Configuring cell systems to handle variable demand

214 Pages·1996·6 MB·English

by  Schaller, Jeffrey

CONFIGURING CELL SYSTEMS TO HANDLE VARIABLE DEMAND
By
Jeffrey Schaller
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1996
.
ACKNOWLEDGMENTS
I would like to thank Dr. Selcuk Erenguc, my
supervisory committee chairman, for his invaluable guidance
and patient encouragement throughout my years in the
program. My supervisory committee cochairman Dr. Asoo J.
Vakharia has also provided invaluable guidance during the
past year and I am thankful that he brought his expertise in
cellular manufacturing to our department. I also would like
to thank Dr. Harold Benson, Dr. Patrick Thomson, and Dr.
Chung-Yee Lee for serving on my supervisory committee and
for the fine instruction they provided in the classes I
attended. I would also like to thank Dr. Antal Majthay for
the direction he provided while supervising my assistantship
assignments
Finally, and most importantly, I want to thank my
mother and father for the love and support they have
provided my entire life. Anything good that I have ever
accomplished is a result of having the best parents.
11
TABLE OF CONTENTS
ACKNOWLEDGMENTS ii
LIST OF TABLES vii
LIST OF FIGURES ix
Abstract x
Chapters
1 INTRODUCTION 1
A Brief Description of Cellular Layouts 1
Product Line Layout
1
Process Layout
3
Cellular Layouts
4
Cell Formation
7
Early Cell Formation Techniques
8
Later Cell Formation Techniques
9
The Impact of Dynamic Demand on Cell Formation
Decisions 11
Dynamic Demand's Impact on Cellular Layouts 11
.
Strategies to Handle Dynamic Demand while
using a Cellular layout 16
Current Practices 18
Automotive Parts Manufacturer 18
Enclosure Manufacturer
2
The Problem and its Terminology 23
The Organization of this Proposal 24
2 REVIEW OF CELL FORMATION LITERATURE 26
Introduction
26
Procedures Based solely on a Part-Machine Incidence
Matrix
30
Objectives and Constraints 31
Frequently used objectives 31
iii
Frequently used constraints 34
Other objectives 35
Methods 37
Manual Identification 38
Matrix Manipulation 39
Clustering using Similarity Coefficients 40
Graph Theory 42
Mathematical Models 43
Other Methods 44
Evaluation of Procedures 44
Procedures Which use a Part-Machine Incidence Matrix
and Incorporate Data to Provide Weights for Parts
or Machines 46
Objectives and Constraints 47
Methods 48
Routing Based Procedures Without Machine Capacities 49
Operations Sequences 50
Alternative Routings 51
Procedures Which Incorporate Setup Information. 52
.
Procedures Which Incorporate Machine Capacities 55
.
Model l 55
Model 2 57
Methods 59
Procedures That Consider Multiple Scenarios for Part
Volumes
60
Other Procedures 63
Summary
64
MODELS
66
Introduction 66
Model 1 67
Description of Model I 67
Model I Formulation 69
Description of Model I Equations 71
An Example of Model I 74
Model II 77
Description of Model II 77
Model II Formulation .... 79
Description of Model II Equations 81
An Example of Model II 83
IV
4 SOLUTION PROCEDURES 87
Introduction
87
Model I Heuristic 88
Lower Bounds for Model II 91
Linear Programming Relaxation 91
Lagrangean Relaxation
92
Linear Programming Relaxation Tightened
by-
Adding Valid Inequalities 94
Valid Inequality Set 1 94
Valid Inequality Set 2 96
Valid Inequality Set 3 97
Heuristics for Model II 98
Heuristic 1 98
Description of Heuristic 1 98
Formal Procedure for Heuristic 1 100
.
Discussion of Heuristic l 101
Heuristic
2 101
Heuristic 3A 105
Description of Heuristic 3A 105
Formal Procedure .107
Discussion of Heuristic 3A 110
Heuristic 3B Ill
5 DESCRIPTION OF EXPERIMENTS AND DATA 114
Introduction 114
Experiments 115
Experiment 1 Data 116
Part-Machine Incidence Matrices 117
Processing Times 119
Part Demand 120
Inventory and Backorder Costs 120
Machine Capacities and Number of Machines of Each
Type 121
List of Potential Cells 122
Cell Production and Intercell Production Costs 123
Summary 124
Experiment 2 Data 125
6 RESULTS 127
Results of Experiment 1 127
v
Lower Bounds for Model II 127
Heuristic Results for Model II 130
Discussion of Results 131
Lower Bounds 131
Heuristics 136
Results of Experiment 2 138
7 CONCLUSIONS AND FUTURE RESEARCH 142
Conclusions 142
Future Research 144
APPENDICES
A FORMULATION OF MODEL I EXAMPLE 147
B FORMULATION OF MODEL II EXAMPLE 149
C PROBLEM 1 DATA AND SOLUTION 151
Problem 1 Data 151
Problem 1 Solution 171
LIST OF REFERENCES 178
BIOGRAPHICAL SKETCH
200
VI
.
LIST OF TABLES
TABLE page
1. Part Routing 5
2. Cellular Layout 5
3. An Example of a Part-Machine Incidence Matrix. 8
. .
4. Capacity Consideration 10
5 Part Family Machine Requirements 12
.
6. Load on Machine Type by Period 13
7. Average Load 13
8. Part-Machine Incidence Matrix for Model I Example. 75
9. By Part Demand 75
10. Machine Data 75
11. Potential Cells 84
12. Production Costs 85
13. Part-Machine Data Sets 118
14 Summary of Experimental Problems 124
15. L.P. and Lagrangean Relaxations 128
16. Valid Inequality Relaxations 129
17. Heuristics 1 and 2 132
18. Heuristics 3A and 3B 133
vii
19. Problem 1 Results 140
20. Problem 2 Results 140
21. Problem 1 Selected Cells 172
22. Part/Cell Processing 173
vui
..
LIST OF FIGURES
FIGURE
pacre
1 Product Line Layout 2
2 Process Layout 3
3 . A Process Layout 6
4. A Cellular Layout
6
5. Family 1 Load by Period 14
6. Family 2 Load By Period 14
7. Total Load by Period 15
IX
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
CONFIGURING CELL SYSTEMS TO HANDLE VARIABLE DEMAND
By
Jeffrey Schaller
May 1996
Chairperson: Dr. S. Selcuk Erenguc
Major Department: Decision and Information Sciences
This research addresses the problem of configuring cell
systems for environments that experience variable demand.
Past cell formation models consider only average product
demand to determine configurations of cells. Short term
fluctuations in demand may cause a cell configuration that
would appear to be good, when considering only average
demand, to perform poorly because during some periods
machines of a certain type may be under utilized in one cell
while machines in another cell may not be able to produce
the required products. For these reasons, a methodology for
configuring cell systems in environments that experience
variable demand is needed.