MGMT 309 TERMS

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MGMT 309 TERMS
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  1. A situation in which more than one optimal solution
    is possible. It arises when the angle or slope of the objective is the same as
    the slope of the constraint.
    Alternative Optimal Solution
  2. Cells that represent the decision variables in Solver.
    Changing Cell
  3. A restriction (stated in the form of an inequality of
    an equation) that inhibits (or binds) the value that can be achieved by the
    objective function.
    Constraint
  4. A point that lies on one of the corner of the feasible
    region. This means that it falls at the intersection of two constraint lines.
    Corner (or Extreme) Point
  5. The method of finding the optimal solution to an LP problem
    that involves testing the profit or cost level at each corner point of the
    feasible region. The theory of LP states that the optimal solution must lie at
    one of the corner points.
    Corner Point Method
  6. The unknown quantities in a problem for which optimal
    solution values are to be found.
    Decision Variable
  7. The area that satisfies all of a problem’s resource
    restrictions—that is, the region where all constraint overlap. All possible
    solutions to the problem lie in the feasible region.
    Feasible Region
  8. Any point that lies in the feasible region. Basically, it is
    any point that satisfies all of the problem’s constraints.
    Feasible Solution
  9. Any point that lies outside the feasible region. It
    violates one or more of the stated constraints.
    Infeasible Solution
  10. A straight line that represents all nonnegative combinations
    of the decision variable for a particular profit (or cost) level.
    Level (Iso) Line
  11. The general category of mathematical modeling and
    solution techniques used to allocate resources while optimizing a measurable
    goal; LP is one type of programming model.
    Mathematical Programming
  12. A mathematical statement of the goal of an
    organization, stated as an intent to maximize or minimize some important
    quantity, such as profit or cost.
    Objective Function
  13. A common LP problem that involves a decision as to
    which products a firm should produce given that it faces limited resources.
    Product Mix Problem
  14. A constraint that does not affect the feasible
    solution region.
    Redundant Constraint
  15. An iterative procedure for solving LP problems.
    Simplex Method
  16. The difference between the right-hand-side and
    left-hand-side of a ≤ constraint. Slack typically represents
    the unused resource.
    Slack
  17. An Excel add-in that allows LP problems to be set up
    and solved in Excel.
    Solver
  18. The difference between the left-hand-side and
    right-hand-side of a ≥
    constraint. Surplus typically represents the level of oversatisfaction of a
    requirement.
    Surplus
  19. The cell that contains the formula for the objective
    function in Solver.
    Target Cell
  20. A condition that exists when the objective value can
    be made infinitely large (in a maximization problem) or small (in a
    minimization problem) without violating any of the problem’s constraints.
    Unbounded Solution
  21. The coefficient for a decision variable in the objective
    function. Typically, this refers to unit profit or unit cost.
    Objective Function Coefficient (OFC)
  22. The difference between the marginal contribution to
    the objective function value from the inclusion of a decision variable and the
    marginal worth of the resources it consumes. IN the case of a decison variable
    that has an optimal value of zero, it is also the minimum amount by which the
    OFC of that variable should change before it would have a nonzero optimal value
    Reduced Cost
  23. The study of how sensitive an optimal solution is to
    model assumptions and to data changes. Also referred to as postoptimality
    analysis.
    Sensitivity Analysis
  24. The magnitude of the change in the objective function
    value for a unit increase in the RHS of a constraint.
    Shadow Price
  25. The difference between the RHS and LHS of a ≤ constraint. Typically
    represents the unused resource.
    Slack
  26. The difference between the LHS and RHS of a ≥ constraint. Typically
    represents the level of oversatisfication of a requirement
    Surplus
  27. Decision variables that are required to have integer
    values of either 0 or 1. Also called 0-1 variables.
    Binary Variables
  28. An algorithm used by Solver and other software to
    solve IP problems. It divides the set of feasible solutions into subregions
    that are examined systematically.
    Branch-and-Bound Method
  29. Decision variables that are required to be integer
    valued. Actual values of these variables are restricted only by the constraints
    in the problem.
