1. CONFERENCE ORGANIZATION - APPLICATION OF FUZZY MULTIPLE ATTRIBUTE GROUP DECISION MAKING METHOD IN STRETCHER CONCEPT SELECTION The Jaya Suteja
1. CONFERENCE ORGANIZATION
Honorary Executive Committee Prof. dr. Fasli Jalal, Ph.D
Director of DGHE, the Department of National Education, Republic of Indonesia
Prof. Chairuddin P. Lubis, DTM&H, Sp. A(K)
Rector, University of Sumatera Utara (USU)
Prof. Dr. Ir. Satryo Soemantri Brodjonegoro
Visiting Professor, TUT Japan
Prof. Ir. Mansur Ma’shum, Ph.D
Rector, University of Mataram
Prof. Dr. Ir. Djoko Santoso
Rector, Institute of Technology Bandung
Prof. Dr. Ir. Djoko Suharto
Institute of Technology Bandung
International Advisory Board Prof. Dr. Shigeru Aoki
Prof. Youn Y. Earmme
Toyo University, Tokyo, Japan (Retired) Korea Advanced Institute of Science &
Prof. Dr. Hiroomi Homma
Technology, Yusong‐gu, Korea Toyohashi University of Technology,
Prof. Mamtimin Geni
Toyohashi, Japan Xinjiang Engineering College, Xinjiang, China
Prof. Dr. Masashi Daimaruya Prof. Dr. Pramote Dechaumphai
Muroran Institute of Technology, Chulalongkorn University, Bangkok, Thailand Muroran, Hokkaido, Japan
Prof. Dr. Bustami Syam
Prof. Dr. Masanori Kikuchi
Universitas Sumatera Utara, Medan, Indonesia Science University of Tokyo, Tokyo, Japan
Prof. Yasuhiro Kanto
Prof. Dr. Kikuo Kishimoto
Toyohashi University of Technology, Toyohashi, Tokyo Institute of Technology, Tokyo,
Japan
Japan Prof. Ahmad Kamal Arifin
Prof. Dr. Jay S. Gunasekera
University of Kebangsaan Malaysia, Bangi, Ohio University, Athens, Ohio, USA
Malaysia
Prof. Dr.‐Ing. Peter Dietz
Prof. Jamasri, Ph.D
Institut fur Mascinenwesen der University of Gadjah Mada, Yogyakarta, Technischen Universitat, Clausthal,
Indonesia
Germany Assoc. Prof. Ichsan S. Putra
Prof. Dr. Benjamin Soenarko
Institute of Technology Bandung, Bandung, Institute of Technology Bandung ,
Indonesia
Bandung, Indonesia
Prof. Komang Bagiasna
Inter ‐University Center, Institut Teknologi Bandung, Bandung, Indonesia
Secretary:
Chairperson: Ikhwansyah Isranuri (USU, Indonesia)
Bustami Syam IC‐STAR USU, Indonesia)
Program Coordinator:
Hendri Nurdin; Batu Mahadi Siregar; Zulfikar Co‐Chairperson:
(Director
Secretariat & Treasurer:
Masanori Kikuchi (SUT, Japan)
Lely Savira Harahap
Ahmad Kamal Ariffin (UKM, Malaysia) Ichsan S. Putra (ITB, Bandung)
Supporting Staffs:
M. Sabri (UKM), Farid Triawan (Tokyo Tech, Regular Conference Coordinators:
Japan),
Samsul Rizal (UNSYIAH)
A. Rahayu, Rahmayani Siregar, Eva Ikhwansyah Isranuri (USU) Mugdhiyana, Suita Sari Sabar Nababan (UNRAM)
Eliza
Local Supporting Staffs:
Taufiq
Bin Nur (USU)
(University of Mataram’s Students) Student Conference Coordinators:
M. Sabri (UKM)
Heru Santoso (UGM)
Tulus (USU)
The conference covers, but not limited to, the following topics: Fracture Behaviors
FEM in Forming Process Computational Mechanics Static and Dynamic Problems Noise and Vibration Control in Engineering The Atomic/Molecular Dynamics Analysis of Machine Element Design Computational Method in Chemical Engineering FEM Application in Geotechnical and Structural Engineering Numerical and Experimental Fracture Mechanics Numerical Analysis Tools for Web‐Based Applications Computational Methods in Thermo and Fluid Mechanics Artificial Intelligence Application in Engineering, such as Expert System, Pattern Recognition, Neural
