Table Of ContentSpringer Series in Advanced Manufacturing
For furthervolumes:
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R. Venkata Rao
Decision Making
in the Manufacturing
Environment Using
Graph Theory
and Fuzzy Multiple
Attribute Decision
Making Methods
Volume 2
123
R. VenkataRao
Mechanical EngineeringDepartment
S.V. NationalInstitute ofTechnology
Surat, Gujarat
India
ISSN 1860-5168
ISBN 978-1-4471-4374-1 ISBN 978-1-4471-4375-8 (eBook)
DOI 10.1007/978-1-4471-4375-8
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Dedicated to my parents (Lakshmi Narayana
and Jayamma), dearest wife (Sujatha Rao)
and beloved daughter (Jaya Lakshmi)
Preface
Manufacturing is the backboneofany industrialized nation. The advancementsin
manufacturingtechnologiesintherecentpasthavebroughtaboutametamorphism
intheworldindustrialscene.Alongarrayofemergingtechnologiesopenedupthe
potentialforavarietyofnewproducts.Fastchangingtechnologiesontheproduct
front cautioned the need for an equally fast response from the manufacturing
industries. To meet the challenges, the manufacturing industries have to select
appropriate manufacturing strategies, product designs, manufacturing processes,
work piece, and tool materials, machinery, and equipment, and so on. The
selection decisions are complex as decision making is more challenging today.
Decision makers in the manufacturing sector frequently face the problem of
assessing wide range of alternative options and selecting one based on a set of
conflicting criteria. Efforts need to be extended to identify the criteria which
influence an alternative selection for a given problem under consideration, using
simple,andlogicalmethods,toeliminateunsuitablealternativesandselectionofa
most appropriate alternative to strengthen the existing selection procedures.
Keeping in view of these aspects, I have previously authored a book entitled
‘‘Decision Making in the Manufacturing Environment Using Graph Theory and
Fuzzy Multiple Attribute Decision Making Methods’’ published by Springer,
London in 2007 and presented simple, systematic, and logical methods.
The book published in 2007 described the methodologies and applications of
six MADM methods namely, graph theory and matrix approach (GTMA),
Weighted Sum Method (WSM), Weighted Product Method (WPM), Analytic
Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal
Solution (TOPSIS), and Compromise Ranking Method (VIKOR). However, other
MADMmethodssuchasDataEnvelopmentAnalysis(DEA),PreferenceRanking
METHod for Enrichment Evaluations (PROMETHEE), ELimination Et Choix
Traduisant la Realité (ELECTRE), COmplex PRoportional ASsessment (COP-
RAS), UTility Additive (UTA), Ordered Weighted Averaging (OWA), etc., were
not covered in the book. Hence, the proposed book, which can be considered as
volume 2 of the first book, includes all these widely used MADM methods. The
existing MADM methods are improved in this book by removing certain
vii
viii Preface
deficiencies and incorporating certain changes. Furthermore, three novel multiple
attribute decision making methods for solving the decision-making problems of
the manufacturing environment are proposed. Of these, two methods namely,
Subjective and Objective Integrated Weights (SOIW) method and the Weighted
Euclidian Distance Based Approach (WEDBA) method consider the objective
weightsofimportanceoftheattributesaswellasthesubjectivepreferencesofthe
decision maker to decide the integrated weights of importance of the attributes.
Furthermore, these methods use fuzzy logic to convert the qualitative attributes
into the quantitative attributes. Various application examples are presented to
illustrate the potential of the proposed methods. The ‘‘Combinatorial Based
MathematicsApproach(CBMA)’’proposedinthisbookcombinestheadvantages
of both GTMA with AHP methods.
The proposed book documents the latest research works including the author’s
ownworksrelatedtoeachofthe listedtopics.Furthermore,thebookpresentsthe
realindustrialcasestudiesundereachtopic,resultsofapplicationoftheproposed
methods,andthecomparisons.Thealgorithmsandcomputerprogramsforvarious
MADMmethodsincludedinthebookwillbeverymuchusefultothereaders.The
book will be very useful to the decision makers in the manufacturing sector as it
makes decision making easier, logical, systematic, efficient, and effective.
