Modeling Manufacturing Systems: From Aggregate Planning to Real-Time Control

220 Pages·1999·7.58 MB·English

by  P. Brandimarte, A. Villa (auth.), Professor Paolo Brandimarte, Professor Agostino Villa (eds.)

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
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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 knowledge
based procedures;
• the specification of requirements for new models, including discrete-event and
continuous representations, to be used in simulating and designing the man
agement strategies for manufacturing plants.
The technical areas of interest included:
• at the system level, models for plant layout design, process planning, produc
tion planning and scheduling;
• at the component level, models for the functional description of flexible manu
facturing 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 In
ternational Symposia under IFAC sponsorship.
This volume collects some contributions from members of the IFAC Technical Com
mittee 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 1
0.1 Introduction and overview of the contributions 1
0.2 For further reading 3
0.3 References .................... . 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 25
2.1 Introduction........... 25
2.2 Basic decomposition approach. 28
2.3 Multi-resource scheduling ... 31
2.4 Set-up times. . . . . . . . . . . 32
2.5 Convergent and divergent job routings 33
2.6 Further extensions and practical aspects 34
2.6.1 Transportation times ...... . 34
2.6.2 Unequal transfer and production batches 35
2.6.3 Open shops . . . . . . . . . . 36
2.7 JOBPLANNER............. ...... . 37
2.7.1 Components of JOBPLANNER ...... . 38
2.7.2 Practical experiences with JOB PLANNER . 38
2.8 Conclusions............ 39
2.9 References ............ . 40
Appendix A: Derivation of set-up jobs 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 .. 63
3.7.5 Proof of theorem 5 .. 64
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
Contents ix
4.3.2 Combined methods of reactive scheduling 92
4.4 Conclusions: future research issues in the field . 95
4.5 References...................... 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 Ap
proach
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 . . . . . . . . . . . 175
7.4.3 Redditivity and risk ..... . 179
7.4.4 Framework optimisation model 183
7.5 Numerical example 184
7.6 Conclusions 187
7.7 References..... 188
8 Adaptive Production Control In Modern Industries
K.N. McKay, J.A. Buzacott 193
8.1 Introduction........................ 193
8.2 Motivation - inherent uncertainty . . . . . . . . . . . . 194
8.3 Applying production control methods - a perspective . 195
8.3.1 Production control - 1900-1930 196
8.3.2 Production control - 1945-1965 .. 198
8.3.3 Production control - 1965-1980 .. 200
8.3.4 Production control - 1980-present . 201
8.4 Production control concepts for immaturity or uncertainty . 203
8.4.1 Organizational design 203
8.4.2 Plan generation . 207
8.4.3 Plan execution 209
8.5 Conclusion .... 210
8.6 Acknowledgments. 211
8.7 References ..... 211
o
Modeling Manufacturing Systems:
an Introduction
P. Brandimartel
A. Villal
0.1 Introduction and overview of the contributions
Even a quick glimpse at the literature on modeling in manufacturing systems shows
the diversity of approaches that have been adopted. Models may be classified along
many dimensions [4].
Purpose. Some models are used to evaluate the performance of a complex system
for a given set of control parameters or for a given system configuration. This
is the case of evaluative models such as simulation of queueing network models.
The suitable system configuration must be obtained by repeated runs of the
model, which on the other hand can be quite accurate. In generative models
some simplification is accepted in order to obtain an "optimal" answer by
solving an optimization model directly (either by an exact or by a heuristic
procedure).
Hierarchical level. Some models are used for system design, both in physical or
in logical terms. At the opposite end of the line, we may find more operational
models which should be used in real time. At an intermediate level, we may
find "tactical" models whose time horizon are weeks or a few months.
Time representation. Models of physical systems are usually based on a contin
uous time representation, leading to differential equations. This approach is
taken in manufacturing systems when the material flow is represented as a
continuous flow; this paves the way to optimal control models. More com
monly, time is discretized (e.g. in months or weeks) to come up with suitable
plans; in this case the representation is based on difference equations, to which
constraints can be added in order to write linear programming models (pos
sibly mixed-integer). At the physical level, most manufacturing systems can
be considered as a discrete event systems. Discrete event models are easily
analyzed by evaluative models, but a graph based representation (in which the
nodes represents activities and arcs their precedence relationships) can be used
to build generative models.
1 Dipartimento di Sistemi di Produzione ed Economia dell' Azienda, Politecnico di Torino,
Corso Duca degli Abruzzi 24, 10129 Torino, Italy. e-mail: [email protected]
P. Brandimarte et al. (eds.), Modeling Manufacturing Systems
© Springer-Verlag Berlin Heidelberg 1999