Network Genetic Algorithm, etc. Metal and Polymeric Foams Experimental Solid and Fluid Mechanics
Day ‐1 (Friday, 15 May 2009)
Technical Session ACTIVITIES
TIME
Senggigi Room (Regular Conference) Mataram Room (Regular Conference)
‐ 15:30 FRACTURE ARTIFICIAL INTELLIGENCE APLICATION IN 13:40 MOLECULAR DYNAMICS ; FEM APLICATION ENGINEERING ;
BEHAVIORS ; THE ATOMIC /
Chairperson: Prof. Homma Hiromi Chairperson: Prof. Ahmad Kamal Arifin 15:30 – 15:50
COFFEE/TEA BREAK
FEM APPLICATION IN GEOTECHNICAL; COMPUTATIONAL METHODS IN THERMO 15:50 ‐ 17:40 Chairperson: AND FLUID MECHANICS
Ir. Budi Santosa, MS., Ph.D. Chairperson: Prof. Miyagi Kiyohiro 7:00 ‐ 11:00
BANQUET DINNER
Table 7.2 Presentation Schedule in Technical Session (Day‐2)
Day ‐2 (Saturday, 16 May 2009)
Technical Session ACTIVITIES
TIME
Senggigi Room (Regular Conference) Mataram Room (Regular Conference)
COMPUTATIONAL METHODS IN COMPUTATIONAL METHOD IN CHEMICAL 8:30 ‐10:20 ELECTRICAL AND ELECTRONIC APPLICATION ENGINEERING
Chairperson: Dr. Bambang Agus Kironoto Chairperson: Dr. Syifaul Huzni, M.Sc. 10:20 ‐10:40
COFFEE/TEA BREAK
FEM APPLICATION; COMPUTATIONAL ANALYSIS OF MACHINE ELEMENT DESIGN; 10:40 ‐12:40
MECHANICS; NUMERICAL & EXPERIMENTAL FRACTURE MECHANICS;
Chairperson: Prof. Dr. Yasuhiro Kanto Chairperson: Prof. Ahmad Kamal Arifin 12:40 ‐13:40 .
LUNCH BREAK
ARTIFICIAL INTELLIGENCE APLICATION IN COMPUTATIONAL METHODE ENGINEERING; COMPUTATIONAL 13:40 ‐15:30
METHODE Chairperson: Dr.Eng. Agus Setyo Muntohar Chairperson: Dr. Amna Abdurrahman
Reviewer :
1. Prof. Dr. Kikuchi Masanori 2. Prof. Dr. Ahmad Kamal Arifin
COFFEE/TEA BREAK
CLOSING CEREMONY / GROUP PHOTO
Table 7.3 Detailed of Presentation Schedule in Technical Session AFTERNOON SESSION DAY 1 – FRIDAY, 15 May 2009
VENUE
A (Regular Conference):
VENUE
B (Regular Conference):
Senggigi Room (First Floor) Mataram Room (First Floor) Session
FRACTURE BEHAVIORS ; THE ATOMIC / ARTIFICIAL INTELLIGENCE APLICATION IN MOLECULAR DYNAMICS ; FEM APLICATION
ENGINEERING ;
Chairperson: Prof. Dr. Homma Hiromi Chairperson: Prof. Ahmad Kamal Arifin
1. Fatigue Crack Growth Simulation In 3‐D 1. Tool Life Analysis by Partial Swarm Keynote Field using S‐FEM
Optimization
Speaker Masanori Kikuchi, Yoshitaka Wada and M.M.Noor, K.Kadirgama, M.M.Rahman, Yulong Li
M.S.M.Sani, M.R.M.Rejab, A.K.Ariffin
2. A Finite Element Investigation of the 2. Sustainable Product Development for Car Residual Stress and Deformation in
Lifting Equipment using Virtual Reality Sliding Contact between Cylinders.
Willyanto Anggono, Ian Hardianto Rifky Ismail, M. Tauviqirrahman,
Siahaan, R.M. Moch. Trah Isworo Jamari, and D.J. Schipper
Nugroho, Satria Arief Budi
3. Finite Element Modeling of Tire‐Road 3. Solving the Multiobjective Transportation Contact Problem using Fuzzy Compromise
M. Sabri, A. K. Ariffin & M. J. M. Nor
Programming
Parwadi Moengin
Method Solution for Piping Stress Analysis on Electrical
4. Static and Dynamic Load Calculation for 4. Optimum
Determining Brake Distance Design Power Generation
Ian Hardianto Siahaan, Amelia Yusri Heni N. A.
