This document describes the Analytic Network Process (ANP) model for complex decision making. The ANP model includes the following key elements:
1. A top-level network with four merit nodes: benefits, opportunities, costs, and risks.
2. Subnetworks below each merit node containing control criteria hierarchies to evaluate each merit.
3. Additional subnetworks for high priority control criteria containing decision alternatives.
4. Pairwise comparisons to obtain weights for criteria, alternatives, and influences between elements. Limit matrices converge the results.
5. Sensitivity analysis identifies the best alternative for different priorities of the merit nodes like benefits, costs, and risks. The document provides an example ANP model and
Recommender systems have become an important personalization technique
on the web and are widely used especially in e-commerce applications.
However, operators of web shops and other platforms are challenged by
the large variety of available algorithms and the multitude of their
possible parameterizations. Since the quality of the recommendations that are
given can have a significant business impact, the selection
of a recommender system should be made based on well-founded evaluation
data. The literature on recommender system evaluation offers a large
variety of evaluation metrics but provides little guidance on how to choose
among them. The paper which is presented in this presentation focuses on the often neglected aspect of clearly defining the goal of an evaluation and how this goal relates to the
selection of an appropriate metric. We discuss several well-known
accuracy metrics and analyze how these reflect different evaluation goals. Furthermore we present some less well-known metrics as well as a variation of the area under the curve measure that are particularly suitable for the evaluation of
recommender systems in e-commerce applications.
Recommender systems have become an important personalization technique
on the web and are widely used especially in e-commerce applications.
However, operators of web shops and other platforms are challenged by
the large variety of available algorithms and the multitude of their
possible parameterizations. Since the quality of the recommendations that are
given can have a significant business impact, the selection
of a recommender system should be made based on well-founded evaluation
data. The literature on recommender system evaluation offers a large
variety of evaluation metrics but provides little guidance on how to choose
among them. The paper which is presented in this presentation focuses on the often neglected aspect of clearly defining the goal of an evaluation and how this goal relates to the
selection of an appropriate metric. We discuss several well-known
accuracy metrics and analyze how these reflect different evaluation goals. Furthermore we present some less well-known metrics as well as a variation of the area under the curve measure that are particularly suitable for the evaluation of
recommender systems in e-commerce applications.
Adeguamento Sismico, Master Livorno 07/03/14, Francesco PetriniFranco Bontempi
Polo Universitario Sistemi Logistici di Livorno
Master Universitario di 2° Livello: Soluzioni Innovative nell’Ingegneria Edile.
Soluzioni strutturali integrate: Adeguamento sismico.
Francesco Petrini
Pisa, 7 marzo 2014
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Using JMeter and Google Analytics for Software Performance TestingXBOSoft
Ed Curran, VP of Engineering at XBOSoft, shares some of his hands on experience in working with JMeter for load and performance testing. In the webinar, he provided explanations of different types of performance testing and how you can use Google Analytics to understand what users are really doing on your web apps and then how to leverage JMeter and analyze the results to improve your app's performance.
Adeguamento Sismico, Master Livorno 07/03/14, Francesco PetriniFranco Bontempi
Polo Universitario Sistemi Logistici di Livorno
Master Universitario di 2° Livello: Soluzioni Innovative nell’Ingegneria Edile.
Soluzioni strutturali integrate: Adeguamento sismico.
Francesco Petrini
Pisa, 7 marzo 2014
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Using JMeter and Google Analytics for Software Performance TestingXBOSoft
Ed Curran, VP of Engineering at XBOSoft, shares some of his hands on experience in working with JMeter for load and performance testing. In the webinar, he provided explanations of different types of performance testing and how you can use Google Analytics to understand what users are really doing on your web apps and then how to leverage JMeter and analyze the results to improve your app's performance.
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This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
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This work was presented at 51st AIAA/SDM conference, Apr 14, 2010 in Orlando. The work presented in this paper was performed in collaboration with Prof. Achille Messac and Dr. Ritesh Khire.
Software Engineering
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The Comprehensive Product Platform Planning (CP3) framework presents a flexible mathematical model of the platform planning process, which allows (i) the formation of sub-families of products, and (ii) the simultaneous identification and quantification of plat- form/scaling design variables. The CP3 model is founded on a generalized commonality matrix that represents the product platform plan, and yields a mixed binary-integer non- linear programming problem. In this paper, we develop a methodology to reduce the high dimensional binary integer problem to a more tractable integer problem, where the com- monality matrix is represented by a set of integer variables. Subsequently, we determine the feasible set of values for the integer variables in the case of families with 3 − 7 kinds of products. The cardinality of the feasible set is found to be orders of magnitude smaller than the total number of unique combinations of the commonality variables. In addition, we also present the development of a generalized approach to Mixed-Discrete Non-Linear Optimization (MDNLO) that can be implemented through standard non-gradient based op- timization algorithms. This MDNLO technique is expected to provide a robust and compu- tationally inexpensive optimization framework for the reduced CP3 model. The generalized approach to MDNLO uses continuous optimization as the primary search strategy, how- ever, evaluates the system model only at the feasible locations in the discrete variable space.
1. The SUPER DECISIONS Software
The Analytic Network Process for decision making with
dependence and feedback
By Creative Decisions Foundation
4922 Ellsworth Avenue
Pittsburgh, PA 15213
Phone: 412-621-6546
Fax: 412-681-4510 1
2. The Parts of a Complex ANP Model
with Benefits, Opportunities, Costs
and Risks
• Top level network with the merit nodes:
benefits, opportunities, costs and risks
• Subnets (4) beneath each merit node containing
control criteria hierarchies for each merit
• Subnets for each of the high priority control
criteria. There may be as many as 10 or 12 of
these.