The book is intended for designers, manufacturing engineers, practitioners,
managers, institutes involved in design, and manufacturing related projects,
applied research workers, academics, and graduate students in mechanical,
industrial, and manufacturing engineering.
I am gratefulto Anthony Doyle and Grace Quinn ofSpringer-Verlag, London,
for their support and help in producing this book. I wish to thank various
researchers and the publishers of international journals for giving me the
permission to reproduce certain portions of their published research works.
I gratefully acknowledge the support of my past and present M.Tech. and Ph.D.
students. My special thanks to the Director, Registrar, and my colleagues at S.V.
National Institute of Technology.
While every attempt has been made to ensure that no errors (printing or
otherwise)enterthebook,thepossibilityofthesecreepingintothebookisalways
there. I will be grateful to the readers if these errors are pointed out. Suggestions
for further improvement of the book will be thankfully acknowledged.
Surat, India, August 2012 R. Venkata Rao
Contents
1 Multiple Attribute Decision Making in the Manufacturing
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Importance of Multiple Attribute Decision Making
in the Manufacturing Environment. . . . . . . . . . . . . . . . . . . . . 1
1.2 Some Important Decision Making Situations
of the Manufacturing Environment. . . . . . . . . . . . . . . . . . . . . 2
1.3 Multiple Attribute Decision Making Methods . . . . . . . . . . . . . 3
1.4 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Improved Multiple Attribute Decision Making Methods . . . . . . . . 7
2.1 Improved Analytic Hierarchy Process Method. . . . . . . . . . . . . 7
2.1.1 Formulating the Decision Table. . . . . . . . . . . . . . . . . . 8
2.1.2 Deciding Weights of the Attributes . . . . . . . . . . . . . . . 8
2.1.3 Calculating Composite Performance Scores . . . . . . . . . 10
2.2 Improved Technique for Order Preference by Similarity
to Ideal Solution Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Formulating the Decision Table. . . . . . . . . . . . . . . . . . 10
2.2.2 Deciding Weights of the Attributes . . . . . . . . . . . . . . . 11
2.2.3 Calculating Composite Performance Scores . . . . . . . . . 11
2.3 Data Envelopment Analysis Method. . . . . . . . . . . . . . . . . . . . 12
2.3.1 The Basic CCR Model. . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 Strengths and Limitations of Basic CCR Model . . . . . . 15
2.3.3 Reduced CCR Model. . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.4 Improved RCCR/Assurance Region Model. . . . . . . . . . 16
2.4 Improved Preference Ranking Organization Method
for Enrichment Evaluations. . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1 Formulation of Decision Table . . . . . . . . . . . . . . . . . . 18
2.4.2 Deciding Weights of the Attributes . . . . . . . . . . . . . . . 18
2.4.3 Improved PROMETHEE Calculations . . . . . . . . . . . . . 18
ix
x Contents
2.5 Improved ELimination Et Choix Traduisant
la REalité Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5.1 Construction of the Decision Table . . . . . . . . . . . . . . . 22
2.5.2 Calculating the Weights of the Attributes
Using AHP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5.3 Calculations Using ELECTRE for Final Ranking . . . . . 22
2.6 Improved COmplex PRoportional ASsessment Method. . . . . . . 24
2.6.1 Construction of the Decision Table . . . . . . . . . . . . . . . 25
2.6.2 Calculating the Weights of the Attributes
Using AHP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6.3 COPRAS Calculations for Final Ranking . . . . . . . . . . . 25
2.7 Improved Gray Relational Analysis Method . . . . . . . . . . . . . . 27
