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
Max. 40 F  30 S
2
1
S . t.
F  S  20
5
2
1
S 5
5
3
3
F  S  21
5
10
F, S  0
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
Constraints
Value of objective
function at OSP.
Values of decision variables at
optimal solution point. (Leave blank.)
Contributions to
objective function
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