Big Data - Exam II

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Big Data - Exam II
2013-09-28 15:58:22
Modeling Analysis Linear Programming

Modeling & Analysis - Linear Programming
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  1. mathematical (quantitative) models
    a system of symbols and expressions representing a real situation
  2. environmental scanning and analysis
    the monitoring, scanning, and interpretation of collected information
  3. influence diagrams
    graphical models of mathematical models - can facilitate the variable identification process
  4. forecasting (predictive analytics)
    predicting the future
  5. Decision Support Systems
  6. MBMS (model base management systems)
    used to aid in the management of models
  7. DSS - use quantitative models
    expert systems - use knowledge-based models
    DSS uses mostly _____ models, whereas expert systems use _____ models in their applications.
  8. 1. optimization of problems with few alternatitives
    2. optimization via algorithm
    3. optimization via an analytic formula
    4. simulation
    5. heuristics
    6. predictive models
    7. other models
    seven groups of DSS models
  9. heuristics
    model that includes finding a good enough solution using rules
  10. optimization of problems with few alternatitives
    model that infolves finding the best solution from a small number of alternatives
  11. optimization via algorithm
    model that involves finding the best solution from a large number of alternatives, using a step-by-step improvement process (linear and other mathematical programming models)
  12. optimization via an analytic formula
    model that involves finding the best solution in one step using a formula
  13. simulation
    model that involves finding a good enough solution or the best among the alternatives checked, using experimentation
  14. predictive models
    model that predicts the future for a given scenario
  15. other models
    model used to solve a what-if case, using a formula
  16. management support system
  17. multidimensional analysis (modeling)
    involves data analysis in several dimensions - data are generally shown in a spreadsheet format
  18. influence diagram
    a graphical representation of a model - ie: a model of a model
  19. result (or outcome) variables
    decision variables
    uncontrollable variables (and/or parameters)
    intermediate result variables (not a "major" component)

     - all linked by mathematical relationships
    four basic components of a model
  20. Decision Support System (DSS)
    any system that is designed to help a decision maker make a better decision
  21. a decision variable
    A rectangle in an influence diagram represents ____.
  22. an uncontrollable or intermediate variable
    A circle in an influence diagram represents ____.
  23. the result (outcome) variable; intermediate or final
    An oval in an influence diagram represents ____.
  24. the direction of influence (relationship)
    A arrow in an influence diagram indicates ____.
  25. mathematical relationships
    What links the components of models?
  26. result (outcome) variables
    reflect the level of effectiveness of a system - ie: indicate how well the system performs or attains its goals
  27. dependent variables
    Result variables are also known as ______.
  28. decision variables
    describe alternative courses of action - ex: the amount to invest in bonds, people, times, & schedules
  29. uncontrollable variables (fixed), parameters, or variables (can vary)
    factors that affect the result variables but are not under the control of the decision maker
  30. constraints
    Variables that limit the decision maker form _____.
  31. intermediate result variables
    reflect intermediate outocomes in mathematical models - ie: not what you're looking for, but leads to what you're looking for
  32. certainty
    categories of knowledge that decision situations can be grouped into
  33. certainty model
    • decision making model in which:
    • complete knowledge is assumed
    • all potential outcomes are known
    • may yield the optimal solution
  34. uncertainty model
    • decision making model in which:
    • there are several outcomes for each decision
    • the probability of each outcome is known
    • knowledge would lead to less uncertainty
  35. risk analysis model (probablistic decision making)
    • the decision making model in which the following are taken into consideration:
    • probability of each of several outcomes occuring
    • level of uncertainty >= expected value
  36. decision making under certainty
    it is assumed that complete knowldege is available so that the decision maker knows exactly what the outcome of each course of action will be - occurs most often with structured problems with short time horizons
  37. decision making under uncertainty
    decision maker considers situations in which several outcomes are possible for each course of action - decision makes does not know, or cannot estimate, the probability of occurrence
  38. decision making under risk (ie: probabilistic or stochastic) decision making situation
    the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence - long-run probabilities that the given outcomes will occur are assumed to be known or can be estimated
  39. assumed risk
    The category of knowledge that most major business decisions are made under:
  40. risk analysis (calculated risk)
    a decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives
  41. risk analysis (calculated risk)
    can be performed by calculating the expected value of each alternative and selecting the one with the best expected value
  42. spreadsheet
    most popular end-user modeling tool:
  43. 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
  44. constraints
    restriction of allocation by several limitations and requirements
  45. optimal solution (best solution)
    the solution for an alocation problem in which the degree of goal attainment associated with it is the highest
  46. non-quantitative models (qualitative)
    quantitative models
    two types of mathematical models
  47. non-quantitative models (qualitative)
    captures symbolic relationships between decision variables, uncontrollable variables and result variables
  48. quantitative models
    mathematically links decision variables, uncontrollable variables , and result variables
  49. objective function
    a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimized
  50. decision variables
    variables in a linear programming (LP) problem whose values are unknown and are searched for
  51. objective function coefficient
    unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable
  52. constraints
    expressed in the form of linear inequalities or equalities that limit resources and/or requirements
  53. capacities
    describe the upper and sometimes lower limits on the constraints and variables
  54. input/output (technology) coefficients
    indicate resource utilization for a decision variable