    General Integer Variables
  30. A mathematical programming technique that produces
    integer solutions to LP problems.
    Integer Programming (IP)
  31. A category of problems in which some decision
    variables must have integer values (either general integer or binary) and other
    decision variables can have fractional values.
    Mixed Integer Programming
  32. The minimum guaranteed amount one is willing to accept to
    avoid the risk associated with a gamble.
    Certainty Equivalent
  33. A number from 0 to 1 such that when α is close to 1, the decision criterion is optimistic, and when
    α is close to zero, the
    decision criterion is pessimistic.
    Coefficient of Realism (α)
  34. A course of action or a strategy that can be chosen
    by a decision maker.
    Decision Alternative
  35. A decision-making environment in which several outcomes can
    occur as a result of a decision or alternative. Probabilities of the outcomes
    are known.
    Decision Making Under Risk
  36. A decision-making environment in which several
    outcomes can occur. Probabilities of these outcomes, however, are not known
    Decision Making under Uncertainty
  37. A table in which decision alternatives are listed down
    the rows and outcomes are listed across the columns. The body of the table
    contain the payoff.
    Decision Table
  38. A ratio of the expected value of sample information and the
    expected value of perfect information.
    Efficiency of Sample Information
  39. The average or expected monetary outcome of a decision if it
    can be repeated many times. This is determined by multiplying the monetary
    outcomes by their respective probabilities. The results are then added to
    arrive at the EMV.
    Expected Monetary Value (EMV)
  40. The average or expected regret of a decision
    Expected Opportunity Loss (EOL)
  41. The average or expected value of information if it is
    completely accurate.
    Expected Value of Perfect Information (EVPI)
  42. The average or expected value of the decision if the
    decision maker knew what would happen ahead of time.
    Expected Value with Perfect Information (EVwPI):
  43. The average or expected value of imperfect or survey
    information
    Expected Value of Sample Information (EVSI)
  44. An optimistic decision-making criterion. This is the
    alternative with the highest possible return.
    Maximax
  45. A pessimistic decision-making criterion that maximizes
    the minimum outcome. It is the best of the worst possible outcomes
    Maximin
  46. A decision criterion that minimizes the maximum
    opportunity loss.
    Minimax Regret
  47. The amount you would lose by not picking the best
    alternative. For any outcome, this is the difference between the consequences
    of any alternative and the best possible alternative. Also called regret.
    Opportunity Loss
  48. : A person who avoids risk. As the monetary value
    increases on the utility curve, the utility increases at a decreasing rate.
    This decision maker gets less utility for a greater risk and higher potential
    returns
    Risk Avoider
  49. A person who is indifferent toward risk. The utility
    curve for a risk-neutral person is a straight line.
    Risk Neutral
  50. The monetary amount that a person is willing to give up in
    order to avoid the risk associated with a gamble.
    Risk Premium
  51. A person who seeks risk. As the monetary values
    increases on the utility curve, the utility increases at an increasing rate.
    This decision maker gets more pleasure for a greater risk and higher potential
    returns.
    Risk Seeker
  52. Decisions in which the outcome of one decision influences
    other decisions.
    Sequential Decisions
  53. A graph or curve that illustrates the relationship between
    utility and monetary values. When this curve has been constructed, utility
    values from the curve can be used in the decision-making process.
    Utility Curve
  54. A theory that allows decision makers to incorporate
    their risk preference and other factors into the decision-making process.
    Utility Theory
  55. A game in which the optimal strategy for both players
    involves playing more than one strategy over time. Each strategy is played a
    given percentage of the time
    Mixed Strategy game
  56. A game in which both players will always play just one
    strategy.
    Pure Strategy
  57. A game that has a pure strategy
    Saddle Point Game
  58. A game that has only two players
    Two-person Game
  59. The expected winning of the game if the game is played
    a large number of times
    Value of the Game
  60. A game in which the losses for one player equal the gains
    for the other player.
