04/01/20031 project and product selection by he jiang department of management university of utah...
Post on 21-Dec-2015
215 views
TRANSCRIPT
![Page 1: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/1.jpg)
04/01/2003 1
Project and Product Selection
by He Jiang
Department of ManagementUniversity of Utah
April 1st, 2003
![Page 2: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/2.jpg)
04/01/2003 2
Outline
• On Integrating Catalogs
• A Hierarchical Constraint Satisfaction Approach to Product Selection for Electronic Shopping Support
• A Multiple Attribute Utility Theory Approach to Ranking and Selection
![Page 3: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/3.jpg)
04/01/2003 3
On Integrating Catalogs
Rakesh Agrawal and Ramakrishnan Srikant
IBM Almaden Research Center
![Page 4: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/4.jpg)
04/01/2003 4
Summary
• Problem: integrating documents from different sources into a master catalog.
• Gaps: Many data sources have their own categorizations; implicit similarity information in these source catalogs may be ignored.
• Approaches: Naïve Bayes classification• Contribution: classification accuracy can be
improved by incorporate the implicit similarity information present in these source categorizations
![Page 5: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/5.jpg)
04/01/2003 5
Problem—Why Integration?
• B2C shops need to integrate catalogs from multiple vendors ( Amazon);
• B2B portals merged into one company (Chipcenter & Questlink eChips);
• Information portals categorize documents into categories (Google & Yahoo!).
• Corporate portals Merge intra-company and external information into a uniform categorization
![Page 6: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/6.jpg)
04/01/2003 6
Problem Identification—Model Building
• Problem identification: classification problem.
• Master catalog M with categories C1, C2, …, Cn;
• Source catalog N with categories S1, S2, …, Sm;
• Merge documents in N into M.
![Page 7: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/7.jpg)
04/01/2003 7
Question
How to Integrate?
![Page 8: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/8.jpg)
04/01/2003 8
Straightforward Approach:• Completely ignore N’s categorization, put each of N’s
product into M’s category according to M’s classification rule.
![Page 9: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/9.jpg)
04/01/2003 9
Enhanced Approach
• incorporate the implicit categorization information present in N into M.
![Page 10: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/10.jpg)
04/01/2003 10
Assumptions and Limitations
• M and N may are homogeneous and have significant overlap;
• M and N use the same vocabularies (Larkey, 1999).
• Catalog hierarchies is flattened and is treated as a set of categories(Good 1965 & Chakrabarti 1997)
• Different hierarchy levels (if M>N, can help distinguish categories that M doesn’t have; if N>M, NBHC can be applied.
![Page 11: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/11.jpg)
04/01/2003 11
Related Works and Gaps
• Naïve-Bayes classifiers are accurate and fast(Chakrabarti et al 1997, …), so we choose Bayesian model;
• Folder systems such as email routing(Agrawal et al, 2000,…), action predicting(Maes, 1994 & Payne et al, 1997), query organizing using text clustering(Sahami et al, 1998) and filings transferring(Dolin et al 1999); But none of this systems address the task of merging hierarchies
• The Athena system includes the facility of reorganizing folder hierarchy into a new hierarchy (Agrawal et al, 2000); But no information from the old hierarchy is used in either building the model or routing the documents.
![Page 12: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/12.jpg)
04/01/2003 12
Straightforward Approach
othereach oft independen are din words:Assumption
)|Pr()|Pr(•
dataset in the documents ofnumber Total
CCategory in documents ofNumber )Pr(•
)Pr(
)|Pr()Pr()|Pr(
i
dt
ii
i
iii
CtCd
C
d
CdCdC
:ddocument agiven
Ccategory ofy probabilitPosterior :classifier Bayes Naïve i
![Page 13: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/13.jpg)
04/01/2003 13
t ii
i
i
ii
tCnCn
tCn
VCn
tCnCt
),()(
y. vocabular theof size theis |V|
Cicategory in documents
in t wordof soccurrence ofnumber the—),(
0 ,||)(
),()|Pr(
Straightforward Approach—Continued
![Page 14: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/14.jpg)
04/01/2003 14
Enhanced Bayes Classification
M.in Ccategory in documents ofnumber theis |C| where
) )Cin be topredicted Sin docs of(Number |C(|
)Cin be topredicated Sin documents ofNumber (|C|
Sin documents ofnumber Total
Cin be topredicated Sin documents ofNumber )|Pr(
Nin category a denotes S where
)|Pr(
)|Pr()|Pr(),|Pr(
ii
j
n
1j j
ii
i
SC
Sd
CdSCSdC
i
iii
![Page 15: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/15.jpg)
04/01/2003 15
Effect of Weight on Accuracy
• Weight can make difference for a given M and N; Tune set method to select a good value for the weight.
