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1
Decision Support:
Multi-Attribute Methods
Jožef Stefan International Postgraduate School, Ljubljana
Programme: Information and Communication Technologies [ICT3]
Course Web Page: http://kt.ijs.si/MarkoBohanec/DS/DS.html
Marko Bohanec
Institut Jožef Stefan, Department of Knowledge Technologies, Ljubljana
and
University of Nova Gorica
Decision Analysis
Part 4:
Multi-Attribute Models
Motivation for Multi-Attribute Modeling
So far we have considered single-objective models,
but most of real-life decisions are multiple-objective:
e.g.: price + performance (conflicting)
Influence diagrams facilitate multi-objective modeling to some
degree. However, more is needed in terms of model
development and analysis of decisions. Thus, specialised
models and software.
Multi-attribute modeling is very useful and practical.
ID’s and Multiple ObjectivesVenture
succeeds
or fails
Invest?
Return on
investment
Overall
Satisfaction
Computer
Industry
Growth
Maximize Overall Satisfaction
Return on
InvestmentInvest in
Computer Industry
Questions
1. Have you ever encountered a:– multi-objective decision problem?– multi-attribute model?
When, where, for what kind of problems?
2. Compare multi-attribute models with:– decision trees– influence diagrams
3. Suggest types of decision problems suitable for the application of multi-attribute models
Marko Bohanec
Evaluation Models
EVALUATION
MODEL
options
EVALUATION
ANALYSIS
2
Marko Bohanec
Multi-Attribute Models
cars
CAR
PRICE
TECH
COMF
lug
pers
doors
safety
maint
buying
problem decomposition
Marko Bohanec
Multi-Attribute Models
1
2
3
4
5
digital
photo camera
COMP
functions
multi-attribute model
quality
price
sensor
weight
size
PHOTO
DIM TECH
evaluation
of cameras
body
lens
Marko Bohanec
Multi-Attribute Modelling: Why?• Systematic, structured approach (to difficult real-life problems)• Model development:
– problem decomposition into smaller, less-complex subproblems– requires understanding and careful elaboration of the problem– facilitates and motivates communication and knowledge interchange
• Evaluation:– selection of a single option– option ranking
• Analysis:– “what-if” analysis– sensitivity analysis– explanation:
• how? (evaluation procedure)• why? (selective explanation of advantages/disadvantages)
– option generation
• Contributes to better decisions:– understanding, justification, explanation, documentation
Marko Bohanec
Multi-Attribute Model Structure
a1 a2 a3a1 a2 a3a1 a2 an
X1 X2 Xn
F(X1,X2,…,Xn)
Y
ATTRIBUTES
VALUE
FUNCTION
VALUE
(UTILITY)
OPTIONS
Marko Bohanec
Multi-Attribute Model for Car Selection
a1 a2 a3a1 a2 a3a1 a2 anCARS
PRICE FUEL SAFETY
F(X1,X2,…,Xn)
CAR
ATTRIBUTES
VALUE
FUNCTION
VALUE
Marko Bohanec
Quantitative
Multi-Attribute Model for Car Selection
VALUE FUNCTION
PRICE FUEL SAFETY
CAR
1
0
50×P1+20×P2 +30×P3
OPTIONS VALUE
1. 22.000 8 6
2. 26.000 6 9
3. 19.000 7 8
63
72
88
3
Marko Bohanec
Qualitative
Multi-Attribute Model for Car Selection
OPTIONS VALUE
VALUE FUNCTION (DECISION RULES)
PRICE FUEL SAFETY
high high unacc unacc
low low unacc unacc
high low good unacc
med high good unacc
med med acc acc
med med good acc
low low acc acc
low med good good
med low good good
low low good good
CAR
1. med high acc
2. high low good
3. low med acc
unacc
unacc
acc
Marko Bohanec
Hierarchical Multi-Attribute Model
X1 X2 X3 X4 X5
X6
Y
F(X1,X2,X3)
F(X6,X4,X5)
a1 a2 a3 a4 a5
basic attributes
alternatives
aggregate attributes
utility function
utility function
utility value (utility)
value function
aggregate attributes
value function
basic attributes
alternative(option)
Multi-Attribute Modelling: Why?• Systematic, structured approach (to difficult real-life problems)• Model development:
– problem decomposition into smaller, less-complex subproblems– requires understanding and careful elaboration of the problem– facilitates and motivates communication and knowledge interchange
• Evaluation:– selection of a single option– option ranking
• Analysis:– “what-if” analysis– sensitivity analysis– explanation:
• how? (evaluation procedure)• why? (selective explanation of advantages/disadvantages)
– option generation
• Contributes to better decisions:– understanding, justification, explanation, documentation
Multi-Attribute Modelling: How?