    Zero-sum Game
  61. A specific class of network models that involves
    determining the most efficient assignment of people to projects, salespeople to
    territories, contracts to bidders, jobs to machines and so on
    Assignment Model
  62. A problem that finds the maximum flow of any quantity
    or substances through a network
    Maximal Flow Model
  63. A model that determines the path through the network that
    connects all the nodes while minimizing total distance.
    Minimal-Spanning Tree Model
  64. A model that determines the shortest path or route
    through a network
    Shortest-Path Model
  65. : A specific case of network models that involves
    scheduling shipment from origins to destination so that total shipping costs
    are minimized.
    Transportation Model
  66. An extension of the transportation model in which some
    points have both flows in and out of them.
    Transshipment Model
  67. The population from which arrivals at the queuing system
    come. Also known as the calling population.
    Arrival Population
  68. The case in which arriving customer refuse to join the
    waiting line.
    Balking
  69. A probability distribution that is often used to describe
    random service times in a queuing system.
    Exponential Distribution
  70. A case in which the number of customers in the system is
    significant proportion of the calling population.
    Finite (or Limited) Population
  71. A queue that cannot increase beyond a specific size.
    Finite (or Limited) Queue Length
  72. A queue discipline in which the customers are served
    in the strict order of arrival.
    First-In First-Out (FIFO)
  73. : A system in which service is received from more than
    one station, one after the other.
    Multiphase System
  74. Descriptive characteristics of a queuing system,
    including the average number of customers in a line and in the system, the
    average waiting times in a line and in the system, and the percentage of idle
    time.
    Operating Characteristics
  75. A probability distribution that is often used to
    describe random arrivals in a queue.
    Poisson distribution
  76. One or more customers or units waiting to be served.
    Also called a waiting line.
    Queue
  77. The rule by which customer in a line receive services.
    Queue Discipline
  78. : The case in which customer enter a queue but then
    leave before being served.
    Reneging
  79. A queue discipline in which the customers are served
    in the strict order of arrival
    First-In First-Out (FIFO):
  80. A system in which service is received from more than
    one station, one after the other
    Multiphase System
  81. Descriptive characteristics of a queuing system,
    including the average number of customers in a line and in the system, the
    average waiting times in a line and in the system, and the percentage of idle
    time.
    Operating Characteristics
  82. A probability distribution that is often used to
    describe random arrivals in a queue
    Poisson distribution
  83. One or more customers or units waiting to be served.
    Also called a waiting line.
    Queue
  84. The rule by which customer in a line receive services
    Queue Discipline
  85. The case in which customer enter a queue but then
    leave before being served.
    Reneging
  86. The proportion of time that the service facility is in
    use
    Utilization Ratio (ρ)
  87. : A simulation model in which we need to keep track of the
    passage of time by using a simulation clock.
    Discrete-Event Simulation
  88. An excel function that can be used to randomly
    generate values from discrete general probability distributions.
    LOOKUP
  89. A simulation that experiments with probabilistic
    elements of a system by generating random numbers to create value for those
    elements.
    Monte Carlo Simulation
  90. An Excel function generates a random number between 0
    and 1 each time it is computed.
    RAND
  91. A number (typically between zero and one in most
    computer programs) whose values is selected completely at random.
    Random Number
  92. A single run of a simulation model. Also known as a
    run or trial
    Replication
  93. A technique that involves building a mathematical
    model to represent a real-world situation. The model is then experimented with
    to estimate the effects of various actions and decisions
    Simulation
  94. : A state then, when entered, cannot be left. The
    probability of going from an absorbing state to any other state is 0.
    Absorbing State
  95. A condition that exists when the state probabilities
    for a future period are the same as the state probabilities for a previous
    period.
    Equilibrium Condition
  96. A type of analysis that allows us to predict the
    future by using the state probabilities and the matrix of transition
    probabilities.
    Markov Analysis
  97. A matrix containing all transition probabilities for a
    certain process or system.
    Matrix of Transition Probabilities
  98. The probability of an event occurring at a point in
    time. Examples include the probability that a person will be shopping at a
    given grocery store during a given month
    State Probability
  99. The condition probability that we will be in a future
    state given a current or existing state.
    Transition Probability

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