Sugondo, and Willyanto Anggono
5. Pore Water Pressure Change Of A 5. Surface Rougness Analysis in End Milling Homogenous Slope During Rainfall
with Response Ant Colony Optimization Infiltration
M.M.Noor, M.M.Rahman, Agus Setyo Muntohar and Hung‐Jiun
K.Kadirgama, M.S.M.Sani, A.K.Ariffin
Liao
AFTERNOON SESSION DAY 1 – FRIDAY, 15 May 2009 VENUE
B (Regular Conference): Senggigi Room (First Floor)
A (Regular Conference):
VENUE
Mataram Room (First Floor) Session
FEM APPLICATION IN GEOTECHNICAL; COMPUTATIONAL METHODS IN THERMO
AND FLUID MECHANICS
Chairperson: Ir. Budi Santosa, MS., Ph.D. Chairperson: Prof. Dr. Miyagi Kiyohiro
1. Lateral Movement of the Tie‐Back Wall 1. Numerical Investigation of In‐Cylinder in Alluvial Soil
Pressure Characteristic of Port Keynote
Injection Compressed Natural Gas Speaker
Agus Setyo Muntohar, Hung ‐Jiun, Liao
Engine Model
Rosli Abu Bakar and Semin
2. Numerical Analysis of Time Dependent 2. The k-ε Turbulence Model for Laterally Loaded Pile in Clay
Predicting Turbulence Characteristics
Jasim M Abbas, Zamri Hj Chik, Mohd
in Rough Uniform Open Channel Flow
Raihan Taha, Qassun S. M Shafiqu Bambang Agus Kironoto
3. Numerical Modelling on Shallow Water 3. Sustainable Technology for Improving 2D Equations Applied on Flow Around a
Autoclave Performance using Finite Cylinder Element Application
Bambang Yulistianto Willyanto Anggono, Ian Hardianto Siahaan, Andree Kadana Tirta, Satria
Budi
A Design Approach of Shallow Arief
Foundation Based on Rigid Plastic Analysis 4. Verification of A VOF–Based Simulation
Husna Asmaul
for Thin Liquid Film Flow Applications
S. Balachandran, N.H. Shuaib,
5. Gravitational Pump Design Based on
H. Hasini, M.Z. Yusoff
Runge ‐Kutta Method
J. Aminuddin
5. Trends of Engine Speed On Engine Performance Of Four Cylinder Direct Injection Hydrogen Engine
M. M. Rahman, Mohammed K. Mohammed, Rosli A. Bakar, M.M. Noor and K. Kadirgama
VENUE
B (Regular Conference): SenggigiRoom (First Floor)
A (Regular Conference):
VENUE
Mataram Room (First Floor) Session
COMPUTATIONAL METHODS IN COMPUTATIONAL METHOD IN CHEMICAL
ELECTRICAL AND ELECTRONIC ENGINEERING
APPLICATION
Chairperson: Dr. Bambang Agus Kironoto Chairperson: Dr. Syifaul Huzni, M.Sc.
1. Kernel Adatron for Multiclass Support 1. Evaluation of Drag Force Effect on Keynote Vector Machine
Hold ‐Up in A Gas‐Liquid Stirred Tank
Speaker Budi Santosa
Reactor
R. Zadghaffari
2. Numerical Method for Constructing 2. Cathodic Protection Simulation for Optimal Bids by Electricity Generators in
Pipe ‐Lines Structure with Ribbon Deregulated Electricity Market
Sacrificial Anode
Vladimir Kazakov Safuadi, Syarizal Fonna, M. Ridha, Israr,
A. K. Ariffin and A. R. Daud
3. Analysis Throughput Multi ‐code 3. Prediction of Wax Deposition in Multicarrier CDMA S‐ALOHA To Support
Pipeline by CFD Techniques Various Data Rate
Hoda seyedinezhad, Farmarz Hormozi Indri Neforawati and Hoga Saragih
4. Corrosion Analysis using BEM by 4. Churn Prediction in Telecommunication
Considering Polarization Curve of Steel using Kernel Adatron
Syarizal Fonna, Safuadi, Israr, M. Budi Santosa
Ridha, and A. K. Ariffin
of Parallel in the Region Nganjuk (East‐Java) using
5. Local Short‐Term Wind Speed Prediction 5. Implementation
Computational tools for the Curing Neural Network
Simulation of Thermoset Composites Using the One Dimension Age
Ali Musyafa, Binti Cholifah, Imam
Algorithm
Robandi Amna Abdurrahman, Ahmad Kamal bin Zulkifle, Norma Alias, and Ishak
Hashim
VENUE
A (Regular Conference):
VENUE
B (Regular Conference):
Senggigi Room (First Floor) Mataram Room (First Floor) Session
FEM APPLICATION; COMPUTATIONAL ANALYSIS OF MACHINE ELEMENT DESIGN;
MECHANICS; 10.40 ‐12.40 Chairperson: Prof. Ahmad Kamal Arifin
Chairperson: Prof. Dr. Yasuhiro Kanto
1. Shock and Elastic Waves on Dynamic 1. On
of Stress Compaction Process of Two Layered Concentration Factor in Thin Plate with
the Edge ‐effect
Keynote Powder Media in the Dies Two Holes
Speaker Kiyohiro Miyagi, Yukio Sano, Takuo Satryo Soemantri
Hayashi, Toshiyasu Sueyoshi
2. 3D Visualization of Wire Radiation 2. Stress Distribution Analysis of Stress Pattern using NEC2++ Antenna Radiation
Corrosion Cracking Specimen using Generator Ansys