•
•
in which the document will be correctly classified or will never be correctly classified
• The highest possible accuracy achievable with the enhanced algorithm is no worse than what can be achieved with the basic algorithm.
ff then , if 0, , weight ofpair given any For :1 Theorem21 xx2121
), ,( interval aexist thered,document each For :2 Theorem 21
![Page 16: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/16.jpg)
04/01/2003 16
Experimental Results—Data Sets Used
• Synthetic catalog: deriving source catalog N from M using different distributions(e.g. Gaussian).
• Real Catalog: two real-world catalogs that have some common documents; treat the first catalog minus the common documents as M, the remaining documents in the second catalog as N;
![Page 17: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/17.jpg)
04/01/2003 17
Experimental Results
![Page 18: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/18.jpg)
04/01/2003 18
Experimental Results
![Page 19: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/19.jpg)
04/01/2003 19
Experimental Results
![Page 20: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/20.jpg)
04/01/2003 20
Experimental Results—Catalog Size
![Page 21: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/21.jpg)
04/01/2003 21
Experimental Results—Catalog Size
![Page 22: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/22.jpg)
04/01/2003 22
Contributions and Future Research Directions
• Contributions: enhancing the standard Naive Bayes classification by incorporating the category information of the source catalogs; the highest accuracy of the enhanced technique can be no worse than that can be achieved by standard Naïve Bayes classification.
• Future research: using other classifiers such as SVM to incorporating the implicit information of N requires further work
![Page 23: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/23.jpg)
04/01/2003 23
A Hierarchical Constraint Satisfaction Approach to Product Selection for
Electronic Shopping Support
Young U. Ryu
IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and humans
Vol. 29, No. 6, November 1999
![Page 24: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/24.jpg)
04/01/2003 24
Summary
• Problem: proposing a product selection mechanism for electronic shopping support;
• Approach: hierarchical constraint satisfaction (HCS) approach
• Gap: simple taxonomy hierarchy(STH) approach is flawed in that the the search is conducted on a single generic product hierarchy;
• HCS is more powerful and flexible than STH.
![Page 25: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/25.jpg)
04/01/2003 25
Simple taxonomy Hierarchy Approach
![Page 26: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/26.jpg)
04/01/2003 26
Question
• 1. How do we search for a sugar-free decaffeinated cola?
• 2. If there isn’t a cola that satisfy all the requirements, i.e., cola, sugar-free and decaffeinated. what’s your recommendation?
![Page 27: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/27.jpg)
04/01/2003 27
Gaps
• Search is conducted on a single generic product hierarchy;
• There may exist a product that cannot satisfy all the constraints;
• A product may be evaluated to be better than another while there is no big differences between these two products.
![Page 28: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/28.jpg)
04/01/2003 28
Hierarchical Constraint Satisfaction Approach
• Constraint Satisfaction: a methodology determining assignments of values to variables that are consistent with given constraint;
• Hierarchical Constraint Satisfaction: an extension of STH which minimizes the the satisfaction errors of hierarchically organized constraints based on their importance;
• Value of HCS: can be applied to cases in which there isn’t a solution that is consistent with given constraints due to conflicting constraints.
![Page 29: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/29.jpg)
04/01/2003 29
Concepts Introduced
• Constraint domain transformation: transformation of a Boolean constraint to a arithmetic constraint;
• Tree domain: is one whose elements are structured as a tree; thus can be handled more flexibly;
• Indifference interval: overcome a shortcoming of hierarchical reasoning when the difference between two alternatives is small;
![Page 30: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/30.jpg)
04/01/2003 30
Constraint Satisfaction Error
• Measures the degree of satisfaction of an arithmetic constrain c by the constraint satisfaction error function
• for Boolean constraint, transform them into arithmetic constraints;
• e.g.
