Modeling Manufacturing Systems From Aggregate Planning to Real-Time Control
Springer-Verlag Berlin Heidelberg GmbH
Paolo Brandimarte. Agostino Villa (Eds.) Modeling Manufacturing Systems From Aggregate Planning to Real-Time Control With 54 Figures and 16 Tables Springer
Professor Paolo Brandimarte Professor Agostino Villa Technical University of Torino Dipartimento di Sistemi di Produzione ed Economia dell' Azienda Corso Duca degli Abruzzi 24 1-10129 Torino, Italy ISBN 978-3-642-08483-6 Cataloging-in-Publication Data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Modeling manufacturing systems : from aggregate planning to real time control 1 ed.: Paolo Brandimarte; Agostino Villa. ISBN 978-3-642-08483-6 ISBN 978-3-662-03853-6 (ebook) DOI 10.1007/978-3-662-03853-6 This work is subject to copyright. All rights are reserved. whether the whole or part of the material is concerned. specifically the rights of translation. reprinting. reuse of illustrations. recitation. broadcasting. reproduction on microfilm or in any other way. and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9. 1965. in its current version. and permission for use must always be obtained from Springer-Verlag Berlin Heidelberg GmbH. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg 1999 Originally published by Springer -Verlag Berlin Heidelberg New York in 1999 Softcover reprint of the hardcover 1 st edition 1999 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 protective laws and regulations and therefore free for general use. Hardcover design: Erich Kirchner. Heidelberg SPIN 10547355 42/2202-5 4 3 2 1 o - Printed on acid-free paper
Preface Four years ago the International Federation of Automatic Control (IFAC) set up a Technical Committee on Manufacturing Modelling, Management and Control. Among the goals of this committee were: the development, the comparison, and the classification of formal models, both descriptive and prescriptive, of Computer Integrated Manufacturing Systems; the integration among optimization methods, simulation models and knowledgebased procedures; the specification of requirements for new models, including discrete-event and continuous representations, to be used in simulating and designing the management strategies for manufacturing plants. The technical areas of interest included: at the system level, models for plant layout design, process planning, production planning and scheduling; at the component level, models for the functional description of flexible manufacturing and assembly systems, and for the design of strategies for production activity control, process supervision, and maintenance. The Technical Committee is going on with the organization of Workshops and International Symposia under IFAC sponsorship. This volume collects some contributions from members of the IFAC Technical Committee on Manufacturing Modelling, Management and Control. We wish to thanks the members for their valuable contributions and the anonymous reviewers for their cooperation in making this book possible. The editors.
Contents o Modeling Manufacturing Systems: an Introduction P. Brandimarte, A. Villa 0.1 Introduction and overview of the contributions 0.2 For further reading 0.3 References.... 1 1 3 3 1 From the Aggregate Plan to Lot-Sizing in Multi-level Production Planning J.-C. Hennet 5 1.1 Introduction... 5 1.2 The planning process................ 6 1.2.1 The Aggregate Planning Problem (APP) 6 1.2.2 The detailed planning level.... 10 1.2.3 The multi-stage planning problem 10 1.3 The Lot-Sizing Problem.......... 13 1.3.1 Problem description... 13 1.3.2 A decomposed technique for cost evaluation. 17 1.4 Example.. 19 1.5 Conclusion 21 1.6 References. 21 2 Shop Floor Scheduling in Discrete Parts Manufacturing G.J. Meester, J.M.J. Schutten, S.L. van de Velde, W.H.M. Zijm 2.1 Introduction... 2.2 Basic decomposition approach. 2.3 Multi-resource scheduling... 2.4 Set-up times........... 2.5 Convergent and divergent job routings 2.6 Further extensions and practical aspects 2.6.1 Transportation times.... 2.6.2 Unequal transfer and production batches 2.6.3 Open shops.......... 2.7 JOBPLANNER....... 2.7.1 Components of JOBPLANNER.... 2.7.2 Practical experiences with JOB PLANNER. 2.8 Conclusions... 2.9 References.... Appendix A: Derivation of set-up jobs 25 25 28 31 32 33 34 34 35 36 37 38 38 39 40 43
