Table Of ContentCOMPUTER-AIDED DESIGN,
ENGINEERING, AND MANUFACTURING
Systems Techniques And Applications
VOLUME
V
DESIGN
THE oF
MANUFACTURING
SYSTEMS
COMPUTER-AIDED DESIGN,
ENGINEERING, AND MANUFACTURING
Systems Techniques And Applications
V O LU M E
V
DESIGN
THE OF
MANUFACTURING
SYSTEMS
Editor
CORNELIUS LEONDES
CRC Press
Boca Raton London New York Washington, D.C.
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Preface
A strong trend today is toward the fullest feasible integration of all elements of manufacturing, including
maintenance, reliability, supportability, the competitive environment, and other areas. This trend toward
total integration is called concurrent engineering. Because of the central role information processing
technology plays in this, the computer has also been identified and treated as a central and most essential
issue. These are the issues that are at the core of the contents of this volume.
This set of volumes consists of seven distinctly titled and well-integrated volumes on the broadly
significant subject of computer-aided design, engineering, and manufacturing: systems techniques and
applications. It is appropriate to mention that each of the seven volumes can be utilized individually. In
any event, the great breadth of the field certainly suggests the requirement for seven distinctly titled and
well-integrated volumes for an adequately comprehensive treatment. The seven volume titles are:
1. Systems Techniques and Computational Methods
2. Computer-Integrated Manufacturing
3. Operational Methods in Computer-Aided Design
4. Optimization Methods for Manufacturing
5. The Design of Manufacturing Systems
6. Manufacturing Systems Processes
7. Artificial Intelligence and Robotics in Manufacturing
The contributors to this volume clearly reveal the effectiveness and great significance of the techniques
available and, with further development, the essential role that they will play in the future. I hope that
practitioners, research workers, students, computer scientists, and others on the international scene will
find this set of volumes to be a unique and significant reference source for years to come.
Cornelius T. Leondes
Editor
© 2001 by CRC Press LLC
Editor
Cornelius T. Leondes, B.S., M.S., Ph.D., is an Emeritus Professor at the School of Engineering and Applied
Science, University of California, Los Angeles. Dr. Leondes has served as a member or consultant on
numerous national technical and scientific advisory boards. He has served as a consultant for numerous
Fortune 500 companies and international corporations, published over 200 technical journal articles,
and edited and/or co-authored over 120 books. Dr. Leondes is a Guggenheim Fellow, Fulbright Research
Scholar, and Fellow of IEEE. He is a recipient of the IEEE Baker Prize, as well as its Barry Carlton Award.
© 2001 by CRC Press LLC
Contributors
Shabbir Ahmed Necdet Geren Nikolaos V. Sahinidis
Georgia Institute of Technology University of Çukurova University of Illinois at Urbana-
Atlanta, Georgia Adana, Turkey Champaign
Urbana, Illinois
Venkat Allada Klaus Henning
University of Missouri-Rolla University of Technology (RWTH) Inga Tschiersch
Rolla, Missouri Aachen, Germany University of Technology (RWTH)
Aachen, Germany
Saifallah Benjaafar Bao Sheng Hu
University of Minnesota Xi’an Jiaotong University Ke Yi Xing
Minneapolis, Minnesota Xi’an, China Xidian University
Xi’an, China
Dietrich Brandt Mark A. Lawley
University of Technology (RWTH) Purdue University Roman Zˇavbi
Aachen, Germany West Lafayette, Indiana University of Ljubljana
Ljubljana, Slovenia
Jozˇe Duhovnik T. Warren Liao
University of Ljubljana Louisiana State University
Ljubljana, Slovenia Baton Rouge, Louisiana
Placid M. Ferreira Spyros A. Reveliotis
University of Illinois at Urbana- Georgia Institute of Technology
Champaign Atlanta, Georgia
Urbana, Illinois
© 2001 by CRC Press LLC
Contents
Preface
Chapter 1 Long-Range Planning of Chemical
Manufacturing Systems
Shabbir Ahmed and Nikolaos V. Sahinidis
Chapter 2 Feature-Based Design in Integrated Manufacturing
Venkat Allada
Chapter 3 Flexible Factory Layouts: Issues in Design, Modeling,
and Analysis
Saifallah Benjaafar
Chapter 4 Structural Control of Large-Scale Flexibly Automated
