1. MSS Modeling Key element in DSS Many classes of models
Specialized techniques for each model
Allows for rapid examination of alternative solutions
Multiple models often included in a DSS
Trend toward transparency
Multidimensional modeling exhibits as spreadsheet

2. Major Modeling Issues Problem identification Environmental analysis
Variable identification
Forecasting
Multiple model use
Model categories or selection
Model management
Knowledge-based modeling

3. Simulations Explore problem at hand Identify alternative solutions
Can be object-oriented
Enhances decision making
View impacts of decision alternatives

4. DSS Models Algorithm-based models Statistic-based models
Linear programming models
Graphical models
Quantitative models
Qualitative models
Simulation models
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5. Problem Identification
Environmental scanning and analysis
Business intelligence
Identify variables and relationships
Influence diagrams
Cognitive maps
Forecasting
Fueled by e-commerce
Increased amounts of information available through technology
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7. Static Models Single photograph of situation Single interval
Time can be rolled forward, a photo at a time
Usually repeatable
Steady state
Optimal operating parameters
Continuous
Unvarying
Primary tool for process design

8. Dynamic Model Represent changing situations Time dependent
Varying conditions
Generate and use trends
Occurrence may not repeat

9. Decision-Making Certainty Assume complete knowledge
All potential outcomes known
Easy to develop
Resolution determined easily
Can be very complex
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10. Decision-Making Uncertainty Several outcomes for each decision
Probability of occurrence of each outcome unknown
Insufficient information
Assess risk and willingness to take it
Pessimistic/optimistic approaches
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11. Decision-Making Probabilistic Decision-Making Decision under risk
Probability of each of several possible outcomes occurring
Risk analysis
Calculate value of each alternative
Select best expected value
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12. Influence Diagrams Graphical representation of model
Provides relationship framework
Examines dependencies of variables
Any level of detail
Shows impact of change
Shows what-if analysis
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13. Influence Diagrams Variables: Decision
Intermediate or uncontrollable
Result or outcome (intermediate or final)
Decision
Arrows indicate type of relationship and direction of influence
Certainty
Amount in CDs
Interest earned
Sales
Uncertainty
Price
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14. Influence Diagrams Random (risk) Preference
~ Demand
Random (risk)
Place tilde above variable’s name
Sales
Sleep all day
Graduate
University
Preference
(double line arrow)
Get job
Ski all day
Arrows can be one-way or bidirectional, based upon the direction of influence
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16. Modeling with Spreadsheets
Flexible and easy to use
End-user modeling tool
Allows linear programming and regression analysis
Features what-if analysis, data management, macros
Seamless and transparent
Incorporates both static and dynamic models
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18. Decision Tables Multiple criteria decision analysis Features include:
Decision variables (alternatives)
Uncontrollable variables
Result variables
Applies principles of certainty, uncertainty, and risk
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19. Decision Tree Graphical representation of relationships
Multiple criteria approach
Demonstrates complex relationships
Cumbersome, if many alternatives
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20. MSS Mathematical Models
Link decision variables, uncontrollable variables, parameters, and result variables together
Decision variables describe alternative choices.
Uncontrollable variables are outside decision-maker’s control.
Fixed factors are parameters.
Intermediate outcomes produce intermediate result variables.
Result variables are dependent on chosen solution and uncontrollable variables.
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21. MSS Mathematical Models
Nonquantitative models
Symbolic relationship
Qualitative relationship
Results based upon
Decision selected
Factors beyond control of decision maker
Relationships amongst variables
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23. Mathematical Programming
Tools for solving managerial problems
Decision-maker must allocate resources amongst competing activities
Optimization of specific goals
Linear programming
Consists of decision variables, objective function and coefficients, uncontrollable variables (constraints), capacities, input and output coefficients
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24. Multiple Goals
Simultaneous, often conflicting goals sought by management
Determining single measure of effectiveness is difficult
Handling methods:
Utility theory
Goal programming
Linear programming with goals as constraints
Point system
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25. Sensitivity, What-if, and Goal Seeking Analysis
Assesses impact of change in inputs or parameters on solutions
Allows for adaptability and flexibility
Eliminates or reduces variables
Can be automatic or trial and error
What-if
Assesses solutions based on changes in variables or assumptions
Goal seeking
Backwards approach, starts with goal
Determines values of inputs needed to achieve goal
Example is break-even point determination
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26. Search Approaches
Analytical techniques (algorithms) for structured problems
General, step-by-step search
Obtains an optimal solution
Blind search
Complete enumeration
All alternatives explored
Incomplete
Partial search
Achieves particular goal
May obtain optimal goal
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27. Search Approaches Heurisitic Repeated, step-by-step searches
Rule-based, so used for specific situations
“Good enough” solution, but, eventually, will obtain optimal goal
Examples of heuristics
Tabu search
Remembers and directs toward higher quality choices
Genetic algorithms
Randomly examines pairs of solutions and mutations
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29. Simulations Imitation of reality
Allows for experimentation and time compression
Descriptive, not normative
Can include complexities, but requires special skills
Handles unstructured problems
Optimal solution not guaranteed
Methodology
Problem definition
Construction of model
Testing and validation
Design of experiment
Experimentation
Evaluation
Implementation
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30. Simulations Probabilistic independent variables
Discrete or continuous distributions
Time-dependent or time-independent
Visual interactive modeling
Graphical
Decision-makers interact with simulated model
may be used with artificial intelligence
Can be objected oriented
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32. Optimization via Mathematical Programming
Linear programming (LP)
Used extensively in DSS
Mathematical Programming
Family of tools to solve managerial problems in allocating scarce resources among various activities to optimize a measurable goal