Ridwan Montezari, Ignatius Dwi Syifaul Huzni, M. Ikhsan, M. Ridha & Mandaris, Lisandro Damian N Perez
A. Kamal Ariffin
Meyer, Soemarni Mardjoeki, R. Harry
Harjadi, Harry Ramza
3. Crack Initiation and Propagation in Nylon 6 Sphere under Various Impact 3. Sound Profile Measurement for Brackish
Velocities
Water Sutikno and H. Homma
Sunardi, Anton Yudhana, Jafri Din,
Saberi Mawi
4. Optimization of the simple plate using high cycle multiaxial fatigue criteria
4. Simulation for Predicting Thermal Effect
Ismail 1 A.K Ariffin, S. Abdullah
in the Eye’s Tissues Following A theraphy
A.E
using Laser Retinal Photocoagulator
AMT Nasution, NAP Ningtyas
5. Influence of Magnetic Field on Noise Reduction: Experimental Study on
5. Numerical Analysis of Harmonic Automotive Noise Silencer Propagation and Distortion Caused by a
Nonlinear Load in Balance Distribution
Ikhwansyah Isranuri, Eka Sunitra
Network
Sabar Nababan
B (Student Conference): VENUE
A (Regular Conference):
VENUE
Senggigi Room (First Floor) Mataram Room (First Floor) Session
ARTIFICIAL INTELLIGENCE APLICATION IN COMPUTATIONAL METHODE ENGINEERING; COMPUTATIONAL
Chairperson:
13.40 – METHODE Dr.Eng. Agus Setyo Muntohar, M.Eng.Sc. 15.30 Reviewers :
Chairperson: Dr. Amna Abdurrahman 1. Prof. Dr. Kikuchi Masanori 2. Prof. Dr. Ahmad Kamal Arifin
1. Application of Fuzzy Multiple Attribute 1. Genetic Algorithms Approach for Group Dicision Making Method in
Multiobjective Stock Portfolio Keynote Stretcher Concept Selection
Optimization Problem Speaker
The Jaya Suteja Hoklie and Lavi Rizki Zuhal
2. Symmetrical and Unsymmetrical Flow 2. Application
Augmented Lagrange Separation in Supersonic Ideal Contour Multiplier Method In the Collaborative
Nozzles
H. Jihad, Dedi Priadi, Tresna P. Design Soemardi, Eddy S. Siradj Yuwono
Optimization of Engineering Product
Bagus
B. Pratiknyo
3. Comparison Eigenvalue of Planar 3. Analysis of Tapered Velocity and Tapered
Waveguide Characteristic Equation Coupling Couplers
using Analytical Approach and Method
Ary Syahriar
of Line
Wibi Noviardi
4. Impact Response of Traffic Cones with Different Lower Base Structures
4. Switch Characteristic in Silica Directional
Bustami Syam, Weriono, Rahmawati,
Couplers
Samsul Rizal, Basuki Wirjosentono Disra Agifral, Syifa’ul Barir, and Ary
Syahriar
5. Design and Analysis of Supersonic Axysimmetric Minimum Length Nozzle (MLN) As Rocket Nozzle
Bagus
H. Jihad, Dedi Priadi, Tresna P. Soemardi, Eddy S. Siradj
6. Stress Distribution Simulation in Non‐ Standardized Motor Cycle Helmet Subjected impact Loading
Izwar Lubis, Bustami Syam, Samsul Rizal, Tugiman
DECISION MAKING METHOD IN STRETCHER CONCEPT SELECTION
The Jaya Suteja
Product Design Research Group, Department of Manufacturing Engineering
University of Surabaya Jl. Raya Kalirungkut, Surabaya, INDONESIA Phone/Fax.: +62-31- 2981397 E-mail: jayasuteja@yahoo.com
Abstract
One of crucial phases in product design and development is concept selection phase. The best concept resulted in this phase will be developed and embodied to fulfill the customer need. In most cases, the importance weight of each criterion used in concept selection and the performance rate of each concept alternative with respect to each criterion are determined based on the competence and intuition of some decision makers. Therefore, they are subjective, imprecise, and vague. In this paper, the best concept selection of stretcher is determined by applying Fuzzy Multiple Attribute Decision Making as the aggregation method of performance ratings with respect to all criteria for each alternative and Technique for Order Preference by Similarity to Ideal Solution as the ranking method of alternatives according to the overall aggregated performance ratings. Three stretcher concept alternatives are evaluated to select the best stretcher concept alternative. The criteria used in the stretcher concept selection are lightness, compactness, tight bond, strong join, reasonable price, easiness to use, easiness to identify blood, and easiness to hold. In addition, the decision makers who give opinion related to the importance weight and performance rate are designer, manufacturer, and lead user. By applying the fuzzy multiple attribute group decision making, the concept selection process is more effective and objective.
