Model
any simplification of reality that captures the important characteristics/details
Profit model
π = selling price x quantity - ((variable cost x quantity) + fixed costs)
= contribution per unit x quantity - fixed costs
breakeven point
when profit (π) = 0
Five steps in Decision Analysis
Define the problem
List all possible alternatives
identify the possible outcomes for each decision alternative
identify the payoff for each combination
select a decision analysis modeling technique, apply, and make decision
components of decision making problem
States of nature
decision alternatives
outcome/payoff
States of nature
events that occur in the future
we don’t have control over them
Decision alternatives
have ultimate control the decision made
Outcome
combination of a state and a decision; payoff
maximax
the criterion where a decision maker selects the decision that will result in the maximum of the maximum payoffs
the best of the best
optimistic
accept more risk
maximin
the criterion where a decision maker selects the decision that will result in the maximum of the minimum payoffs
the best of the worst
pessimistic
risk averse
minimax regret
the criterion where a decision maker selects the decision that will result in the minimum regret
Hurwicz
use alpha, coefficient of optimism to weight best and worst payoffs
Expected value of perfect information (EVwPI)
= expected value under certainty - maximum expected value
Decision trees
a visual representation of the states, decisions, and outcomes of a sequential decision problem
chance node, decision node, branches
decision node
defined by a square, denotes when a decision maker must make a choice or a decision
chance node
defined by a circle, represents an event or state of nature that can occur in the future
branch
defined by a line, represents decision alternatives and/or states of nature, and links decisions and/or states
how to solve a decision tree?
create right → left
solve left → right
EOQ
linear programming assumptions
linear programming 3 parts
the objective function
constraints
non-negativity assumptions
decision variables
Symbols used to represent an item of interest to the user that can take on any value
parameters
Known constant values that are defined for each problem
objective function
a linear relationship of decision variables describing the problem objective
what you’re trying to do
maximize profit or minimize cost
constraints
physical limitations or restrictions on the LP problem
feasible region
The region mapped by the constraints containing values of the decision variables that are allowed by the constraint set
corner point
a point where set of constraints intersect
corner point feasible solution
A point where a set of constraints intersect that is on the edge of the feasible region.