33. LP Allocation Problem Characteristics
1. Limited quantity of economic resources
2. Resources are used in the production of products or services
3. Two or more ways (solutions, programs) to use the resources
4. Each activity (product or service) yields a return in terms of the goal
5. Allocation is usually restricted by constraints

34. LP Allocation Model Rational economic assumptions
1. Returns from allocations can be compared in a common unit
2. Independent returns
3. Total return is the sum of different activities’ returns
4. All data are known with certainty
5. The resources are to be used in the most economical manner
Optimal solution: the best, found algorithmically

35. Linear programming components
LP is composed of the following:
1- decision variables- vars whose values are unknown and / or searched for.
2- objective functions: math functions that do the following:
a- relates decision vars to goals.
b- measure goal attainment to be optimized.

36. 3- objective function coefficient: unit profit or cost coefficient indicating the contribution to the objective of one unit of decision variable.
4- constraints: expressed in the form of linear inequalities or equalities that limit resources and/or requirements.
5-capacities describe upper and lower limits on constraint variables.

37. 6- input-output coefficient-technology which indicate resource utilization for decision variables.
Do the homework handed in class for linear programming implentation.

38. Multiple Goals
Analysis of management decision aims at evaluating how far each alternative brings management toward achieving its goals.
Most management decisions have multiple goals.
Different management have different goals
To achieve multiple goals, we need to analyze each alternative in light of achievement of the proposed goal.

39. Goals may complement each other or conflict each other.
Difficulties of analyzing multiple goals:
1- it is hard for organizations to clearly state their goals.
2-DM may change the importance assigned to goals overtime or for different situation – situation change.

40. 3- goals and sub goals are viewed differently at various levels of organization and within different departments.
4-if organization changes or the environment changes goals also change.
5-difficult to quantify relations between alternatives and their role in goal determination.

41. 6-Each DM has different goals regarding a complex problem and he participate to solve it.
7-particpant in problem solving assess the priorities of various goals differently.

42. Method to handle Multiple Goals
1- utility theory
2- goal programming
3- expression of goals and constraints using LP.
4- a point system

43. Sensitivity Analysis
Attempts to assess the impact of change in the input data or parameters on the proposed solution- the result variables.
Allows flexibility and adaptation to changing conditions and to the requirements of different decision-making situation.
Provide better understanding of the model and the decision making situation it attempts to describe.

44. Permits managers to input data so that confidence in the model increases.
Tests relationships such as:
1- impact of change in external variables (uncontrolled) and on outcome variables.
2-Impact of changes in decision variables on outcome vars.
3- effect of uncertainly in estimating external vars.

45. 4-Effects of different dependent interactions among vars.
5-Robustness of decision under changing conditions

46. Uses of sensitivity analysis
Sensitivity analysis can be used for:
1- revising models to eliminate large sensitivities.
2-adding details about sensitive vars or scenarios.
3-obtaining better estimates of sensitive external vars.
4-altering the real-world system to reduce actual sensitivity.

47. 5- accepting and using the sensitive real world, leading to continuous and close monitoring of actual results.

48. Types of sensitivity A- automatic sensitivity analysis
Reports the range within which a certain input var (unit cost) can vary without affecting the proposed solution.
Usually limited to one change at a time, only for certain vars.

49. B- Trail and Error:
Impact of change in any var or several vars, can be determined through trail and error approach.
You can change some input and solve problem again. The more vars change the more solutions you discover. This can be done through either what-if or goal seeking.

50. What-if : structured as what will happen to the solution if an input var or an assumption or value is changed?
Goal-seeking: calculates values of the input necessary to achieve a desired level of an output (goal). It represent a backward solution approach.
Ex: how many tellers needed to reduce waiting time in a bank?

51. Problem-solving search methods
When problem solving, Choice phase involves a search for an appropriate course of action that can solve the problem.
For normative models such as math programming based ones, either an analytical approach is used OR a complete, exhaustive calculations are applied (comparing outcomes of all alternatives).

52. For descriptive models, comparison of limited number of alternatives is used.
Analytical techniques:
Use math formulas to derive an optimal solution directly or predict a certain result.
Used for solving structured problems of operational nature such as resource allocation or inventory managment

53. Algorithms:
Used by analytical technique to increase efficiency of search.
It is step by step search process for getting an optimal solution.
Solutions are generated and tested for improvements, and tested again , this repeats until no further improvements is possible.