Manufacturing Systems
Spyros A. Reveliotis, Mark. A. Lawley, and Placid M. Ferreira
Chapter 5 The Design of Human-Centered Manufacturing Systems
Dietrich Brandt, Inga Tschiersch, and Klaus Henning
Chapter 6 Model-Based Flexible PCBA Rework Cell Design
Necdet Geren
Chapter 7 Model of Conceptual Design Phase and Its Applications
in the Design of Mechanical Drive Units
Roman Zˇavbi and Jozˇe Duhovnik
Chapter 8 Computer Assembly Planners in Manufacturing Systems
and Their Applications in Aircraft Frame Assemblies
T. Warren Liao
Chapter 9 Petri Net Modeling in Flexible Manufacturing Systems
with Shared Resources
Ke Yi Xing and Bao Sheng Hu
© 2001 by CRC Press LLC
1
Long-Range Planning
of Chemical
Manufacturing
Systems
1.1 Introduction
1.2 The Long-Range Planning Problem
General Formulation
1.3 Deterministic Models
Shabbir Ahmed An MILP Model • Extensions of the MILP Model
Georgia Institute of Technology 1.4 Hedging against Uncertainty
Sources and Consequences of Uncertainty • Fuzzy
Nikolaos V. Sahinidis1
Programming • Stochastic Programming • Fuzzy (FP) vs.
University of Illinois Stochastic Programming (SP)
at Urbana-Champaign 1.5 Conclusions
1.1 Introduction
Recent years have witnessed increasingly growing awareness for long-range planning in all sectors.
Companies are concerned more than ever about long-term stability and profitability. The chemical
process industries is no exception. New environmental regulations, rising competition, new technology,
uncertainty of demand, and fluctuation of prices have all led to an increasing need for decision policies
that will be ‘‘best” in a dynamic sense over a wide time horizon. Quantitative techniques have long
established their importance in such decision-making problems. It is, therefore, no surprise that there is
a considerable number of papers in the literature devoted to the problem of long-range planning in the
processing industries. It is the purpose of this chapter to present a summary of recent advances in this
area and to suggest new avenues for future research.
The chapter is organized in the following manner. Section 1.2 presents the long-range planning problem.
Section 1.3 discusses deterministic models and solution strategies. Models dealing with uncertainty are
discussed in Section 1.4. Finally, some recommendations for future research and concluding remarks are
presented in Section 1.5.
1Address all correspondence to this author (e-mail: [email protected]).
1.2 The Long-Range Planning Problem
Let us consider a plant comprising several processes to produce a set of chemicals for sale. Each process
intakes a number of raw materials and produces a main product along with some by-products. Any of
these main or by-products could be the raw materials for another process. We, thus, have a list of chemicals
consisting of the main products or by-products that we wish to sell as well as ingredients necessary for
the production of each chemical. We might then contemplate the in-house production of some of the
required ingredients, forcing us to consider another tier of ingredients and by-products. The listing
continues until we have considered all processes which may relate to the ultimate production of the
products initially proposed for sale. At this point, the final list of chemicals will contain all raw materials
we consider purchasing from the market, all products we consider offering for sale on the market, and
all possible intermediates. The plant can then be represented as a network comprised of nodes repre-
senting processes and the chemicals in the list, interconnected by arcs representing the different alterna-
tives that are possible for processing, and purchases to and sales from different markets.
The process planning problem then consists of choosing among the various alternatives in such way
as to maximize profit. Once we know the prices of chemicals in the various markets and the operating
costs of processes, the problem is then to decide the operating level of each process and amount of each
chemical in the list to be purchased and sold to the various markets. The problem in itself grows
combinatorially with the number of chemicals and processes and is further complicated once we start
planning over multiple time periods.