viii Contents 3 Integrating Layout Design and Material Flow Management in Assembly Systems M.Lucertini, D.Pacciarelli, A.Pacifici 45 3.1 Introduction... 45 3.2 Statement of the problem 47 3.2.1 Problem data... 47 3.2.2 Decision variables 48 3.2.3 A numerical example. 48 3.2.4 Problem statement 51 3.3 Feasibility properties... 52 3.3.1 Feasibility graph. 52 3.3.2 Feasibility given 'IT" 54 3.3.3 Feasibility given,\ 54 3.4 Optimization properties. 56 3.4.1 Completion time minimization 57 3.4.2 Cycle time minimization.. 59 3.4.3 Part transfer minimization 59 3.5 Conclusions.......... 60 3.6 References... 60 3.7 Appendix: proofs of theorems 62 3.7.1 Proof of theorem 1.. 62 3.7.2 Proof of theorem 2.. 62 3.7.3 Proof of theorem 3.. 62 3.7.4 Proof of theorem 4.. 3.7.5 Proof of theorem 5.. 4 Reactive Scheduling in Real Time Production Control E. Szelke, L. Monostori 65 4.1 Reactive operation management - Predictive, reactive and proactive scheduling.............................. 65 4.1.1 Objectives of reactive operation management - reactivej proactive scheduling.................... 66 4.1.2 Monitoring - A basis of RSjPS in real-time production control 70 4.1.3 RS problem complexity - IT requirements against solution approaches.................... 75 4.2 Models of reactive and proactive scheduling problems. 77 4.2.1 Graphical modelling techniques..... 79 4.2.2 Simulation... 80 4.2.3 Concurrent Modelling Language (CML) 81 4.2.4 Graph theoretic modelling used for RS as a Constraint Satisfaction (CS) problem.... 82 4.2.5 Neural network models........... 84 4.2.6 Genetic algorithm based models... 85 4.2.7 Stochastic models of proactive scheduling 85 4.2.8 Distributed agent architectures...... 86 4.3 Solution approaches - methods, techniques, tools 90 4.3.1 AI-based methods and heuristic search techniques of RS 90 63 64
Contents ix 4.3.2 Combined methods of reactive scheduling 4.4 Conclusions: future research issues in the field. 4.5 References... 92 95 97 5 Simulation within CAD-Environment P. Kopacek, G. Kronreif, T. Perme 115 5.1 Introduction... 115 5.1.1 Simulation and CAD.. 117 5.2 Simulation system LASIMCO. 119 5.2.1 Formulation of requirements. 119 5.2.2 Conceptual solution and applied theory 120 5.2.3 Developed tools............ 123 5.2.4 Examples... 126 5.3 Simulation in robotics - ROMOBIL/SITAR 129 5.3.1 Introduction.... 129 5.3.2 Simulation system SITAR...... 131 5.3.3 Modelling of robot cells in ROMOBIL 132 5.3.4 Application example 134 5.4 Conclusion 135 5.5 References.... 136 6 Model of Material Handling and Robotics C.- Y. Huang, S. Y. No! 139 6.1 Introduction... 139 6.2 Traditional models of material handling and robotics. 140 6.2.1 Traditional approaches.............. 140 6.2.2 Concerns with traditional modeling approaches 141 6.2.3 A Comparison of models of material handling and robotics with the tool perspective... 141 6.3 Facility Description Language (FDL).................. 141 6.4 Concurrent Flexible Specifications (CFS) for material handling and robotics............................. 144 6.4.1 CFS using data/control flow diagram and Petri nets 145 6.4.2 Overview of specification software tools 146 6.4.3 Case study application.. 146 6.4.4 Flexibility of specification 157 6.5 Discussion and conclusion 157 6.6 References... 158 7 A Simultaneous Approach for IMS Design: a Possibility Based Approach G. Perrone, S. Noto La Diega 161 7.1 Introduction... 161 7.2 The decisional environment for Strategic IMS Design....... 163 7.3 The Strategic IMS Design Decision-Making Tool: the possibilistic programming theory.................. 166 7.4 The possibilistic framework for strategic FMS design 171 7.4.1 Market... 171
x Contents 7.4.2 Production........... 7.4.3 Redditivity and risk.... 7.4.4 Framework optimisation model 7.5 Numerical example 7.6 Conclusions 7.7 References... 8 Adaptive Production Control In Modern Industries K.N. McKay, J.A. Buzacott 8.1 Introduction... 8.2 Motivation - inherent uncertainty............ 8.3 Applying production control methods - a perspective. 8.3.1 Production control - 1900-1930 8.3.2 Production control - 1945-1965.. 8.3.3 Production control - 1965-1980.. 8.3.4 Production control - 1980-present. 8.4 Production control concepts for immaturity or uncertainty. 8.4.1 Organizational design 8.4.2 Plan generation. 8.4.3 Plan execution 8.5 Conclusion... 8.6 Acknowledgments. 8.7 References... 175 179 183 184 187 188 193 193 194 195 196 198 200 201 203 203 207 209 210 211 211