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TRANSCRIPT
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Chapter 4
MODELING AND
ANALYSIS
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Learni ng Obj ectiv es Understand the basic concepts of
management support system (MSS)modeling
Describe how MSS models interact withdata and the user
Understand some different, well-known
model classes Understand how to structure decisionmaking with a few alternatives
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Learni ng Obj ectiv es Describe how spreadsheets can be used
for MSS modeling and solution
Explain the basic concepts of
optimization, simulation, and heuristics,
and when to use them
Describe how to structure a linear
programming model
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Learni ng Obj ectiv es Understand how search methods are used
to solve MSS models
Explain the differences among algorithms,
blind search, and heuristics
Describe how to handle multiple goals
Explain what is meant by sensitivity
analysis, what-if analysis, and goal seeking
Describe the key issues of model
management
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MSS Model ing Lessons from modeling at DuPont
By accurately modelingand simulatingits rail
transportation system, decision makers were
able to experiment with different policies andalternatives quickly and inexpensively
The simulation model was developed and
tested known alternative solutions
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MSS Model ing Lessons from modeling for Procter & Gamble
DSS can be composed of several models used
collectively to support strategic decisions in the
company
Models must be integrated
models may be decomposed and simplified
A suboptimization approach may be appropriate
Human judgment is an important aspect of usingmodels in decision making
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MSS Model ing Lessons from additional modeling
applications
Mathematical (quantitative) model
A system of symbols and expressions that
represent a real situation
Applying models to real-world situations can
save millions of dollars or generate millionsof dollars in revenue
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MSS Model ing Current modeling issues
Identification of the problem and
environmental analysis
Environmental scanning and analysis
A process that involves conducting a search
for and an analysis of information in external
databases and flows of information
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MSS Model ing Current modeling issues
Variable identification
Forecasting
Predicting the future
Predictive analytics systems attempt to
predict the most profitable customers, the
worst customers, and focus on identifyingproducts and services at appropriate prices
to appeal to them
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MSS Model ing Current modeling issues
Multiple models: A DSS can include several models,
each of which represents a different part of the
decision-making problem
Model categories
Optimization of problems with few alternatives
Optimization via algorithm
Optimization via an analytic formula
Simulation
Predictive models
Other models
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MSS Model ing Current modeling issues
Model management
Knowledge-based modeling
Current trends
Model libraries and solution technique libraries
Development and use of Web tools
Multidimensional analysis (modeling)A modeling method that involves data analysis in
several dimensions
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MSS Model ing Current trends
Multidimensional analysis (modeling)
A modeling method that involves data
analysis in several dimensions
Influence diagram
A diagram that shows the various types of
variables in a problem (e.g., decision,independent, result) and how they are
related to each other
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Stati c a nd Dynami cModel s Static models
Models that describe a single interval of a
situation
Dynamic models
Models whose input data are changed
over time (e.g., a five-year profit or loss
projection)
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Cert ai nty,Uncertai nty, and Ri sk
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Cert ain ty,Uncertai nty, and Ri sk Certainty
A condition under which it is assumed that future
values are known for sure and only one result is
associated with an action Uncertainty
In expert systems, a value that cannot be
determined during a consultation. Many expert
systems can accommodate uncertainty; that is,
they allow the user to indicate whether he or she
does not know the answer
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Cert ain ty,Uncertai nty, and Ri sk Risk
A probabilistic or stochastic decision
situation
Risk analysis
A decision-making method that analyzes
the risk (based on assumed known
probabilities) associated with different
alternatives. Also known as calculated risk
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MSS Model ingwith Spreadsheets Models can be developed and
implemented in a variety of programming
languages and systems
The spreadsheet is clearly the most
popularend-user modeling toolbecause it
incorporates many powerful financial,
statistical, mathematical, and otherfunctions
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MSS Model ingwith Spreadsheets
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MSS Model ingwith Spreadsheets Other important spreadsheet features include
what-if analysis, goal seeking, data
management, and programmability
Most spreadsheet packages provide fairlyseamless integration because they read and
