilicata Macchia Romana 85100 Potenza Manfredi Bruccoleri, Dr. Ing. Giovanni Perrone, Prof. Ing. Giovanna Lo Nigro, Prof. Ing. Sergio Noto La Diega, Prof. Ing. ISBN 978-1-84800-057-5 e-isbn 978-1-84800-058-2 DOI 10.1007/978-1-84800-058-2 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008921524 2008 Springer-Verlag London Limited Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copy-right Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: estudio Calamar S.L., Girona, Spain Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

Preface Globalisation is pushing manufacturing companies toward a more distributed production approach. Indeed, corporate manufacturing firms are spreading their production all over the world in order to stay close to the customers, while medium manufacturing firms organise themselves in networks in order to scale their production to a global level. This tendency is putting a lot of stress on production planning. Indeed, the more distributed production facilities are, the more difficult and complex production planning becomes. Both multi-plant facility and manufacturing networks require to be coordinated in order to reach effectiveness and efficiency required by the competitive arena. Most of the production planning tools, such as Advanced Planning and Scheduling (APS) tools, are designed to manage a centralised production, i.e. a production that is accomplished in a single plan. When it comes to managing a network of plants or a manufacturing network, the application of APS tools becomes complex, since the complexity of the planning problem scales up and most of all, because the necessity to recollect data from different sites in a centralised planner causes several problems of data consistency and updating. This is the reason why, in managing production networks, decentralised production planning tools have been recommended by researchers and industrial managers. Indeed, decentralised production planning has several positive outcomes when it comes to managing production networks: a) data management is easier and more trustworthy; b) production planning systems are more robust, scalable, reliable; c) as a result production planning activities are easier and more reliable. However, distributed production planning has some drawbacks: a) production planning outcomes are considered less efficient than centralised production planning outcomes; b) coordination among the different entities involved in production planning activities needs to be properly designed; c) properly commercial tools are not available yet.

vi Preface This book concerns the above-mentioned issues. It faces the production planning problem in complex and very structured manufacturing firms such as those involved in the semiconductor industry. The book presents research work answering major issues dealing with decentralised production planning; in particular, for the considered research context it shows: How to structure and organise decentralised production planning for a complex multi-national corporate; How to organise business processes among the decentralised entities involved in the production planning process; Which kind of methodological tools can be used to obtain a reliable, effective and efficient cooperative production plan; Which kind of technology can be used to develop a distributed cooperative production planning system; A benchmark analysis showing how the proposed approach and methodologies allow obtaining realistic production plans in the considered research context. Very briefly (a more detailed outline of this book is given at the end of Chapter 1), the book is organised as follows: Chapter 1 introduces the research problem and the research context with reference to the state of the art; Chapters 2 and 3 provide respectively an overview of Game Theory and Negotiation Theory which are the methodological tools used to build cooperative production plans in a distributed environment; Chapter 4 presents an overview of the Agent Theory that is the technological tool suggested to develop a distributed production planning tool; Chapter 5 presents our approach for organising and structuring a distributed production planning system in a complex environment such as the semiconductor industry. Chapters 6 and 7 present the methodological approach suggested to reach a cooperative production plan in distributed networks such as described in Chapter 5. Specifically, Chapter 6 presents the methodological approach for planning production at medium-term level, and Chapter 7 at plant level. Chapter 8 presents the integration of the methodological approach presented in Chapters 6 and 7 in order to show how the proposed algorithms integrate with each other in order to provide a consistent production planning tool; finally, Chapter 9 presents the conclusions of the research developed in the book. I wish to thank all the researchers who have been involved in this project. Special thanks go to Dr. Pierluigi Argoneto who, with his Ph.D. work, has allowed us to develop a consistent and unitary body of methodological approaches for planning production in production networks. Palermo, 25 July 2007 Giovanni Perrone

Contents 1 Introduction and Literature Overview...1 1.1 Introduction...1 1.2 Production Planning in High-tech, High-volume Industry...2 1.3 Strategic and Tactical Level...2 1.4 Operational Models: Optimization and Decision Support...5 1.4.1 Mathematical Approach...7 1.4.2 Queuing and Stochastic Approaches...8 1.4.3 Heuristics and Simulation-based Approaches...8 1.5 Motivation...9 1.6 Book Outline...10 1.7 References...10 2 Game Theory: an Overview...13 2.1 Introduction...13 2.2 Game Setup...14 2.3 Non-cooperative Static Games...15 2.4 Existence of Equilibrium...16 2.5 Multiple Equilibria...17 2.6 Dynamic Games...17 2.7 Simultaneous Moves: Repeated and Stochastic Games...18 2.8 Cooperative Games...18 2.9 N-Person Cooperative Games...19 2.10 Characteristic Function and Imputation...20 2.11 Shapley Value...21 2.12 The Bargaining Game Model...22 2.13 References...23 3 Negotiation: an Overview...25 3.1 Introduction...25 3.2 Negotiation and Rational Self-interested Agents...28 3.3 Negotiation Models...29