),( ic
......20045
1805510545
,
20045
18055
320
,
),,(
3
2
1
3
2
1
321
x
xx
ifgreen
x
x
x
ifred
xxxf
![Page 31: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/31.jpg)
04/01/2003 31
Hierarchical reasoning and indifference interval
ijevery for ),'(c)(c
and ),'(c)(c
n,i somefor if
'product another n better tha is product A
j, j,
j, i,
xx
ijevery for ,|)'(c)(c|
and ,)(c)'(c
:ionconsiderat into interval ceindifferen take
j, j,
i, i,
j
i
i
c
c
c
![Page 32: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/32.jpg)
04/01/2003 32
Constraint Hierarchies
iijijiji
Ccijiji
h
h
CcxcwxC
xcwxC
cc
CH
iij
:),(max),(or
),(),(
iserror on satisfacti sconstraint aggregatedthen
c'.than important more is c constraint that means '
Con relation orderingk strict wea a is
sconstraint ofset a is C where
,pair a ishierarchy constraintA
h
![Page 33: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/33.jpg)
04/01/2003 33
Example
• Shopping for wipes products using hierarchical constraint satisfaction approach. Each product is described by the following attributes:
• Cost: cents per sheet
• Add-on materials: “baking soda”, “aloe vera”, …;
• Strength: measured by pressure(psi) that breaks a sheet;
• Dispenser type: “box”, “pop-up”;
• Added artificial scent: unscented, natural aloe scented, natural jasmine scented and chemical perfume scented;
• Product purpose: “general purpose”, “diaper change”.
![Page 34: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/34.jpg)
04/01/2003 34
Example—Result
![Page 35: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/35.jpg)
04/01/2003 35
Contributions and Future Research Directions
• Contribution: the product search mechanism is viewed as a satisfaction problem of hierarchically organized constraints over product attributes, thus it is more powerful and flexible than product selection based on a single product taxonomy hierarchy.
• Future research: Purchasing requirement specification or constraint hierarchy elicitation; complete prototype implementation of the HCS approach; actual purchasing/sales transaction based on speech –act theory, illocutionary logic and inter-organizational activity coordination.
![Page 36: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/36.jpg)
04/01/2003 36
A Multiple Attribute Utility Theory Approach to ranking and Selection
John Butler, Douglas J. Morrice and
Peter W. Mullarkey
Management Science, Vol. 47, No. 6, June 2001
![Page 37: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/37.jpg)
04/01/2003 37
Summary• Problem: developing a ranking and selection
procedure for making comparison of systems that have multiple performance measures;
• Approach: combining Multiple Attribute Utility Theory (MAUT) and statistical ranking and selection (R&S) using indifference zone;
• Gaps: costing approach is flawed in that accurate cost data may not be available, and it may be difficult to measure performance using costs..
• Advantages: rigorous; close to business practice; simpler to implement; can estimate the number of simulations required; can assess the relative importance of criteria
![Page 38: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/38.jpg)
04/01/2003 38
Gaps
• Most of the R&S literature focused on procedures that reduce the multivariate performance measures to a scalar performances measure problem, but these procedures may have some disadvantages, e.g. accurate cost data may not be available; it maybe difficult to accurately attach a dollar value to intangible variables;
• Current techniques may require a complicated step of estimating a covariance matrix(Gupta & Panchapakesan 1979);
• Previous work doesn’t provide an approach to estimate the number of simulations required to select the best configurations with a high level of probability(Andijani 1998, Kim & Lin 1999).
• Previous work lacks a trade-off mechanism that allows the decision maker to combine disparate performance measures.
![Page 39: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/39.jpg)
04/01/2003 39
Assumptions
• Decision maker’s preferences are accurately represented ( Clemen 1991, Keeney & Raiffa 1976);
• Performance measures that is converted to “utils” can be converted to meaningful unit by choosing an invertible utility function;
• There is a indifference zone for the decision maker on all the performance measures;
![Page 40: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/40.jpg)
04/01/2003 40
General Outline of the Procedure
Construct MAU Model
Run Simulator
Assess Indifference Zone
Simulation Output Vector
Apply MAU Model and Scalar Based R&S Procedures
Sensitivity Analysis on MAU Weights
Assess Utility Functions
Assess Weights
Utility Exchange
![Page 41: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/41.jpg)
04/01/2003 41
s.preferenceon m and j i, attributes
between n interactio therepresent that constants scaling the
are wi, measurefor weight theis,10 i, measureover
functionutility sigle a is ,1(.)0 measures, eperformanc
over variablesrandom of vestor a is )...,( Where
)()...()(...
)()()(
)()()()(
ijm
21
2211...123
1
11
i
i
n
nnn
n
i ij ijmmmjjiiijm
jj
n
i ijiiij
n
iiii
w
u
XXXX
XuXuXuw
XuXuXuw
XuXuwXuwXu
Multilinear Utility Function
![Page 42: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/42.jpg)
04/01/2003 42
Multiplicative MAU Model
.w1- and ,10 Where
)()...()(...
