BUSOBA 2321 Exam 1

The Ohio State University

Model

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

1. Define the problem

2. List all possible alternatives

3. identify the possible outcomes for each decision alternative

4. identify the payoff for each combination

5. select a decision analysis modeling technique, apply, and make decision

components of decision making problem

1. States of nature

2. decision alternatives

3. 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

1. the objective function

2. constraints

3. 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.