Keywords: fuzzy multiple attribute group decision, stretcher, concept selection
1. Introduction competence and intuition of some decision makers. The decision makers usually consist of
One of crucial phases in product design and lead user, designer, and manufacturer. Because the development is concept selection phase. The best
importance weight is determined based on the concept resulted in this phase will be developed
intuition, the importance weight of each criterion and embodied to fulfill the customer need. In the
is subjective, imprecise, and vague. The concept selection phase, two important steps must
subjectivity, imprecision, and vagueness are
be conducted carefully to obtain a good result. The mostly caused by three sources such as first step is determination of selection criteria and
unquantifiable, incomplete, and non-obtainable the importance weight of each criterion. The
information [1].
second step is determination of performance rate Furthermore, the performance rate of each for each concept alternative with respect to each
alternative concept with respect to each criterion is alternative concept with respect to each criterion is
et. al. compare the existing Fuzzy TOPSIS required to implement a methodology that
methods based on their type of attribute weight, accommodate the subjectivity, imprecision, and
type of fuzzy number, ranking method, and vagueness in determining the importance weight
normalization method [6].
of all selection criteria and performance rating of According to the Olcer and Odabasi all concept alternatives.
classification, the possible fuzzy linguistic FMADM method for crisp and fuzzy type of
2. Literature Study
performance rating and fuzzy type of attribute To select the best alternative form a finite
weight is the method proposed by Chen, C. T. [4]. number of alternatives characterized by multiple
Olcer and Odabasi suggest TOPSIS method in attributes, Multiple Attribute Decision Making
ranking phase because it is quite effective in (MADM) can be applied as the selection methods.
identifying the best alternative quickly and gives Because the classical MADM cannot effectively
general and broad acceptability in many problem handle multiple attribute group decision making
domains.
problems with subjective, imprecise and vague Chen, C. T., proposes that the rating of each information, then Fuzzy Multiple Attribute
alternative and the weight of each criterion, which Decision Making (FMADM) is implemented to
are described by linguistic terms, are expressed in solve the problem with subjective, imprecise, and
triangular fuzzy numbers [8]. Then a vertex vague information.
method for TOPSIS is proposed to calculate the Some researches have been conducted in
distance between two triangle fuzzy numbers. In applying FMADM to select the best alternative
his research, it is assumed that the fuzzy positive from a finite set of alternatives. Liang and Wang
(FPIS) and negative ideal solutions (FNIS) as (1, implemented FMADM in robot selection [2].
1, 1) and (0, 0, 0) respectively. Chen applied FMADM for handling multiple
Referring to Chen, et. al., A = {A 1 ,A 2 , ..., A m } attribute fuzzy decision making problems in tool
is a set of alternatives for the problem of fuzzy steel materials selection [3]. Olcer and Odabasi
hybrid multiple attribute decision making and C = develop a new FMADM and apply it to
{C 1 , C 2 , ..., C n } is a set of criteria with which all propulsion/maneuvering system selection [4].
the alternatives are rated. The performance rating Chuu also applies FMADM to evaluate Advance
of alternative A i to criteria C j is denoted as x ij (i = Manufacturing Technology [5].