)()()(
)()()()(
)](1[)(1
then t,independenutility mutual If
2211...123
1
11
1
i
nnn
n
i ij ijmmmjjiiijm
jj
n
i ijiiij
n
iiii
i
n
i ii
w
XuXuXuw
XuXuXuw
XuXuwXuwXu
XuwwXwu
![Page 43: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/43.jpg)
04/01/2003 43
Additive MAU Model
• If mutual utility additive independent, then
• Example for additive independence:
n
iiii XuwXu
1
)()(
0.5probility with ),(x
0.5probility with ),(x B
0.5probility with ),(x
0.5probility with ),(x
*
*
**
21
*
2*
1
2*
1*
21
x
x
x
xA
![Page 44: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/44.jpg)
04/01/2003 44
Single Attribute Utility Function Used
•
• Methods for assigning weights: trade-off method; analytical hierarchy process (AHP).
i. measurefor constants scaling are and and
ancerisk toler smaker'decision theis where
)( )(
ii
i
RTxiiii
BA
RT
eBAxu ii
![Page 45: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/45.jpg)
04/01/2003 45
Question
• What’s the benefit of using this function?
![Page 46: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/46.jpg)
04/01/2003 46
R&S Experimental Set-up
• Correct Selection (CS): the R&S procedure accurately identifies the configuration with largest expected utility .
• Two stage indifference zone procedure for R&S.
)]([ ][KXuE
10 and ,11 where
)]E[u(X-
)]E[u(X whenever }{
*
*1]-[K
[K]*
*P/K)(
PCSP
![Page 47: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/47.jpg)
04/01/2003 47
Selection of
• A Utility Exchange ApproachTable 1 Alternatives by Measures Matrix for Car Selection
Table 2 Equivalent Hypothetical Cars
*
Alternative Cost Horsepower Harmony 17,000 160 Starburst 17,000 125 Keyo 15,200 100 Palomino 18,500 160
Alternative Cost Horsepower Harmony 17,000 140 Starburst 17,000 140 Keyo 15,200 140 Palomino 18,500 160
![Page 48: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/48.jpg)
04/01/2003 48
Question Again
• Does it mean that the 20 horsepower is worth $1,200?
![Page 49: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/49.jpg)
04/01/2003 49
Selection of
*
ikiik
kk
k
cxux
Q
Qxuux
x
)( such that specified is ' where
))(
('
is ' of level computingfor expressionan
function,utility r Multilinea for the :1n Propositio •
'1
2
1111
1
function. AMUfor K, , 2, ,1k
k,ion configurat and 1 measure standard for the
))(var())(var(
:hold iprelationsh following The :2n Propositio •
22
'11
Q
XuXu K
K
![Page 50: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/50.jpg)
04/01/2003 50
Establishing the Indifference Zone
• Curve dividing the indifference and preference zone:
1
1]1[
)(ln1
*1
11]1[1][RT
CE
KK
K
eB
RTCECE
![Page 51: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/51.jpg)
04/01/2003 51
Different for ZoneceIndifferen theofzation Characteri *
![Page 52: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/52.jpg)
04/01/2003 52
• Example:
Different for ZoneceIndifferen theofzation Characteri *
![Page 53: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/53.jpg)
04/01/2003 53
Application of the Procedure—Case Description
• Case example: Land Seismic Survey;• Performance measures: survey cost; survey
duration; utilization of the four crews; • Relationship of the crews:
![Page 54: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/54.jpg)
04/01/2003 54
Application of the Procedure—Results
![Page 55: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/55.jpg)
04/01/2003 55
Application of the Procedure—Results
![Page 56: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/56.jpg)
04/01/2003 56
Application of the Procedure—Sensitivity Analysis to Weight
![Page 57: 04/01/20031 Project and Product Selection by He Jiang Department of Management University of Utah April 1 st, 2003](https://reader030.vdocuments.net/reader030/viewer/2022032521/56649d585503460f94a374e8/html5/thumbnails/57.jpg)
04/01/2003 57
Contributions and Future Research Directions
• Contribution: provides a formal procedure that can be applied to realistic problems; presents a scalar performance measure that can summarize performance on multiple criteria, including nonlinear preference functions and the relative importance of the measures;
• Future research: combine MAU theory with the work of Chen et al; extend the MAU methodology with Chick and Inoue’s work to include their Bayesian technique and relieve some of the computational burden of all R&S procedure; combine the work in this paper with R&S procedures designed facilitate variance reduction through the use of common random numbers (See Matejcik and Nelson 1995 and Goldman and Nelson 1998).