0. Problem identification
1. Tree (or hierarchy) of attributes
2. Utility functions
3. Evaluation and analysis of alternatives
4+ Implementation
1. Tree of Attributes
Decomposition of the problem to to sub-problems ("Divide and Conquer!")
MAINTENBUYING COMFORTSAFETY
The most difficult stage!
TECH.CHAR.PRICE
CAR
2. Utility Functions (Aggregation)
Aggregation: bottom-up aggregation of attributes’ values
MAINTENBUYING COMFORTSAFETY
TECH.CHAR.PRICE
CAR
75% 25%
SAFETY COMFORT TECH.CH.
low exc unacc
high low unacc
med accept accept
high good exc
4
3. Evaluation and Analysis
• direction: bottom-up
(terminal ⇒root attributes)
• result: each option evaluated
• inacurate/uncertain data?
EVALUATION
CAR
PRICE
TECH
COMF
lug
pers
doors
safety
maint
buying
CAR
PRICE
TECH
COMF
lug
pers
doors
safety
maint
buying
3. Evaluation and Analysis
• interactive inspection
• “what-if” analysis
• sensitivity analysis
• explanation
ANALYSIS
CAR
PRICE
TECH
COMF
lug
pers
doors
safety
maint
buying
CAR
PRICE
TECH
COMF
lug
pers
doors
safety
maint
buying
MADM Tools
1. “Paper and Pencil” (Abacon)
2. Spreadsheets and mathematical modelling
software(MS Excel)
3. Specialized MADM software
Spreadsheet Modelling
Specialized Software (1/4)
Logical Decisionshttp://www.logicaldecisions.com/
Criterium DecisionPlushttp://www.infoharvest.com/
WinPrehttp://www.hut.fi/Units/SAL/
Downloadables/winpre.html
Specialized Software (2/4)
Expert Choice http://www.expertchoice.com/
HiViewhttp://www.catalyze.co.uk/products/hiview
5
Web-HIPRE http://www.hipre.hut.fi/
Specialized Software (3/4) Specialized Software (4/4)
DEXihttp://kt.ijs.si/MarkoBohanec/dexi.html
Vredana
DEX
Exercise
You would like to buy a new laptop computer for your
own purposes (study, internet, fun, ...).
Suggest a suitable set of attributes and
create a tree of attributes.
Consider the guidelines presented on the next two
slides.
Developing Attribute Structure
Three basic strategies:
• Top-Down: Start with the overall evaluation (target
objective), decompose it to sub-goals.
• Bottom-Up: Start with desirable characteristics, sub-
goals. Group them into connected, meaningful sub-trees.
• Middle-Out: Combining the two above. Iteratively
decompose (refine) and group (generalise) attributes.
Developing Attribute Structure
Desirable features of attributes and their structure:
• Completeness: Do not overlook important attributes• Relevance (non-redundancy): Use only relevant
attributes, omit redundant attributes• Minimality: Use a minimal number of attributes• Orthogonality: Basic attributes should be independent of
each other• Operativity: Basic attributes should be easy to assess or
measure• Comprehensibility: Create meaningful sub-trees of inter-
related attributes
Marko Bohanec
Working Example
One Thursday morning, Charles, instead of attending his
Management Science Techniques for Consultants class, was mulling
over his four job offers. His offers came from: Acme Manufacturing,
Bankers Bank, Creative Consulting, and Dynamic Decision Making.
He knew that factors such as location, salary, amount of
management science (which he loved), and long term prospects were
important to him, but he wanted some way to formalize the relative
importance, and some way to evaluate each job offer.
Adapted from: Michael A. Trick, Analytic Hierarchy Process, http://mat.gsia.cmu.edu/mstc/multiple/node4.html
6
Marko Bohanec
Kepner-Tregoe
Kepner, C. H., Tregoe, B. B. (1981).
The New Rational Manager. Princeton Research Press.
