Table Of ContentFEASIBILITY
OF
DISTRIBUTED MANUFACTURING OPERATIONS CONTROL
By
SERDAR KIRLI
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
F THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1999
ACKNOWLEDGMENTS
I would like to thank Dr. Sencer Yeralan for his support,
encouragement and friendship. This study would not be possible
without his vision and guidance. He has been not only an advisor
but also a dear friend.
I would like to extend my appreciations to my committee
members Dr. Suleyman Tufekci, Dr. John F. Mahoney and Dr. John
K. Schueller for their suggestions and comments throughout the
study.
ii
TABLE OF CONTENTS
£326
ABSTRACT vi
CHAPTERS
1 INTRODUCTION „._ _ _ 1
1.1 Background 1
1.2 Trends in Technology 3
1.3 New Paradigm: Distributed Control 4
1.3.1 Manufacturing Environment: A Complex System 5
1.3.2 Distributed Manufacturing Operations Control 7
1.3.3 Technological Aspects 10
1.3.4 Related Work .._ 13
1.4 Motivation _ „._ 18
1.5 Approach ..„ 20
1.6 Organization 21
2 ELEMENTS OF DISTRIBUTED CONTROL 23
2.1 Embedded Data Processing 23
2.2 Network Communications 28
3 MODELING OF A DISTRIBUTED MANUFACTURING SYSTEM 33
3.1 Overview 33
3.2 Manufacturing Environment 35
3.2.1 Setup 35
3.2.2 Production Goal and Mode of Operation 36
3.2.3 Features of the Manufacturing System 37
3.3 Adaptive Control Policies 42
3.3.1 Control Policy Buffer_Size 43
3.3.2 Control Policy Delay_Time 51
4 SIMULATIONS ...„ _.. 57
4.1 Overview _ _ „ 57
4.2 Static Control Policies _ 58
4.3 Simulation Scenarios 59
4.3.1 Case 1: No Bottleneck (Balanced Line) „ 60
4.3.2 Case 2: Single Mild Bottleneck 61
4.3.3 Case 3: Single Severe Bottleneck 62
4.3.4 Case 4: Variable Bottleneck 62
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4.3.5 Case 5: Two Bottlenecks - — 64
4.3.6 Issues Involving Simulation Parameters 65
4.4 Simulations - - 68
4.4.1 Case 1: No Bottleneck (Balanced Line) 69
4.4.2 Case 2: Single Mild Bottleneck _ 75
4.4.3 Case 3: Single Severe Bottleneck 79
4.4.4 Case 4: Variable Bottleneck 83
4.4.5 Case 5: Two Bottlenecks - 87
4.5 E4.v4a.l6uaMtiixoendofCasSeimulation Results — 10902
4.6 Observations on Adaptive Behavior 102
4.6.1 An Example 103
4.6.2 Step Size „ 106
4.6.3 Oscillatory Behavior _ 113
5 DESIGN AND IMPLEMENTATION - 115
5.1 Technology Issues 115
5.1.1 Communication Protocol 116
5.1.2 Embedded Controllers - 119
5.2 Design Issues 120
5.2.1 Physical Components of Implementation 121
5.2.2 Scanner Module — 123
5.2.3 Time Base _ 125
5.2.4 CAN Messages 128
5.2.4.1 Initialization messages 130
5.2.4.2 Production messages 133
5.2.4.3 Data transfer messages 137
5.3 Implementation Issues _ ~ 138
5.3.1 CAN Data Transfer Rate 139
5.3.2 Time Base 139
5.3.3 CAN Message IDs 140
5.3.4 Communication Modes 143
5.3.5 Coding 147
5.4 Emulation Results „ 148
5.5 Observations and General Remarks _..151
5.6 Detailed Remarks on the Implementation of DMOC 152
5.6.1 Issues in Simulating Distributed Systems 152
5.6.2 Observations from the Physical Implementation 154
5.6.2.1 Communication rate 155
5.6.2.2 Time base 155
5.6.2.3 Synchronization 158
5.6.2.4 Embedded code development 162
6 SUMMARY AND CONCLUSIONS 165
APPENDICES
A CONTROLLER AREA NETWORK (CAN) „. 168
B A TWO-STATION MARKOV MODEL WITH OUTPUT DELAY ..191
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C ASSEMBLY AND C SOURCE CODE USED IN THE IMPLEMENTATION 204
REFERENCES ..„ _._.„ „.. „...„ 273
BIOGRAPHICAL SKETCH „ 281
V
.