2.8 Improved Utility Additive Method. . . . . . . . . . . . . . . . . . . . . 30
2.9 VIKOR Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.10 Improved Ordered Weighted Averaging Method . . . . . . . . . . . 35
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 Applications of Improved MADM Methods to the Decision Making
Problems of Manufacturing Environment. . . . . . . . . . . . . . . . . . . 41
3.1 Applications of the DEA RCCR/AR Method. . . . . . . . . . . . . . 41
3.1.1 Material Selection for a Given
Engineering Application. . . . . . . . . . . . . . . . . . . . . . . 41
3.1.2 Metal Stamping Layout Selection . . . . . . . . . . . . . . . . 46
3.1.3 Modern Machining Method Selection. . . . . . . . . . . . . . 49
3.1.4 Product End-of-Life Scenario Selection . . . . . . . . . . . . 50
3.2 Applications of the Improved PROMETHEE Method. . . . . . . . 52
3.2.1 Environment Friendly Cutting Fluid Selection
for a Given Machining Application . . . . . . . . . . . . . . . 52
3.2.2 Evaluation of Environmentally Conscious
Manufacturing Programs. . . . . . . . . . . . . . . . . . . . . . . 57
3.2.3 Product End-of-Life Scenario Selection . . . . . . . . . . . . 60
3.2.4 Rapid Prototyping System Selection. . . . . . . . . . . . . . . 62
3.2.5 Gear Material Selection . . . . . . . . . . . . . . . . . . . . . . . 64
3.2.6 Flexible Manufacturing System Selection. . . . . . . . . . . 66
3.2.7 Material Handling Equipment Selection . . . . . . . . . . . . 67
3.3 Applications of the Improved ELECTRE Method . . . . . . . . . . 69
3.3.1 Machine Tool Selection . . . . . . . . . . . . . . . . . . . . . . . 69
3.3.2 Facility Location Selection. . . . . . . . . . . . . . . . . . . . . 73
3.4 Applications of the Improved COPRAS Method . . . . . . . . . . . 74
3.4.1 Material Selection for Given
Engineering Application. . . . . . . . . . . . . . . . . . . . . . . 74
3.4.2 Rapid Prototyping System Selection. . . . . . . . . . . . . . . 76
Contents xi
3.5 Applications of the Improved AHP Method . . . . . . . . . . . . . . 77
3.5.1 Environmentally Conscious Manufacturing
Program Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.5.2 Selection of Electroplating System. . . . . . . . . . . . . . . . 81
3.6 Applications of the Improved GRA Method . . . . . . . . . . . . . . 85
3.6.1 Material Selection for a Given
Engineering Application. . . . . . . . . . . . . . . . . . . . . . . 85
3.6.2 Rapid Prototyping Process Selection . . . . . . . . . . . . . . 92
3.6.3 Plant Layout Design Selection. . . . . . . . . . . . . . . . . . . 96
3.6.4 Product Design Selection of a Power
Electronic Device . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.7 Applications of the Improved UTA Method . . . . . . . . . . . . . . 104
3.7.1 Machine Group Selection in a Flexible
Manufacturing Cell . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.2 Vendor Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
3.8 Applications of the Improved VIKOR Method . . . . . . . . . . . . 120
3.8.1 Plant Layout Design Selection. . . . . . . . . . . . . . . . . . . 120
3.8.2 Rapid Prototyping System Selection. . . . . . . . . . . . . . . 122
3.9 Applications of Improved OWA Method . . . . . . . . . . . . . . . . 123
3.9.1 Machine Group Selection in a Flexible
Manufacturing Cell . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.9.2 Vendor Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4 A Novel Subjective and Objective Integrated Multiple
Attribute Decision Making Method. . . . . . . . . . . . . . . . . . . . . . . . 137
4.1 Proposed Novel Multiple Attribute Decision
Making Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.1.1 Preparation of the Decision Table . . . . . . . . . . . . . . . . 138
4.1.2 Determination of Weights of Importance