Characteristics:
• list of attributes
• importance of attributes is expressed by weights∈[0,10]
• alternatives are described by vectors of values∈[0,10]
• evaluation (aggregation) principle: weighted sum
• supported analyses: what-if, sensitivity
Marko Bohanec
Kepner-Tregoe Model
Job Offers
Method: Kepner-Tregoe
alternative
attribute weight value w*v value w*v value w*v value w*v
location 2 4 8 6 12 9 18 1 2
salary 10 1 10 9 90 6 60 4 40
management science 5 4 20 1 5 7 35 6 30
long-term prospects 3 10 30 1 3 6 18 4 12
total 68 110 131 84
A B C D
Marko Bohanec
Kepner-Tregoe: What-If Analysis
Job Offers
Method: Kepner-Tregoe
alternative
attribute weight value w*v value w*v value w*v
location 2 9 18 9 18 9 18
salary 10 6 60 4 40 5 50
management science 5 7 35 6 30 8 40
long-term prospects 3 6 18 6 18 7 21
total 131 106 129
C original C salary- C2
Marko Bohanec
Kepner-Tregoe Model: ChartsWeights
0 2 4 6 8 10 12
location
salary
management
science
long-term
prospects
Attributes
Weights
location; 2
salary; 10
management
science; 5
long-term
prospects; 3
Va lues
0
20
40
60
80
100
120
140
A B
Stanovanje
Value
Attribute Va lues
0 20 40 60 80 100
location
salary
management science
long-term prospec ts
Attribute
V alue
A B C D
Marko Bohanec
Kepner-Tregoe: Sensitivity Analysis
Sensitivity analysis
0
20
40
60
80
100
120
140
0 2 4 6 8 10
Weight of sa lary
Value
A
B
C
D
Marko Bohanec
AHP
AHP: Analytic Hierarchy Process (Thomas Saaty, 1980)
Characteristics:
• based on multiple attribute hierarchies
• assessing weights by a pairwise comparison of attributes
• assessing preferences by a pairwise comparison of alternatives
• consistency analysis
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Marko Bohanec
Hierarchy of Attributes
salarylocation
JOB
longMS
Bankers Creative DynamicAcme
VALUE
(BASIC)
ATTRIBUTES
ALTERNATIVES
WEIGHTS
PREFERENCES
Marko Bohanec
Pairwise Comparison Values
1 Items i and j are of equal importance (preference)
3 Item i is weakly more important (better) than j
5 Item i is strongly more important (better) than j
7 Item i is very strongly more important (better) than j
9 Item i is absolutely more important (better) than j
2,4,6,8 are intermediate values
Marko Bohanec
Assessing Weights
1. Normalize the columns so that the sum equals 1
2. Take the average of rows.
Marko Bohanec
Assessing Preferences (Scores)
For each attribute, e.g., Location, compare alternatives:
1. Normalize the columns so that the sum equals 1
2. Take the average of rows.
Marko Bohanec
Assessing Preferences (Scores)
Scores for all the attributes:
Evaluation:
Acme:
Banks:
Creative: .335
Dynamic: .238
Marko Bohanec
AHP SoftwareCriterium DecisionPlushttp://www.infoharvest.com/
WinPrehttp://www.hut.fi/Units/SAL/
Downloadables/winpre.html
Expert Choice http://www.expertchoice.com/
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Marko Bohanec
Web-HIPRE http://www.hipre.hut.fi/
Web-HIPRE Software
Marko Bohanec
D-SIGHThttp://www.d-sight.com/
Marko Bohanec
Multicriteria Modeling SoftwareProgram Metoda URL
1000Minds PAPRIKA http://www.1000minds.com
Criterium DecisionPlus AHP, SMART http://www.infoharvest.com/
Decision Deck MCDA http://www.decision-deck.org
Decision Lab PROMETHEE & GAIA http://homepages.ulb.ac.be/~bmaresc/decision_lab.htm
DecisionPad MCDA http://decisionpad.com/
D-SIGHT PROMETHEE http://www.d-sight.com/
Expert Choice AHP http://www.expertchoice.com/
GMAA MCDA http://www.dia.fi.upm.es/~ajimenez/GMAA
HIPRE AHP, SMART http://sal.aalto.fi/en/resources/downloadables/hipre3
Hiview MAUT http://www.catalyze.co.uk/index.php/software/hiview3/
Logical Decisions MCDA http://www.logicaldecisions.com/
MakeItRational AHP http://makeitrational.com/analytic-hierarchy-process/ahp-software
M-MACBETH MACBETH http://www.m-macbeth.com
V.I.S.A MCDA http://www.visadecisions.com
Visual PROMETHEE PROMETHEE & GAIA http://www.promethee-gaia.net/software.html
Visual UTA UTA http://idss.cs.put.poznan.pl/site/visualuta.html
Web-HIPRE SMART..AHP http://www.hipre.hut.fi/
Winpre AHP http://sal.aalto.fi/en/resources/downloadables/winpre
Homework
1. Run Web-HIPRE2. Load one of the existing models (e.g., Cellular Phone)3. Look at all Web-HIPRE’s features for
• describing alternatives• assessing alternatives’ preferences and scores• assessing attributes’ weights
4. Do the following:• evaluation of alternatives• sensitivity analysis
5. Try to make some changes to the model:structure, preferences, weights(but no need to save)
Marko Bohanec
DEX:
Expert System Shell for
Multi-Attribute Decision Making
DEXi:
“DEX for Education”
Computer Program for
Multi-Attribute Decision Making
1987−1995, DOS
1999→Windows
What is DEX?