viii Contents 3.4 Underlying Principle for Electronic Negotiation...30 3.5 Electronic Negotiation Protocols...31 3.6 Characteristics that Differentiate Negotiations Protocols...31 3.7 Modelling Approaches and Solution Concepts...32 3.7.1 Decision Theory...33 3.7.2 Game Theory...34 3.7.3 Negotiation Analysis...34 3.8 Strategic Negotiation...35 3.9 Negotiation Strategies...35 3.10 References...36 4 Multiple-agent Systems: an Overview...41 4.1 Introduction...41 4.2 Applications...42 4.3 Challenging Issues...43 4.4 Individual Agent Reasoning...44 4.5 Observable Worlds...45 4.6 Stochastic Transitions and Utilities...45 4.7 Distributed Decision Making...47 4.8 Recognising and Resolving Conflicts...48 4.9 Communicating Agents...48 4.10 References...49 5 Distributed Production Planning in Reconfigurable Production Networks...51 5.1 Introduction...51 5.2 Production Planning in DPS...52 5.2.1 Context of the Semiconductor Industry...52 5.2.2 PP in the Considered Industrial Case...54 5.2.3 IDEF0 Architecture...55 5.2.4 Agent Architecture...55 5.3 Top PP Level...57 5.4 High PP Level...57 5.5 Medium PP Level...58 5.6 Low PP Level...58 5.7 Shop-floor PP Level...59 5.8 References...60 6 Distributed Models for Planning Capacity of Reconfigurable Production Networks at Medium Term...63 6.1 Introduction...63 6.2 Initial State...63 6.3 The Centralised Model...64 6.4 The Negotiation Model...65 6.5 The Game-theoretical Model...66 6.5.1 Case 1: Characteristic Function h ij > 0...67 6.5.2 Case 2: Characteristic Function h ij < 0...67

Contents ix 6.5.3 The Bargaining Solution...70 6.6 Simulation Case Study...71 6.6.1 The Simulation Environment...71 6.6.2 Simulation Case Study...74 6.7 Results...74 6.7.1 Two-way Analysis of Variance...75 6.7.2 Design of Experiment (DoE)...88 6.8 References...95 7 Distributed Models for Plant Capacity Allocation...97 7.1 Introduction...97 7.2 Initial State...97 7.3 The Centralised Model...98 7.4 The Negotiation Model...99 7.4.1 Generative Function...99 7.4.2 Reactive Function...99 7.5 The Game -theoretical Model...100 7.6 The Simulation...101 7.6.1 The Simulation Environment...101 7.6.2 The Simulation Case Study...104 7.7 Results...105 7.7.1 Efficiency Performance Analysis: Two-way ANOVA...105 7.7.2 Efficiency Performance Analysis: DoE...115 7.7.3 Distance Performance Analysis: Two-way ANOVA...121 7.7.4 Distance Performance Analysis: DoE...130 7.7.5 Number of Reconfigurations Performance Analysis: Two-way ANOVA...136 7.7.6 Number of Reconfigurations Performance Analysis: DoE...147 7.7.7 Absolute Residual Performance Analysis: Two-way ANOVA...153 7.7.8 Absolute Residual Performance Analysis: DoE...164 8 Distributed Production Planning Models: an Integrated Approach...171 8.1 Introduction...171 8.2 The Simulation Case Study...172 8.3 Results...173 8.3.1 Efficiency Performance Analysis: Two-way ANOVA...173 8.3.2 Efficiency Performance Analysis: DoE...182 8.3.3 Distance Performance Analysis: Two-way ANOVA...192 8.3.4 Distance Performance Analysis: DoE...200 8.3.5 Absolute Residual Performance Analysis: Two-ways ANOVA...209 8.3.6 Absolute Residual Performance Analysis: DoE...214 8.4 Conclusions...223 9 Conclusions...225 9.1 Summary...225 9.2 Major Scientific Contributions of This Book...226 9.3 Directions for Future Work...227

x Contents Appendix A: Simulation Results Related to Chapter 6...229 Appendix B: Simulation Input Parameters and Results Related to Chapter 7...233 Appendix C: Simulation Input Parameters and Results Related to Chapter 8...247 Index...255