of the Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.1.3 Computation of Preference Index. . . . . . . . . . . . . . . . . 140
4.1.4 Final Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.2 Application of the Novel Subjective and Objective Integrated
Method to the Problems of Manufacturing Environment. . . . . . 141
4.2.1 Material Selection for a High Speed Naval Craft. . . . . . 141
4.2.2 Material Selection of a Flywheel. . . . . . . . . . . . . . . . . 145
4.2.3 Material Selection of a Cryogenic Storage Tank . . . . . . 148
4.2.4 Industrial Robot Selection. . . . . . . . . . . . . . . . . . . . . . 149
4.2.5 Environment Friendly Cutting Fluid Selection
for Given Machining Application . . . . . . . . . . . . . . . . 152
4.2.6 Flexible Manufacturing System Selection. . . . . . . . . . . 153
4.3 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
xii Contents
5 A Novel Weighted Euclidean Distance-Based Approach. . . . . . . . . 159
5.1 Weighted Euclidean Distance Approach. . . . . . . . . . . . . . . . . 159
5.2 Applications of Proposed WEDBA Method for Decision
Making in the Manufacturing Environment. . . . . . . . . . . . . . . 164
5.2.1 Material Selection of a Flywheel. . . . . . . . . . . . . . . . . 164
5.2.2 Robot Selection for a Given Industrial Application . . . . 167
5.2.3 Flexible Manufacturing System Selection. . . . . . . . . . . 172
5.2.4 Optimum Parameters Selection of Green Electric
Discharge Machining. . . . . . . . . . . . . . . . . . . . . . . . . 183
5.2.5 Selection of Best Product End-of-Life Scenario. . . . . . . 187
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
6 A Combinatorial Mathematics-Based Decision Making Method. . . 193
6.1 Combinatorial Mathematics-Based Approach (CMBA). . . . . . . 193
6.2 Applications of CMBA to the Decision-Making Problems . . . . 195
6.2.1 Selection of Electroplating System. . . . . . . . . . . . . . . . 195
6.2.2 Robot Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
6.2.3 Welding Process Selection . . . . . . . . . . . . . . . . . . . . . 199
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
7 Comparison of Different MADM Methods for Different Decision
Making Situations of the Manufacturing Environment . . . . . . . . . 205
7.1 Evaluation of Environmentally Conscious
Manufacturing Programs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
7.2 Rapid Prototyping System Selection. . . . . . . . . . . . . . . . . . . . 207
7.3 Gear Material Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
7.4 Flexible Manufacturing System Selection . . . . . . . . . . . . . . . . 209
7.5 Vendor Selection: An Industrial Case Study . . . . . . . . . . . . . . 210
7.6 Plant Layout Design Selection. . . . . . . . . . . . . . . . . . . . . . . . 213
7.6.1 Application of Improved AHP Method. . . . . . . . . . . . . 214
7.6.2 Application of Improved GRA Method . . . . . . . . . . . . 215
7.6.3 Application of Improved UTA Method. . . . . . . . . . . . . 215
7.6.4 Application of Improved OWA Method. . . . . . . . . . . . 217
7.6.5 Application of Improved VIKOR Method. . . . . . . . . . . 217
7.6.6 Application of WEDBA Method . . . . . . . . . . . . . . . . . 218
7.7 Application of CMBA Method . . . . . . . . . . . . . . . . . . . . . . . 218
7.8 Warehouse Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
7.8.1 Application of Improved AHP Method. . . . . . . . . . . . . 224
7.8.2 Application of Improved GRA Method . . . . . . . . . . . . 225
7.8.3 Application of improved UTA Method. . . . . . . . . . . . . 225
7.8.4 Application of Improved OWA Method. . . . . . . . . . . . 227
7.8.5 Application of Improved VIKOR Method. . . . . . . . . . . 229
7.8.6 Application of WEDBA Method . . . . . . . . . . . . . . . . . 230
7.8.7 Application of CMBA Method . . . . . . . . . . . . . . . . . . 230