1, 2, ..., m and j = 1, 2, …, n)) The value of x ij is Based on the literature review performed by
not merely crisp but also fuzzy. All values of x ij Olcer and Odabasi, FMADM methods consist of
are formed a decision making matrix, it denoted as two phases, which are aggregation of the
D = (x ij ) m×n or
11 x 12 ... x 1 n for each alternative and determination the ranking
performance ratings with respect to all attributes
order of alternatives according to the overall
x 21 ... ... x ~
2 n aggregated performance ratings [4]. For the first
... ... ... ... phase, Olcer and Odabasi classify the methods to
... ... ... simple additive weighting based FMDAM ...
m 1 x m 2 ... x ~ mn based FMDAM approaches, outranking relation
approaches, Analytic Hierarchy Process (AHP)
Furthermore, w ~ = [ w ~ , w ~ 1 2 ,..., j ~ w n ] is the based FMDAM approaches, implied conjunction
methods, fuzzy linguistic approaches, and weight of criterion, where w ~ satisfies j
miscellaneous FMADM methods [4]. For the
0≤w j ≤ 1, Σ w j = 1 0≤w j ≤ 1, Σ w j = 1
calculated as
x ij = K [ x ij + x ij + ... + x ij ] (1)
j = 1 (6)
= 3 [( v ij − 0 ) + ( v ij − 0 ) + ... + ( v ij − 0 ) 2 ]
w j = K [ w j + w j + ... + w j ] (2)
for i = 1, 2, …, m
where ~ K x ~ ij and w j are the performance rating and
A closeness coefficient is defined to determine the importance weight of the K th decision maker
the ranking order of all alternatives. The closeness respectively.
coefficient of each alternative is calculated as
d CC i i = * − (7) variable and can be described by triangular fuzzy
In this research, ~ and x ij w ~ are linguistic j
number ( a ij , b ij , c ij ) and ( w j 1 , w j 2 , w j 3 ) for i = 1, 2, …, m. According to the closeness coefficient, we can determine the ranking order of
respectively. all alternatives and determine the alternative, To obtain the normalized fuzzy decision
which has the highest closeness coefficient as the matrix, the linear scale transformation is used. R ,
best alternative.
which denotes the normalized fuzzy decision The flow of work, which is followed in this matrix, is calculated as
research, is summarized as follows:
R ~= [ r ~ ij ] mxn (3)
First Stage
Step 1: Form a committee of decision makers where Step 2: Identify the selection criteria with types
~ b ij c ij r
a ij
ij = ( c j * , c j * , c j * )
of them
Step 3: List all possible alternatives cj * = max c ij for benefit criteria and
Step 4: Determine an appropriate linguistic
variables for the importance weight of criteria
Step 5: Determine an appropriate linguistic r ij = ( c ij , b ij , a ij ) variables for the performance rating of
alternatives with respect to selection criteria aj = min a ij for cost criteria
Step 6: Collect decision makers’ opinion for the Furthermore, because of the different
importance weight of each criterion importance of each criterion, the weighted
Step 7: Collect decision makers’ opinion for the normalized fuzzy decision matrix can be
performance rating of each alternative constructed as
Second stage
V ~= [ ~ v ij ] mxn (4)
Step 8: Aggregate the importance weight of
each criterion to obtain the aggregate fuzzy for i = 1, 2, …, m and j = 1, 2, …, n where
weight of criterion
v ~= ~ ~ ij r ij (.) w j . Step 9: Aggregate the performance rating of each alternative with respect to selection criteria
Then, we can define the FPIS (A*) and FNIS
(A ) as A * = ( v 1 , v 2 ,..., v n ) and
Step 10: Construct a fuzzy decision matrix
Third Stage
1 , v ~ 2 ,..., ~ v n ) where ~ v j = (1 ,1 ,1) and
Step 11: Normalize the fuzzy decision matrix Step 12: Construct a weighted normalized fuzzy
~ − v j = (0 ,0 ,0) for j = 1, 2, …, n.
decision matrix
The distance of each alternative from A* and Step 13: Define the Fuzzy Positive Ideal
A - are calculated as Solution and Fuzzy Negative Ideal Solution
Step 14: Calculate the distance of each
( i ~ d v , v ~ ij j )
3. Case Study this stage is to convert the performance ratings from the decision makers, which are mostly in
In this paper, a concept selection of a new expressed in linguistic term into standardized stretcher is taken as a case study. There are some
positive triangular fuzzy numbers. The linguistic characteristics in the new stretcher concept
terms and their corresponding fuzzy number may selection. First, the new stretcher concept
vary. However, a conversion scale with 5 labels as selection involves more that one criterion in
listed in table 1 is used.
