# IS425Exam1ch4

 Modeling and Analysis Topics -Modeling for MSS (a critical component)-Static and dynamic models-Treating certainty, uncertainty, and risk-Influence diagrams-MSS modeling in spreadsheets-Decision analysis of a few alternatives (with decision tables and decision trees)-Optimization via mathematical programming-Heuristic programming-Simulation-Model base management Major Modeling Issues -Problem identification and environmental analysis (information collection)-Variable identification-Influence diagrams, cognitive maps-Forecasting/predicting-More information leads to better prediction-Multiple models: A MSS can include several models, each of which represents a different part of the decision-making problem-Model management Categories of models Static Analysis -Single snapshot of the situation-Single interval-Steady state Dynamic Analysis -Dynamic models-Evaluate scenarios that change over time-Time dependent-Represents trends and patterns over time-More realistic: Extends static models -Certainty Models -Assume complete knowledge-All potential outcomes are known-May yield optimal solution Uncertainty -Several outcomes for each decision-Probability of each outcome is unknown-Knowledge would lead to less uncertainty Risk analysis (probabilistic decision making) -Probability of each of several outcomes occurring-Level of uncertainty => Risk (expected value) Influence Diagrams -Graphical representations of a model “Model of a model”-A tool for visual communication-Some influence diagram packages create and solve the mathematical model-Framework for expressing MSS model relationships--Rectangle = a decision variable--Circle = uncontrollable or intermediate variable--Oval = result (outcome) variable: intermediate or final-Variables are connected with arrows  indicates the direction of influence (relationship) Decision tables -Multiple criteria decision analysis-Features include decision variables (alternatives), uncontrollable variables, result variablesOne goal: maximize the yield after one year Decision trees -Graphical representation of relationships-Multiple criteria approach-Demonstrates complex relationships-Cumbersome, if many alternatives exists Mathematical Programming -A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal Optimal solution: The best possible solution to a modeled problem -Linear programming (LP): A mathematical model for the optimal solution of resource allocation problems. All the relationships are linear Linear Programming Steps 1. Identify the …-Decision variables -Objective function -Objective function coefficients -Constraints --Capacities / Demands2. Represent the model-LINDO: Write mathematical formulation-EXCEL: Input data into specific cells in Excel3. Run the model and observe the results Sensitivity -Assesses impact of change in inputs on outputs-Eliminates or reduces variables-Can be automatic or trial and error What-if -Assesses solutions based on changes in variables or assumptions (scenario analysis) Goal seeking -Backwards approach, starts with goal-Determines values of inputs needed to achieve goal-Example is break-even point determination Heuristic Programming -Cuts the search space-Gets satisfactory solutions more quickly and less expensively-Finds good enough feasible solutions to very complex problems-Heuristics can be -Quantitative-Qualitative (in ES) Traveling Salesman Problem -A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible route When to Use Heuristics -Inexact or limited input data-Complex reality-Reliable, exact algorithm not available-Computation time excessive-For making quick decisions Limitations of Heuristics -Cannot guarantee an optimal solution Modern Heuristic Methods -Tabu search-Intelligent search algorithm-Genetic algorithms-Survival of the fittest-Simulated annealing-Analogy to Thermodynamics Simulation -Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system-Frequently used in DSS tools Major Characteristics of Simulation -Imitates reality and capture its richness-Technique for conducting experiments-Descriptive, not normative tool-Often to “solve” very complex problems--Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques Advantages of Simulation -The theory is fairly straightforward-Great deal of time compression-Experiment with different alternatives-The model reflects manager’s perspective-Can handle wide variety of problem types -Can include the real complexities of problems -Produces important performance measures-Often it is the only DSS modeling tool for non-structured problems Limitations of Simulation -Cannot guarantee an optimal solution-Slow and costly construction process-Cannot transfer solutions and inferences to solve other problems (problem specific)-So easy to explain/sell to managers, may lead overlooking analytical solutions-Software may require special skills Simulation Methodology -Steps: -Model real system and conduct repetitive experiments. 1. Define problem 2. Construct simulation model 3. Test and validate model 4. Design experiments5. Conduct experiments6. Evaluate results7. Implement solution Stochastic vs. Deterministic Simulation -In stochastic simulations: We use distributions (Discrete or Continuous probability distributions) Time-dependent vs. Time-independent Simulation -Time independent stochastic simulation via Monte Carlo technique (X = A + B) Discrete event vs. Continuous simulation Steady State vs. Transient Simulation Simulation Implementation -Visual simulation-Object-oriented simulation Visual interactive modeling (VIM) Also called -Visual interactive problem solving-Visual interactive modeling-Visual interactive simulation-Uses computer graphics to present the impact of different management decisions-Often integrated with GIS -Users perform sensitivity analysis-Static or a dynamic (animation) systems Model Base Management -MBMS: capabilities similar to that of DBMS-But, there are no comprehensive model base management packages-Each organization uses models somewhat differently-There are many model classes-Within each class there are different solution approaches-Relations MBMS-Object-oriented MBMS Authortttran1 ID204876 Card SetIS425Exam1ch4 DescriptionIS425Exam1ch4 Updated2013-03-04T16:22:01Z Show Answers