Let us now consider the operation of the plant over a number of time periods. It is reasonable to
expect that prices and demands of chemicals in various markets would fluctuate over the planning
horizon. These fluctuations along with other factors, such as new environmental regulations or technol-
ogy obsolescence, might necessitate the decrease or complete elimination of the production of some
chemicals while requiring an increase or introduction of others. Thus, we have some additional new
decisions variables: capacity expansion of existing processes, installation of new processes, and shut down
of existing processes. Moreover, owing to the broadening of the planning horizon, the effect of discount
factors and interest rates will become prominent in the cost and price functions. Thus, the planning
objective should be to maximize the net present value instead of short-term profit or revenue. This is
the problem to which we shall devote our attention. The problem can be stated as follows: assuming a
given network of processes and chemicals, and characterization of future demands and prices of the
chemicals and operating and installation costs of the existing as well as potential new processes, we want
to find an operational and capacity planning policy that would maximize the net present value. We shall
now present a general formulation of this problem for a planning horizon consisting of a finite number
of time periods.
General Formulation
The following notation will be used throughout.
Indices
i The set of NP processes that constitutes the network (i (cid:2) 1, NP).
j The set of NC chemicals that interconnect the processes (j (cid:2) 1, NC).
l The set of NM markets that are involved (l (cid:2) 1, NM).
t The set of NT time periods of the planning horizon (t (cid:2) 1, NT).
Variables
E Units of expansion of process i at the beginning of period t.
it
P Units of chemical j purchased from market l at the beginning of period t.
jlt
Q Total capacity of process i in period t. The capacity of a process is expressed in terms of its main
it
product.
S Units of chemical j sold to market l at the end of period t.
jlt
W Operating level of process i in period t expressed in terms of output of its main product.
it
Functions
INVT (E ) The investment model for process i in period t as a function of the capacity installed or
it it
expanded.
OPER (W ) The cost model for the operation of process i over period t as a function of the operating
it it
level.
SALE (S ) The sales price model for chemical j in market l in period t as a function of the sales
jlt jlt
quantity.
PURC (P ) The purchase price model for chemical j in market l in period t as a function of the
jlt jlt
purchase quantity.
(cid:3)O(W ) The mass balance model for the output chemical j from process i as a function of the
ij it
operating level.
(cid:3)I(W ) The mass balance model for the input chemical j for process i as a function of the
ij it
operating level.
Parameters
aL,aU Lower and upper bounds for the availability (purchase amount) of chemical j from
jlt jlt
market l in period t.
dL,dU Lower and upper bounds for the demand (sale amount) of chemical j in market l in
jlt jlt
period t.
With this notation, a general model for long-range process planning can be formulated as follows.
Model GP
NTNP
(cid:2) (cid:2)
max NPV (cid:2) [(cid:4)INVT (E )(cid:4)OPER (W )]
it it it it
t(cid:2)1 i(cid:2)1
(cid:2)NC(cid:2)NM
(cid:5) [SALE (S )(cid:4)PURC (P )]
jlt jlt jlt jlt (1.1)
j(cid:2)1l(cid:2)1
subject to
Q (cid:2) Q (cid:5)E i (cid:2) 1NP t (cid:2) 1,NT (1.2)
it it(cid:4)1 it
W (cid:6)Q i (cid:2) 1NP t (cid:2) 1,NT (1.3)
it it
NM NP NM NP
(cid:2)Pjlt(cid:5)(cid:2)(cid:3)iOj(Wit) (cid:2) (cid:2)Sjlt(cid:5)(cid:2)(cid:3)iIj(Wit) j (cid:2) 1NC t (cid:2) 1,NT (1.4)
l(cid:2)1 i(cid:2)1 l(cid:2)1 i(cid:2)1
aL (cid:6)P (cid:6)aU j (cid:2) 1, NC; l (cid:2) 1NM t (cid:2) 1,NT (1.5)
jlt jlt jlt
aL (cid:6)S (cid:6)aU j (cid:2) 1, NC; l (cid:2) 1NM t (cid:2) 1,NT (1.6)
jlt jlt jlt
E ,Q ,W (cid:7)0 i (cid:2) 1NP t (cid:2) 1,NT (1.7)
it it it
