INFM 718A / LBSC 705 Information For Decision Making

Lecture 4

Overview

• Linear Programming Recap– Modeling– Graphical Solution Approach– In-Class Exercises 2.2 and 2.3

• Excel Solution Approach (Solver)– Maximization Example– In-Class Exercises 2.1, 2.2 and 2.3 in Solver– Minimization Example

Linear Programming

• Decision models that involve decision variables whose feasible values are bounded by a set of constraints, aiming to maximize utility/profit, or minimize loss/cost.

Modeling LP Problems

• What are the decision variables?

• What is the goal (max./min.)?

• Maximize/Minimize what?

• What are the constraints?

Modeling

0,

2110

3

5

3

55

1

202

1

5

2..

3040.

SF

SF

S

SFtS

SFMax

Graphical Solution

Graphical Solution

• Solve for inequalities that intersect at the Optimal Solution Point.

In-Class Exercises

• 2.2

• 2.3

Excel Approach (Solver)

• Build a spreadsheet representation of the model.

• Define the target cell, max./min and constraints in Solver

• Let Solver solve.

Spreadsheet Representation

Decision variables

Values of decision variables atoptimal solution point. (Leave blank.)

Constraints

Contributions to objective function

Value of objectivefunction at OSP.

Cells in red type are formulas; other cell values are entered manually.

Solver Definition

Solver Definition

Solver Definition

Solver Definition

Let Solver Solve

Solver Solution

Exercises

• Solve the following using Solver:– Maximization Example– In-Class Exercises 2.1, 2.2, and 2.3– Minimization Example