write common file structures and easily
interface with databases and other tools
Static or dynamic models can be built in aspreadsheet
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MSS Model ingwith Spreadsheets
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Deci sion Ana lysi s wi thDeci sion Tab les and Deci sionTrees Decision analysis
Methods for determining the solution to a
problem, typically when it is inappropriate
to use iterative algorithms
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Deci sion Ana lysi s wi thDeci sion Tab les and Deci sionTrees Decision table
A table used to represent knowledge and
prepare it for analysis in:
Treating uncertainty
Treating risk
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Deci sion Ana lysi s wi thDeci sion Tab les and Deci sionTrees Decision tree
A graphical presentation of a sequence of
interrelated decisions to be made under
assumed risk
Multiple goals
Refers to a decision situation in which
alternatives are evaluated with several,
sometimes conflicting, goals
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The S tructu re ofMathematical M odels forDecision S upport
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The S tructu re ofMathematical M odels forDecision S upport Components of decision support mathematical
models
Result (outcome) variable
A variable that expresses the result of a decision (e.g.,one concerning profit), usually one of the goals of a
decision-making problem
Decision variable
A variable of a model that can be changed andmanipulated by a decision maker. The decision
variables correspond to the decisions to be made,
such as quantity to produce and amounts of resources
to allocate
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The S tructu re ofMathematical M odels forDecision S upport Uncontrollable variable (parameter)
A factor that affects the result of a decision but
is not under the control of the decision maker.
These variables can be internal (e.g., relatedto technology or to policies) or external (e.g.,
related to legal issues or to climate)
Intermediate result variable
A variable that contains the values of
intermediate outcomes in mathematical
models
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Math emati calProgrammi ngOpti mi za tio n Mathematical programming
A family of tools designed to help solve
managerial problems in which the decision
maker must allocate scarce resourcesamong competing activities to optimize a
measurable goal
Optimal solution
A best possible solution to a modeled
problem
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Math emati calProgrammi ngOpti mi za tio n Linear programming (LP)
A mathematical model for the optimal
solution of resource allocation problems.
All the relationships among the variables inthis type of model are linear
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Math emati calProgrammi ngOpti mi za tio n Every LP problem is composed of:
Decision variables
Objective function
Objective function coefficients
Constraints
Capacities
Input/output (technology) coefficients
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Math emati calProgrammi ngOpti mi za tio n
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Math emati calProgrammi ngOpti mi za tio n
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Multiple goals
Refers to a decision situation in which
alternatives are evaluated with several,
sometimes conflicting, goals
Sensitivity analysis
A study of the effect of a change in one or
more input variables on a proposed
solution
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Sensitivity analysis tests relationships such as:
The impact of changes in external (uncontrollable)
variables and parameters on the outcome
variable(s)
The impact of changes in decision variables on the
outcome variable(s)
The effect of uncertainty in estimating external
variables
The effects of different dependent interactions
among variables
The robustness of decisions under changing
conditions
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Sensitivity analyses are used for:
Revising models to eliminate too-large sensitivities
Adding details about sensitive variables or scenarios
Obtaining better estimates of sensitive externalvariables
Altering a real-world system to reduce actual
sensitivities
Accepting and using the sensitive (and hencevulnerable) real world, leading to the continuous and
close monitoring of actual results
The two types of sensitivity analyses are
automatic and trial-and-error
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Automatic sensitivity analysis
Automatic sensitivity analysis is performed in
standard quantitative model implementations
such as LP Trial-and-error sensitivity analysis
The impact of changes in any variable, or in
several variables, can be determined througha simple trial-and-error approach
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d GoalSeeki ng
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Goal seeking
Asking a computer what values certain
variables must have in order to attain
desired goals
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d GoalSeeki ng
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u p e oa s, ens v yAnal ysi s,What- If Anal ysi s, an d G oalSeeki ng Computing a break-even point by using
goal seeking
Involves determining the value of the decision
variables that generate zero profit
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Problem-Solving SearchMethods
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Problem-Solving SearchMethods Analytical techniques use mathematical
formulas to derive an optimal solution
directly or to predict a certain result