Originates in 1980’s
DEX
Multi-Criteria Decision Analysis
•modeling using criteria and utility
functions
•problem decomposition and
structuring
•evaluation and analysis of
decision alternatives
Multi-Criteria Decision Analysis
•modeling using criteria and utility
functions
•problem decomposition and
structuring
•evaluation and analysis of
decision alternatives
Artificial Intelligence
Expert Systems
•qualitative (symbolic) variables
•"if-then" rules
•decision model = knowledge base
•handling imprecision and uncertainty
•transparent models, explanation
Machine Learning
Artificial Intelligence
Expert Systems
•qualitative (symbolic) variables
•"if-then" rules
•decision model = knowledge base
•handling imprecision and uncertainty
•transparent models, explanation
Machine Learning
Fuzzy sets
•verbal measures
•fuzzy operators
Fuzzy sets
•verbal measures
•fuzzy operators
9
DEX
Method for qualitative multi-attribute modeling
DEX is similar to other multi-attribute methods:
1. Multiple attributes, hierarchically structured
2. Evaluation of alternatives: bottom-up aggregation
CAR
TECH.CH.PRICE
COMFORTSAFETYMAINTBUYING FUEL
Some CarSome Car
DEX
Method for qualitative multi-attribute modeling
DEX is different from other multi-attribute methods:
1. Attributes are discrete, symbolic, qualitative
CAR
TECH.CH.PRICE
COMFORTSAFETYMAINTBUYING FUEL
Numeric (quantitative):
BUYING = 12.233 €
numeric scale, e.g. R+
Symbolic (qualitative):
BUYING = medium
scale: {high, medium, low}
DEX
Method for qualitative multi-attribute modeling
DEX is different from other multi-attribute methods:
2. Evaluation of alternatives (aggregation) is defined by decision rules
CAR
TECH.CH.PRICE
COMFORTSAFETYMAINTBUYING FUEL
PRICE = 75%*BUYING + 25%*MAINTPRICE = 75%*BUYING + 25%*MAINT
75%75% 25%25%
DEX
Method for qualitative multi-attribute modeling
DEX is different from other multi-attribute methods:
2. Evaluation of alternatives (aggregation) is defined by decision rules
CAR
TECH.CH.PRICE
COMFORTSAFETY
MAINTBUYING
FUEL
PRICE = 75%*BUYING + 25%*MAINTPRICE = 75%*BUYING + 25%*MAINT
75%75% 25%25%FUEL SAFETY COMFORT TECH.CH.
high good exc unacc
low bad med unacc
... ... ... ...