2
3. Concepts Important in the ANP
1. Benefits, Opportunities, Costs and Risks
(the merits of the decision)
2. Control Criteria Hierarchies (for each
merit)
3. Decision subnets containing the
alternatives for the high priority control
criteria
4. Strategic Criteria introduced at the end of
3
the process to prioritize the BOCR nodes
5. Hierarchy of Control Criteria for each
Merit Node
• Create or access the subnet of a merit node by
right-clicking it to select Make/Show Subnet
•Control criteria subnets have a goal cluster
with a goal node, a criteria cluster with criteria
nodes and perhaps subcriteria in other clusters.
•The criteria are connected to the goal (and
subcriteria to criteria)
• Pairwise compare criteria and subcriteria, pick
high priority control criteria and build decision
5
subnets for them
10. There is a Decision Subnet for each
Benefit Control Criterion; the one for
Economic Benefits is shown below
10
11. Perform all Pairwise Comparisons, e.g.: What is most
important Operational Criterion for “Outsource All
Application Development Work”
11
12. Results of all pairwise comparisons are
arranged in unweighted supermatrix
12
13. Pairwise compare the clusters connected
from a cluster for influence on that cluster.
13
14. Multiply values in Unweighted SuperMatrix Components by corresponding Cluster Matrix Value
to get Weighted Supermatrix.
Example: All 9 numbers in (Financial, Alternatives) component of unweighted supermatrix are
multiplied by .833 from (Financial,Alternatives) cell in the Cluster matrix to yield values in
(Financial, Alternatives) component in weighted supermatrix below.
All columns then sum to 1 in weighted supermatrix.
14
=1
15. The Weighted Supermatrix converges to the Limit
Supermatrix
All columns may be the same in the limit matrix, although not always the case.
Synthesized results for alternatives come from raw values in limit supermatrix
15
16. Synthesized results in Economic
Benefits Decision Subnet
Alternatives under Economic Benefits Ideals Normals Raw
1 Outsource all application development
work 1.0000 0.6962 0.3427
2 Outsource the design and
programming phases 0.2766 0.1926 0.0948
3 Do not outsource any application
development work 0.1597 0.1112 0.0548
16
17. Synthesized results in Technological
Benefits Decision Subnet
Alternatives under Technological
Benefits Ideals Normal Raw
1 Outsource all application development
work 1.0000 0.4411 0.2189
2 Outsource the design and
programming phases 1.0000 0.4411 0.2189
3 Do not outsource any application
development work 0.2669 0.1178 0.0584
17
18. Combining Economic and
Technological to get overall Benefits
Weight Economic by .833 and Technological
by .167 and add to obtain:
Alternatives under Benefits Overall Ideals Normals Raw
1 Outsource all application development
work 1.0000 0.6350 1.0000
2 Outsource the design and
programming phases 0.3972 0.2522 0.3972
3 Do not outsource any application
development work 0.1776 0.1128 0.1776
18
19. Results under Benefits,
Opportunities, Costs and Risks
Positive Negative
Benefits Oppor. Costs Risks
Alternatives Ideals Ideals Ideals Ideals
1 Outsource all application development
work 1.0000 1.0000 0.8518 1.0000
2 Outsource the design and
programming phases 0.3972 0.8277 0.7653 1.0000
3 Do not outsource any application
development work 0.1776 0.4908 1.0000 0.3570
19
20. Weight the BOCR by rating the top
alternative for each merit against the
Strategic Criteria
20
21. Rating the BOCR
What is the highest valued alternative for Benefits? To determine what
it is synthesize in the Benefits control subnet which will rank the
Alternatives under Benefits. Keep that highest alternative in mind and
perform ratings across the Benefits row as to how it impacts the
strategic criteria. Repeat across the Opportunities row for
Opportunities’ highest valued alternative…and so on. For Costs and
Risks the highest valued alternative will be the worst one so you will be
rating by asking the question “How does this worst alternative for Costs
(Risks) impact the strategic criteria?”
21
22. Final Step is to Combine the BOCR
Using a Formula
1. Additive negative formula – generally best for
long term results: bB+oO-cC-rR
2. Multiplicative formula – equivalent to
marginal cost/benefit analysis and generally
best for short term results: BO/CR
22
27. When to use what formula
• Additive (negative) gives best alternative for the
long-term
• Multiplicative gives the best alternative for the
short-term
• In practice one usually looks at both. Often they
give the same best alternative, but not always.
• Not possible to do sensitivity for multiplicative
formula as the b,o,c and r cancel out.
27
28. Performing Sensitivity on the
BOCR Nodes
• Select the Additive (negative) formula using the
Design>Sensitivity command
• Select Computations>Sensitivity
• Select Edit>Independent Variable (the initial
graph is usually not the one you want – because
the software starts with the first node in
alphabetical order as the independent variable).
You need to select the correct Independent
Variable (to do sensitivity for the Risks, for
example, choose Risks)
28
29. Set the Sensitivity Parameters
• Click on the node “Priority: 1 Availability of …” to select it, then
select Edit to get to the Edit Parameter dialogue box. Change the Wrt
Node to Risks.
29
30. Parameter Settings for Risks Sensitivity
• In the Edit Parameter dialogue box set the Parameter Type to 0 for
priorities and the Network to 0 for the top-level network (it is the
bottom position as shown below – there is no name on it) and select
Risks for the Wrt (with respect to) Node.
30
31. Display the Sensitivity Graph
• Click the Done button then the Update button to display
the graph shown below
• As the priority of Risk increases above about 40% the best
option changes from Outsource All to Do Not Outsource
The “with
respect to”
variable is
shown at the
bottom
31