Chap 1-2 Flashcards
Decision making
The process of defining the problem, identifying the alternatives, determining the criteria, evaluating alternatives, and choosing an alternative
Problem solving
The process of identifying a difference between the actual and the desired state if affairs and then taking action to resolve the difference
Single-criterion decision problems
A problem in which the objective is to find the “best” solution with respect to just one criterion
Multi criteria decision problem
A problem that involves more than one criterion; the objective is to find the “best” solution, taking into account all the criteria
Decision
The alternative selected
Model
A representation if a real object or situation
Simplified!!!!
Iconic model
A physical replica, or representation, of a real object
Analog model
Although physical in form, an analog model does not have a physical appearance similar to the real object or situation it represents
Mathematical model
Mathematical symbols and expressions used to represent a real situation
Constraints
Restrictions or limitations imposed on a problem
Objective function
A mathematical expression that describes the problems objective
Uncontrollable inputs
The environmental factors or inputs that cannot be controlled. To the decision maker
Variables in front of x and y
The number that can’t be less than or more than
Controllable inputs
The inputs that are controlled or determined by the decision maker
Controllable inputs are the decision variables: x and y
Decision variables
Another term for controllable inputs
Deterministic model
A model in which all uncontrollable inputs are known and cannot vary
Stochastic (probabilistic) model
A model I which at least one uncontrollable input is uncertain and subject to variation
Management science
An approach to decision making based on scientific method, makes extensive use of quantitative analysis. Also called operations research, and decision science
Problem solving process
1) Identify and define the problem
2) Determine the set of alternative solutions
3) determine the criteria that will be used to evaluate the alternatives
4) evaluate the alternatives
5) choose an alternative
6) implement the selected alternative
7) evaluate the results to determine whether a satisfactory solution has been obtained
Difference between problem solving and decision making
Decision making is first 5 steps of problem solving- just deciding what decision to make
Problem solving is the last 2 steps of problem solving- implementing the decision and finding the results
Qualitative data
Based subjectively
Based through words, observations, and opinions
Can be turned into a numbers game through scales (1-10)
Quantitative data
Based objectively
Based on numbers and data analysis
Why do we need qualitative and quantitate data when making managerial decisions?
You can turn anything into a scale with numbers
Without qualitative data you can’t know what those numbers mean though
Doctor example- which doc is better?
Optimal solution
The specific decision-variable value(s) that provide the “best” output for the model
Infeasible solution / infeasibility
A decision alternative or solution that does not satisfy one or more constraint
Not in shaded area
Feasible solution
A decision alternative or solution that satisfies all constraints
In shaded area
Fixed cost
The portion of the total cost that does not depend on the volume; it’s cost remains the same no matter how much is produced
Variable cost
The portion of the total cost that is dependent on and varies with the volume
Marginal cost
The rate of change of the total cost with respect to volume
Marginal revenue
The rate of change of total revenue with respect to volume
Break even point
The volume at which total revenue equals total cost
Constraint
An equation or inequality that rules out certain combinations of decision variables as feasible solutions
Problem formation
The process of translating the verbal statement if a problem I to a mathematical statement called the mathematical model
Decision variable
A controllable input for a linear programming model
Nonnegativity constraints
A set if constraints that requires all variables to be nonnegatives
x,y>=0
Mathematical model
A representation if a problem where the object and all constraint conditions are described by mathematical expressions
Linear programming model
A mathematical model with a linear objective function, a set if linear constraints, and nonnegative variables
Linear program
Another term for linear programming model
Linear functions
Mathematical expressions in which the variables appear in separate terms an are raised to the first power
Straight fucking line
Feasible region
The set of all feasible solutions
All of the shaded shit
Slack variables
A variable added to the left-hand side of the less-than-or-equal-to constraint to convert the constraint into an equality. The value of the variable can usually be interpreted as the amount of unused resources
Standard form
A linear program I. Which all the constraints are written as equalities. The optimal solution of the standard form if a linear program is the same as the optimal solution of the original formulation if the linear program
Redundant constraint
A constraint that does not affect the feasible region. If a constraint is redundant, it can be dismissed
Delete that mother fucker
Extreme point
Gradually speaking, extreme points are the feasible solution points occurring at the vertices or “corners” of the feasible region
Determine the intersection of the two god damn lines
Surplus variable
A variable subtracted from the left-hand side of a greater-than-or-equal-to constraint to convert the constraint into an equality. The value of this variable can usually interpreted as the amount over and above some required minimum level
Alternative optimal solutions
The case in which more than one solution provides the optimal value for the objective function
Unbounded
If the value of the solution may be made infinitely large in a maximization linear programming problem or infinitely small in a minimization problem without violating any of the constraints, the problem is said to be unbounded