med good med good
DEX History
1980 20001990 2010
Methodology
•initial development
Software
•DECMAK
•“toolbag”
First applications
•HW and SW selection
•personnel mgmt
•nursery schools
DECMAK
Methodology
•integration
Software
•DEX
•Vredana
National applications
•Housing Fund
•Ministry Sci-Tech
•Talent System
•industry
•medicine
Related
•HINT
Methodology
•further improvement
Software
•DEXi
Education
International applications
•Sol-Eu-Net
•agronomy, GMO
•project evaluation
•finance
Related
•model revision, proDEX
DEX DEXi
Marko Bohanec
DEXi
A simple computer program for MADM that facilitates:
• Creation and editing of– model structure (tree of attributes)
– value scales of attributes
– decision rules (incl. using weights)
– options and their descriptions (data)
• Evaluation of options (can handle missing values)
• Presentation of evaluation results with:– tables
– charts
• Analyses: “what-if”, “±1”, selective explanation, comparison
• Preparing reports and charts
Computer Program for
Multi-Attribute Decision Making
10
Marko Bohanec
DEXi Model Attribute Scale Job unacc; acc; good; exclocation unacc; acc; goodsalary unacc; acc; goodsatisfaction unacc; acc; goodMS unacc; acc; goodlong unacc; acc; good
Tables location salary satisfaction Job 33% 33% 33% 1 unacc * * unacc2 * unacc * unacc3 * * unacc unacc4 acc acc >=acc acc5 acc >=acc acc acc6 >=acc acc acc acc7 acc good good good8 good acc good good9 good good acc good10 good good good exc MS long satisfaction 50% 50% 1 unacc * unacc2 * unacc unacc3 acc acc acc4 >=acc good good5 good >=acc good
Marko Bohanec
DEXi Evaluation
Evaluation results Attribute A B C D Job unacc unacc acc unacclocation acc acc good unaccsalary unacc good acc acc
satisfaction good unacc acc goodMS acc unacc acc good
long good unacc acc acc
location
satisfactionsalary
location
acc
acc
B
location
satisfactionsalary
location
acc
acc
location
satisfactionsalary
location
acc acc
acc acc
D
location
satisfactionsalary
location
acc
acc
Marko Bohanec
Stages of MADM with DEXi
0. Problem Identificationa. problem formulation
b. formation of a decision-making group
c. selection of decision-support methodology
1. Identification of Attributes
a. unstructured list of attributes
b. hierarchy (tree) of attributes
c. measurement scales
2. Definition of Utility Functions (Decision Rules)
3. Evaluation and Analysis of Options
a. description of options (data acquisition)
b. evaluation of options
c. analysis
4. Implementation
Stages of MADM (with DEXi)
0. Problem Identificationa. problem formulation
b. formation of a decision-making group
c. selection of decision-support methodology
1. Identification of Attributes
a. unstructured list of attributes
b. hierarchy (tree) of attributes
c. measurement scales
2. Definition of Utility Functions (Decision Rules)
3. Evaluation and Analysis of Options
a. description of options (data acquisition)
b. evaluation of options
c. analysis
4. Implementation
knowledge acquisition
DECOMPOSITION
AGGREGATION
EXPLOITATION
1.a: Unstructured List of Attributes
Problem in Personnel Management:
Select of a Candidate for a Job (e.g., a project manager)
• education
• age
• experience
• references
• knowledge
• work approach
• ability to work in a group
• leadership
• organizational abilities
• loyalty
• intelligence
• communicativity
• character
• health
• …
Do not overlook important attributes!
11
1.b: Tree of Attributes
Create meaningful, related groups
Avoid aggregate attributes having more than three descendants
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
1.b: Tree of Attributes
1.c: Scales
Scales are discrete, typically ordered from bad to good
Values should distinguish between importantly different characteristics
Their number should gradually increase from bottom to the root
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
unacc, acc, good, exc
unacc, acc, good unacc, acc, good
unacc, acc, good
prim/sec,
high,
univ,
MSc,
PhD
no, pas, act none,
to1year,
1-5,
6-8,
more
18-20,
21-25,
26-40,
41-55,
more
D, C, B, A
poor, aver,
good, exc
less, approp, more
1.c: Scales
2: Decision rules
Personal
Abilit
Comm Leader
Test
Utility Functions, Bottom-Up Aggregation
Comm Leader Abilit
poor less unacc
poor approp unacc
poor more unacc
aver less unacc
aver approp acc
aver more acc
good less unacc
good approp acc
good more good
exc less unacc
exc approp good
exc more good
2: Decision rules
12
3.a: Description of Options
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
Candidate Formal For.Lang Exper Age Comm Leader Test
A MSc pas to1year 21-25 good more B
B PhD act more 26-40 aver less B
3.a: Description of Options
3.bc: Evaluation and Analysis of Options