An algorithm is a step-by-step searchprocess for obtaining an optimal solution
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Problem-Solving SearchMethods
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Problem-Solving SearchMethods A goalis a description of a desired solution
to a problem
The search steps are a set of possible
steps leading from initial conditions to thegoal
Problem solving is done by searching
through the possible solutions
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Problem-Solving SearchMethods Blind search techniques are arbitrary
search approaches that are not guided
In a complete enumeration all the alternatives
are considered and therefore an optimalsolution is discovered
In an incomplete enumeration (partial search)
continues until a good-enough solution is
found (a form of suboptimization)
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Problem-Solving SearchMethods Heuristic searching
Heuristics
Informal, judgmental knowledge of an
application area that constitutes the rules ofgood judgment in the field. Heuristics alsoencompasses the knowledge of how to solveproblems efficiently and effectively, how toplan steps in solving a complex problem, howto improve performance, and so forth
Heuristic programming
The use of heuristics in problem solving
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Simu lati on Simulation
An imitation of reality
Major characteristics of simulation
Simulation is a technique forconducting
experiments
Simulation is a descriptive rather than a
normative method Simulation is normally used only when a
problem is too complex to be treated using
numerical optimization techniques
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Simu lati on Complexity
A measure of how difficult a problem is in
terms of its formulation for optimization, its
required optimization effort, or its stochasticnature
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Simu lati on Advantages of simulation The theory is fairly straightforward.
A great amount oftime compression can beattained
A manager can experiment with differentalternatives The MSS builder must constantly interact with
the manager
The model is built from the managersperspective. The simulation model is built for one particular
problem
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Simu lati on Advantages of simulation
Simulation can handle an extremely wide variety
of problem types
Simulation can include the real complexities ofproblems
Simulation automatically produces many
important performance measures
Simulation can readily handle relatively
unstructured problems
There are easy-to-use simulation packages
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Simu lati on Disadvantages of simulation
An optimal solution cannot be guaranteed
Simulation model construction can be a slow andcostly process
Solutions and inferences from a simulation studyare usually not transferable to other problems
Simulation is sometimes so easy to explain to
managers that analytic methods are oftenoverlooked
Simulation software sometimes requires specialskills
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Simu lati on
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Simu lati on Methodology of simulation
1. Define the problem
2. Construct the simulation model
3. Test and validate the model4. Design the experiment
5. Conduct the experiment
6. Evaluate the results7. Implement the results
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Simu lati on Simulation types
Probabilistic simulation
Discrete distributions
Continuous distributions
Time-dependent versus time-independent
simulation
Object-oriented simulation
Visual simulation
Simulation software
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Vi sual Interacti veSimu lati on Conventional simulation inadequacies
Simulation reports statistical results at the end
of a set of experiments
Decision makers are not an integral part ofsimulation development and experimentation
Decision makers experience and judgment
cannot be used directly
Confidence gap occurs if the simulation results
do not match the intuition or judgment of the
decision maker
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Vi sual Interacti veSimu lati on Visual interactive simulation orvisual
interactive modeling (VIM)
A simulation approach used in the decision-
making process that shows graphicalanimation in which systems and processes
are presented dynamically to the decision
maker. It enables visualization of theresults of different potential actions
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Vi sual Interacti veSimu lati on Visual Interactive models and DSS
Waiting-line management (queuing) is a good
example of VIM
The VIM approach can also be used inconjunction with artificial intelligence
General-purpose commercial dynamic VIS
software is readily available
Quantit ativ e S oftw are
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Packages and Mo del BaseManagement Quantitative software packages
A preprogrammed (sometimes called
ready-made) model or optimization system.
These packages sometimes serve asbuilding blocks for other quantitative
models
Quantit ativ e S oftw are
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Packages and Mo del BaseManagement Model base management
Model base management system (MBMS)
Software for establishing, updating, combining, and soon (e.g., managing) a DSS model base
Relational model base management system(RMBMS)
A relational approach (as in relational databases) to thedesign and development of a model base management
system Object-oriented model base management system(OOMBMS)
An MBMS constructed in an object-orientedenvironment