54. Heuristic Programming
Cuts the search
Gets satisfactory solutions more quickly and less expensively
Finds rules to solve complex problems
Finds good enough feasible solutions to complex problems
Heuristics can be
Quantitative
Qualitative (in ES)

55. When to Use Heuristics 1. Inexact or limited input data
2. Complex reality
3. Reliable, exact algorithm not available
4. Computation time excessive
5. To improve the efficiency of optimization
6. To solve complex problems
7. For symbolic processing
8. For making quick decisions

56. Advantages of Heuristics
1. Simple to understand: easier to implement and explain
2. Help train people to be creative
3. Save formulation time
4. Save programming and storage on computers
5. Save computational time
6. Frequently produce multiple acceptable solutions
7. Possible to develop a solution quality measure
8. Can incorporate intelligent search
9. Can solve very complex models

57. Limitations of Heuristics
1. Cannot guarantee an optimal solution
2. There may be too many exceptions
3. Sequential decisions might not anticipate future consequences
4. Interdependencies of subsystems can influence the whole system
Heuristics successfully applied to vehicle routing

58. Heuristic Types Construction Improvement Mathematical programming
Decomposition
Partitioning

59. Simulation
Technique for conducting experiments with a computer on a model of a management system
Frequently used DSS tool

60. Simulation
To assume the appearance of the characteristics of reality. It is a technique for conducting experiments (what-if analysis) with computer on a model of a management system.
Most common method for handling semi structured and unstructured situation.
It is a well established, useful method for gaining insights into complex MSS situation.

61. Simulation characteristics
It imitates a model.
It conducting experiments, it involves testing specific values of decision or uncontrolled vars and observing the impact on output vars.
It is descriptive, describes or predict the characteristics of a system under different situation.

62. Repeats an experiment many times to obtain an estimate of the overall effect of certain actions.
Used when a problem is too complex to be treated by numerical optimization techniques.
Complexity means:
1- problem can not be formulated for optimization ( assumption do not hold)

63. 2- formulation is too large
3-too many interactions among variables.
4-The problem exhibits some Risk and uncertainty.

64. Simulation advantages
It allows managers to pose what-if questions, use trail and error approach which is considered cheaper, faster, more accurate and less risks.
Managers can experiment to determine which decision vars and part of environment that are important with different alternatives.

65. Provide more understanding of the problem.
Built for specific problem, no generalization is required
Can include the real complexities of the problem, simplification are not necessary, handles unstructured problems.

66. Simulation Disadvantages
Optimal solutions can not be guaranteed, good ( satisfying) is found.
Construction of simulation models could be slow and costly.
Solutions are not transferable to other problem.
Simulation SW need special skills. Not easy to use.

67. Building simulation model
Building a simulation model consists of the following steps:
1- problem definition:
- real-world problem is examined and classified.
-here we specify why simulation is appropriate.
-define system boundaries, environment.

68. 2- construction of simulation model:
- determine vars and relationships among them.
-data are gathered, flowcharts are drawn and computer programs are written.
3-testing and validating the model:
Making sure that the model represent the system under study properly- testing and validation.

69. 4- design the experiment
-determine how long to run the simulation.
- consider two conflicting objectives (accuracy and cost) which are identified by three scenarios :
a- typical  mean and median cases for
random vars
b-best-case (low-cost, high revenues)
c- worst-case (high-cost, low revenues)

70. 5-Conducting experiment:
-range from random-number generation to presenting results.
6- evaluation of results:
results must be interpreted, use sensitivity analysis.
7- implementation:
Chances of success are high because managers are involved in the process of simulation model development.

71. Simulation types

72. Major Characteristics of Simulation
Imitates reality and capture its richness
Technique for conducting experiments
Descriptive, not normative tool
Often to solve very complex, risky problems

73. Advantages of Simulation
1. Theory is straightforward
2. Time compression
3. Descriptive, not normative
4. MSS builder interfaces with manager to gain intimate knowledge of the problem
5. Model is built from the manager's perspective
6. Manager needs no generalized understanding. Each component represents a real problem component
(More)

74. 7. Wide variation in problem types
8. Can experiment with different variables
9. Allows for real-life problem complexities
10. Easy to obtain many performance measures directly
11. Frequently the only DSS modeling tool for nonstructured problems
12. Monte Carlo add-in spreadsheet packages

75. Limitations of Simulation
1. Cannot guarantee an optimal solution
2. Slow and costly construction process
3. Cannot transfer solutions and inferences to solve other problems
4. So easy to sell to managers, may miss analytical solutions
5. Software is not so user friendly

76. Simulation Methodology
Model real system and conduct repetitive experiments
1. Define problem
2. Construct simulation model
3. Test and validate model
4. Design experiments
5. Conduct experiments
6. Evaluate results
7. Implement solution

77. Simulation Types Probabilistic Simulation Discrete distributions
Continuous distributions
Probabilistic simulation via Monte Carlo technique
Time dependent versus time independent simulation
Simulation software
Visual simulation
Object-oriented simulation