1. Evaluation• proceeds from bottom (basic attributes) to the root• result: qualitative evaluation of each option• handles missing (DEXi) or imprecise (DEX) option values
2. Analysis• interactive inspection of results• what-if analysis• analyses:
• compare options• “±1” analysis• selective explanation
• reports• charts
3.b: Evaluation of an Option
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
good
acc goodacc
good
MSc pas to1year 21-25 B
good more
Candidate A
3.b: Evaluation of Options 3.c: What-If Analysis
MScMSc
⇓⇓⇓⇓
PhD
⇐⇐⇐⇐ no effectEmploy
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
good
acc goodacc
good
pas to1year 21-25 B
good more
Candidate A
13
3.c: What-If Analysis
good ⇒⇒⇒⇒ exc
acc ⇒⇒⇒⇒ good
pas
⇓⇓⇓⇓
act
MSc
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
good
acc
goodacc
good
pas to1year 21-25 B
good more
Candidate A
3.c: What-If Analysis
3.c: “±1” Analysis 3.c: Compare options
3.c: Selective Explanation
Employ
Educat Years Personal
Formal For.Lang Exper Age Abilit
Comm Leader
Test
unacc
good unaccgood
PhD act more 26-40 B
aver less
unacc
Candidate B
3.c: Selective Explanation
14
Charts and Reports DEX and DEXi: Experience
• Wide applicability to various application areas
• Usually, solutions are specific (non-general)
1. Model development time• heavily problem-dependent: from hours to months
• typical: 2 to 15 days
2. The most difficult stage• designing the tree of attributes
3. Appropriate decision problems• many attributes (> 15)
• many options (> 10)
• prevailing qualitative decision-making, judgment
• inaccurate or missing data
• group decision making (communication and explanation)
• sufficient resources available (expertise, time)
DEX in DEXi: Future
• Combined qualitative and quantitative models
• Extensions:
• Data Mining (e.g. machine learning of models by HINT)
• Data Bases, Data Warehouses, OLAP
• Software:
• “Dex Machine”: Low-level OO library for QQ models
• Various types and levels of GUI
DEX and DEXi: Summary
1. Combination of
multi-attribute decision making and expert systems
2. Characteristics:
• qualitative (symbolic) decision making
• explanation and analysis
• active support in the acquisition of decision rules
3. Applicability:
• for complex real-world problems
• over 50 real-life applications
Exercise
1. Take one of the already defined “empty” models
shown on the next slide
2. Define all utility functions (decision rules) in that model
3. Define and describe a few (about 4) options
4. Evaluate and analyse the options
5. Extend the model:
• add and/or refine a few attributes
(including their scales and rules)
• repeat the steps 2, and 4.
6. Prepare and print out (or save) a report
ModelsProgrammer’s Performance Attribute Description
PROGRAMMER Programmer's PerformanceKNOWLEDGE Knowledge of the Programmer
EXPER Working ExperienceSPECIAL Specialized Knowledge
WORK Quality of Programmer's WorkQUALITY Quality of the ResultsEFFECTIVE Work Effectivenes: Are the results delivered in time?
APPROACH Working Approach
TEAM Attitude to Team WorkPERSONAL Personal Characteristics
INITIATIVE Self InitiativenessCREATIVE Creativity
Attribute Description
PROGRAMMER Programmer's PerformanceKNOWLEDGE Knowledge of the Programmer
EXPER Working ExperienceSPECIAL Specialized Knowledge
WORK Quality of Programmer's WorkQUALITY Quality of the ResultsEFFECTIVE Work Effectivenes: Are the results delivered in time?
APPROACH Working Approach
TEAM Attitude to Team WorkPERSONAL Personal Characteristics
INITIATIVE Self InitiativenessCREATIVE Creativity
Attribute Description
PORTABLE Portable computerCOMMERCIAL
PRICE [ in Euro ]IMAGE
TECHNICALINTERNAL
PROCESSORMEMORYDISK
EXTERNAL
MONITORKEYBOARD
AUTONOMY
Attribute Description
PORTABLE Portable computerCOMMERCIAL
PRICE [ in Euro ]IMAGE
TECHNICALINTERNAL
PROCESSORMEMORYDISK
EXTERNAL
MONITORKEYBOARD
AUTONOMY
Portable Computer
Attribute Description
ENTERP Performance evaluation of enterprisesFINANC
RETURN
PROFITPROF-AB
LIQUIDECONOMIC
PRODUCTCAPACITY
Attribute Description
ENTERP Performance evaluation of enterprisesFINANC
RETURN
PROFITPROF-AB
LIQUIDECONOMIC
PRODUCTCAPACITY
Performance Evaluation of Companies Attribute Description
CAR Quality of a car
PRICE Price of a car
BUY.PRICE Buying priceMAINT.PRICE Maintenance price
TECH.CHAR. Technical characteristicsCOMFORT Comfort
#PERS Maximum number of passengers#DOORS Number of doorsLUGGAGE Size of the luggage boot
SAFETY Car's safety
Attribute Description
CAR Quality of a car
PRICE Price of a car
BUY.PRICE Buying priceMAINT.PRICE Maintenance price
TECH.CHAR. Technical characteristicsCOMFORT Comfort
#PERS Maximum number of passengers#DOORS Number of doorsLUGGAGE Size of the luggage boot
SAFETY Car's safety
Car Selection
Also available: Employ