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
FEASIBILITY
OF
DISTRIBUTED MANUFACTURING OPERATIONS CONTROL
By
Serdar Kirli
August 1999
Chairman: Dr. Sencer Yeralan
Major Department: Industrial and Systems Engineering
The last two decades have witnessed the emergence of new
technologies in coitputer control and inter-computer
communications. More recently, embedded controllers have
redefined industrial automation through a new generation of
"smart" machine tools (i.e., tools with local data processing
capabilities
)
New engineering technologies most often lead to new
engineering approaches and, in turn, to new engineering
practices. The focus of this dissertation is on the implications
of these emerging technologies on manufacturing operations
control.
Generally speaking, current industrial engineering practice
continue to rely heavily on traditional methods for production
control. These methods may be characterized by two common points
vi
a unicentric view of the physical plant, and the treatment of the
system components as passive elements, that is, uninvolved in the
decision making process.
This study is a trial for a new paradigm, oriented more
towards the discovery and evaluation of previously untested
ideas. Specifically, it investigates the feasibility of
distributed production line operations control where the
information processing is locally carried out by networked
(communicating) embedded station controllers. The decision making
process relies on rule-based local learning from real-time
process data. In accordance with the orientation of the study,
the decision making process is kept intentionally simple to
retain the focus on paradigm development rather than on seeking
streamlined policies. Particularly, a minimal set of rules is
selected to characterize the behavior of stations and a
straightforward learning mechanism is chosen. The work includes a
physical implementation and a computer simulation, whose findings
are compared. The controller area network (CAN) is selected as
the cost-effective communication protocol. A production line
comprised of four stations is used as a testbed for the purposes
of this study.
Evidence is revealed implying that even with simple rule-
based learning, a distributed embedded approach achieves
production line control performances comparable to the
traditional techniques. Moreover, it is demonstrated that such a
control system may be implemented using low-cost embedded
vii
control. The study discovers empirical aspects of distributed
production control that present profound justification for future
research. Perhaps most importantly, the distributed embedded
approach shows promise in cases where system scalability
requirements and environmental dynamics are more demanding.
viii
.
CHAPTER 1
INTRODUCTION
1.1 Backgroiond
The control of manufacturing systems has been a fundamental
field of work in industrial engineering [15,41]. In the last few
decades many approaches have been developed to determine the
optimum operating parameters of manufacturing systems [23,75].
Reviewing the pertinent industrial engineering literature, one
observes a gradual transition from more qualitative approaches
to manufacturing operations control (MOC) toward more
quantitative and analytical approaches [67]
Recent quantitative approaches to MOC have lead to
optimization models that are most often solved on a computer in
an algorithmic fashion. Paralleling the improvements in data
processing technologies, the scale and scope of such models have
been increasing. However, there may be limits to such
improvements. Although it is possible with today's computers to
develop models for large-scale manufacturing systems with
hundreds of elements, as a whole, industry tends to use the more
simplistic techniques, such as push, pull (kanban), constant
work-in-process (CONWIP), and so on. This stems partially from
the difficulties in modeling and in verifying the analytical
solutions, and the simplifying assumptions inherent in most
1
2
analytical models, such as linearity, time-homogeneity, and
deterministic operating disciplines. However, the use of such
simplistic approaches does not always allow the manufacturing
system to fulfill its potential in efficiency and effectiveness.
Presently, production control mainly relies on non-adaptive
control strategies such as material requirements planning (MRP)
[34], kanban [86], and CONWIP [71]. These have all been
developed and have become popular over the last few decades. MRP
is a push system where raw materials are pushed into the
production line periodically based on demand. Once in the system
parts flow toward the output without any control of the
downstream stations possibly causing inventory build-ups in
front of bottleneck stations. Kanban and CONWIP on the other
hand are pull systems, which are geared toward reducing the
work-in-process (WIP) inventory. Contrary to the push systems,
in pull systems the flow of parts is controlled by the
downstream stations. CONWIP is based on the principle that the
total inventory in the system remains constant at all times.
Hence, a new part can enter the system only after a finished
part is removed from the finished goods inventory. To accomplish
that a fixed number of cards are assigned to the production line
that accompany parts through the system. Similar to CONWIP the
material flow in kanban is also regulated with cards. However,
in kanban, cards are allocated to each station rather than the
entire line. Cards are attached to parts and removed when they
move from one station to another. Therefore, the number of cards
at a station determines the maximum WIP level for that station.