selecting concept. The criteria used in the stretcher
concept selection are lightness (C1), compactness
Table 1. Linguistic Variables for the Ratings
(C2), tightness of bond (C3), strongness of join
Linguistic Terms
Fuzzy Number
(C4), reasonable price (C5), easiness to use (C6),
Very Poor (VP)
easiness to identify blood (C7), and easiness to
Poor (P)
hold (C8). According to the type of attribute, all
Fair (F)
criteria above are included as benefit type of
Good (G)
attribute. Each of criteria has its importance
Very Good (VG)
weight determined based on the decision makers
opinion. According to the type of variable, all The importance weights of all criteria are also performance rating of criteria in the concept
converted from linguistic term into standardized selection are linguistic variable. Meanwhile, the
positive triangular fuzzy numbers. The conversion type of all importance weight is only linguistic
scale for the importance weight can be seen in variable.
table 2.
In the new stretcher concept selection, more
than one individual with his or her own
Table 2. Linguistic Terms for the Importance
competence and intuition are required to give his
Weight
or her opinion with regard to the importance
Linguistic Terms
Fuzzy Number
weight of all criteria and the performance rating of (0.0, 0.0, 0.1)
Very Low (VL)
Low (L)
all alternatives. The decision makers are lead user
Medium (M)
(D1), designer (D2), and manufacturer (D3).
High (H)
From generation concept phase, five concept
Very High (VH)
alternatives of stretcher are generated. Through
concept screening, these five concept alternatives The decision makers’ opinion for the are screened to three concept alternatives. Then,
importance weight of each criterion is shown in three concept alternatives (A1 – A3) are evaluated
table 3. Meanwhile, the decision makers’ opinion to obtain the best concept by performing concept
for the performance rating for each alternative can selection.
be seen in table 4.
The first concept alternative (A1) uses one At the second stage, the importance weights of strap with six holes and implements a button to
all criteria based on three decision makers are join two holders of the stretcher. For the second
aggregated to obtain the aggregate fuzzy weight of concept alternative (A2), one strap with a buckle
criteria. Then, all performance ratings of each is used. In addition, it has fourteen holes in it. To
alternative with respect to all criteria based on join two holders of the stretcher, the small holder
three decision makers are aggregated to construct is inserted to the larger holder and both of them
a fuzzy decision matrix. The fuzzy decision matrix are hold firmly with the aid of a pin. The last
and fuzzy weight of three alternatives can be seen concept alternative (A3) is similar to the first
in table 5.
concept selection. Instead of using a button, the At the last stage, the fuzzy decision matrix is concept selection. Instead of using a button, the At the last stage, the fuzzy decision matrix is
Weight of Three Alternatives
Because the FPIS (A*) and FNIS (A - ) are
C1 C2 C3
defined according to Chen, C. T., as [(1, 1, 1), (1,
respectively, the distance of each alternative from
FPIS and FNIS can be calculated as listed in table
Table 5. The Fuzzy Decision Matrix and Fuzzy
Weight of Three Alternatives (cont.)
C4 C5 C6
Table 3. The Importance Weight of Each
D1 D2 D3 A3 (9.0, 10, 10)
Table 5. The Fuzzy Decision Matrix and Fuzzy
C4 (0.7, 0.9, 1.0) (0.9, 1.0, 1.0) (0.7, 0.9, 1.0)
Weight of Three Alternatives (cont.)
C5 (0.3, 0.5, 0.7) (0.9, 1.0, 1.0) (0.7, 0.9, 1.0) C7 C8 C6 (0.7, 0.9, 1.0) (0.7, 0.9, 1.0) (0.7, 0.9, 1.0)
A1 (3.0, 5.0, 7.0) (3.0, 5.0, 7.0) C7 (0.3, 0.5, 0.7) (0.0, 0.1, 0.3) (0.0, 0.1, 0.3)
A2 (7.0, 9.0, 10) (7.0, 9.0, 10) C8 (0.9, 1.0, 1.0) (0.0, 0.1, 0.3) (0.7, 0.9, 1.0)
A3 (3.0, 5.0, 7.0) (3.0, 5.0, 7.0) Weight (0.1, 0.2, 0.4) (0.5, 0.7, 0.8)
Table 4. The Performance Rating of all
Alternatives with Respect to All Criteria Table 6. The Fuzzy Normalized Decision D1 D2 D3 Matrix
A1 (0.0, 0.1, 0.3) (0.4, 0.7, 1.0) (0.2, 0.4, 0.6) A3 (7. 9. 10)
A2 (0.7, 0.9, 1.0) (0.4, 0.7, 1.0) (0.8, 0.9, 1.0) C2 A1 (3, 5, 7)
A3 (0.7, 0.9, 1.0) (0.4, 0.7, 1.0) (0.2, 0.4, 0.6) A2 (3, 5, 7)
Table 6. The Fuzzy Normalized Decision
Matrix (cont.)
