Modeling and Analysis

DSS modeling – Issues

• DSS – can be composed of multiple models

• Modeling Issues -

• Identification of problems and environment analysis

• Variable identification

• Forecasting (predictive analysis)

DSS modeling – Categories

• Optimisation of problems with few alternatives

• Optimisation via algorithm

• Optimisation via analytical formula

• Simulation

• Heuristics

• Predictive models

• Other Models

DSS modeling – Categories

DSS modeling – Trends

• Model libraries and solution techniques

• Using web tools – perform modeling, optimisation, simulation etc

• Multidimensional analysis

• Model for model analysis

Classification of DSS Models

Static Analysis:

• Static model takes a single snapshot of situation

• Everything occurs in a single interval.

• E.g. Make or buy decision

• Stability of the relevant data is assumed.

Dynamic Analysis:

• Represents scenarios that change over time.

• E.g. 5-year profit and loss projection in which the input data, such as costs, prices, and quantities, change from year to year.

• Time dependent

• Important because they use, represent, or generate trends and patterns over time.

• Shows average per period, moving averages and comparative analysis.

Certainty, uncertainty, and risk

Decision situations are often classified on the

basis of what the decision maker believes about

the forecasted results. The categories are:

• Certainty

• Risk

• Uncertainty

Decision Making Under Certainty

• Complete knowledge is available

• Decision maker knows the outcome of each

course of action

• Situation involve is often with structured

problems with short time horizons

• Certain models are relatively easy to develop and solve and they can yield optimal solutions.

Decision making under uncertainty

• Several outcomes for each course of action.

• Decision maker does not know, or cannot estimate the possible outcomes.

• More difficult because of insufficient information.

• Involves assessment of the decision maker’s attitude towards risk.

Decision making under risk(Risk analysis)

• Decision maker must consider several possible outcomes for each alternative.

• The decision maker can assess the degree of risk associated with each alternative.

• Risk analysis can be performed by calculating the expected value for each alternative and selecting the one with best expected value.

Decision analysis with decision tables and decision trees

Decision Table:

• Organize information and knowledge in systematic tabular manner

Decision Trees:

• Alternative representation of the decision table

• Shows the relationship of the problem graphically and handle complex situations

• Can be cumbersome if there are many alternatives or static nature.

• TreeAge Pro and Precision Tree: Powerful and sophisticated decision tree analysis systems

Structure of mathematical models for decision support

Components of decision

support mathematical

models:

• Result Variables

• Decision Variables

• Uncontrollable variables

• Intermediate result

variables

• Result Variables: reflect the level of effectiveness of a system

• Decision Variables: describes alternative course of action.

• Uncontrollable Variables: Some factors that affect the result variables but not under the control of decision maker.

• Intermediate result Variables: reflect intermediate outcomes in mathematical models.

Multiple Goals

Sensitivity Analysis• Attempts to assess the impact of a change in input data

on proposed solution.• Important because it allows flexibility and adaptation

to changing conditions • Provides a better understanding of the model and the

decision making situation• Used for:1.Revising models to eliminate too-large sensitivities.2.Adding details about sensitive variables.3.Obtainong better estimate of sensitive external

variables.4.Altering a real-world system to reduce actual

sensitivities.

What-If-Analysis

• What will happen to the solution if an input variables, an assumption, or a parameter value is changed

• With the appropriate user interface, it is easy for manager to ask a computer model different questions and get the answers.

• Common in expert systems.

• User get an opportunity to change their answers to some question’s.

Goal Analysis

• Calculates the values of the inputs necessary to achieve a desired level of output.

• Represents a backward solution approach

Problem solving search methods

The choice phase of problem solving involves a

search for an appropriate course of action.

Search approaches are:

• Analytical Techniques

• Algorithms

• Blind Searching

• Heuristic Searching

Simulation

• Is a appearance of reality.

• A technique for conducting experiments with computer on model of a management system

• Characteristics:

1.Simulation typically imitative.

2.Technique for conducting experiments.

3.Descriptive rather than a normative.

4.Used only when a problem is too complex to be treated using numerical optimizing techniques.

Advantages of simulation• Theory is fairly straightforward.

• Great time compression

• Descriptive rather than normative.

• Built from the manager’s perspective.

• Built for one particular problem and cannot solve any other problem.

• A manager can experiment to determine which decision variables and which part of environment are really important, and with different alternatives.

• Can handle an extremely wide variety of problem types, such as inventory and staffing.

• Can include the real complexities of problems.

• Automatically produce many important performance measures.

• Relatively easy-to-use simulation packages.

• Often the only DSS modeling method that can readily handle relatively unstructured problem.

Disadvantages of simulation

• An optimal solution cannot be guaranteed.

• Model construction can be a slow and costly process.

• Solutions are not transferable to other problems

• Easy to explain to managers that analytic methods are overlooked.

• Requires special skills because of the complexity of the formal solution method.

The Methodology of Simulation

Test & validate the

model

Real worldproblem

Define the problem

Construct simulation

model

Implement the result

Design the simulation

experiments

Conduct the experiments

Evaluates the results

Simulation type

Probabilistic Simulation:

• One or more of the independent variables

• Follow certain probability distributions namely

1.Discete distribution

2.Continuous distribution

• Conducted with the aid of technique called Monte Carlo simulation.

Time-Dependent Vs Time-Independent

Simulation:

• Time-independent-not important to know the exact time of event

• Time-dependent-In waiting line problems, it is important to know the precise time of arrival.

Object-Oriented Simulation:

• SIMPROCESS is an object-oriented process modeling tool that allows user to create a simulation model by using screen based object.

• Unified Modeling Language(UML)- Designed for object-oriented and object based systems and applications.

• Java based simulations are essentially object oriented.

Visual Simulation:

• Graphical display of computerized results

• Includes animations

• Is one of the most successful development in computer-human interactions and problem solving.

Quantitative Software Packages

• Are preprogrammed models and optimization systems.

• Serve as building blocks for other quantitative models

• A variety of these are available for inclusion in DSS as major and minor modeling components.

• Revenue management systems focus on identifying right product for right customer.

• Airlines have used such systems to determine right price for each airline seat.

• System also available for retail operations, entertainment venues, and many other industries.