A1 (0.6, 0.8, 0.9) (0.0, 0.1, 0.3) (0.8, 0.9, 1.0) C4 A1 (7, 9, 10)
A2 (0.9, 1.0, 1.0) (0.4, 0.6, 0.9) (0.4, 0.6, 0.8) A2 (9, 10, 10)
A3 (0.9, 1.0, 1.0) (0.5, 0.8, 1.0) (0.7, 0.9, 1.0) A3 (9, 10, 10)
Table 6. The Fuzzy Normalized Decision
Matrix (cont.)
A1 (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) A2 (3, 5, 7)
A2 (0.7, 0.9, 1.0) (0.7, 0.9, 1.0) A3 (7, 9, 10)
A3 (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) C7 A1 (3, 5, 7)
Table 7. The Fuzzy Weighted Normalized
Decision Matrix
C8 A1 (3, 5, 7)
Decision Matrix (cont.)
[2] Liang, G. S., Wang, M. J., A Fuzzy Multi- C4 C5 C6 criteria Decision-making Approach for
A1 (0.43, 0.72, 0.9) (0.0, 0.07, 0.26)
Robot Selection, Robotics Computer
A2 (0.69, 0.93, 1.0) (0.24, 0.50, 0.79)
Integrated Manufacturing , Vol. 10, pp. 267–
A3 (0.69, 0.93, 1.0) (0.34, 0.63, 0.9)
274, 1993. [3] Chen, S.M., A New Method for Tool Steel
Table 7. The Fuzzy Weighted Normalized
Materials
Selection under Fuzzy
Decision Matrix (cont.)
Environment, Fuzzy Sets and Systems, Vol. C7 C8 92, pp. 265–274, 1997.
A1 (0.03, 0.12, 0.3)
[4] Olcer, A.I., Odabasi, A.Y., A New Fuzzy
A2 (0.07, 0.21, 0.43)
Multiple Attributive Group Decision Making
A3 (0.03, 0.12, 0.30)
Methodology and Its Application to Propulsion/Maneuvering System Selection
Table 8. The Distance Measurement
Problem, European Journal of Operation
d i Research , Vol. 166, pp. 93–114, 2005 A1 5.36 3.19 [5] Chuu, S. J., Group Decision-making Model A2 3.77 4.84 Using Fuzzy Multiple attributes Analysis for A3 4.10 4.53 the Evaluation of Advanced Manufacturing
Technology, Fuzzy Sets and Systems, Vol. Finally, according to closeness coefficient, the
160 (5), pp. 586–602, 2009 ranking order of the three alternatives can be
[6] A teş, N.Y., Çevik, S., Kahraman, C., sorted as seen in table 9. It is obvious that the
Gülbay, M., Erdoğan, S. ., Multi Attribute second alternative is the best alternative because it
Performance Evaluation Using a Hierarchical has the highest closeness coefficient.
Fuzzy TOPSIS Method, Fuzzy Applications in Industrial Engineering , Springer, Berlin
Table 9. The Closeness Coefficient
Heidelberg, 2006
CC Ranking
[7] Hwang, C. L., Yoon, K., Multiple Attribute A1 0.37 3 Decision Making Methods and Application , A2 0.56 1 Springer, Berlin Heidelberg, 1981
A3 0.52 2 [8] Chen, C. T., Extensions of the TOPSIS for Group Decision-making under Fuzzy
Conclusions
Environment, Fuzzy Sets and Systems Vol. 114 (1), pp. 1–9, 2000
This paper present the use of FMADM as the aggregation method of the performance ratings with respect to all criteria for each alternative and TOPSIS as the ranking method of alternatives according to the overall aggregated performance ratings in a new stretcher concept selection. Based on the applied methods, the second concept alternative is selected as the best result.
By applying the fuzzy multiple attribute group decision making in the new stretcher concept selection, the concept selection process is more effective and objective.
CERTIFICATE
This is to certify that
The Jaya Suteja
The th 6 International Conference on Numeriral Analysis ia � l8lt
Has succesfully participated in
as
Keynote Speaker
This event was held at
The JAYAKARTA Lombok Hotel,
May 15 .. -16 th, 2009 Lombok Island, Mai.ram City, West Nusa Tcagpra Paoriaa: - � Local Organizing Committee
PROF. DR. BUSTAMI SYAM
Chainnan
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