# Ch 3 Introduction to Linear Programming By Kanchala Sudtachat.

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Ch 3 Introduction to Linear Programming By Kanchala Sudtachat

What is Linear programming? A mathematical program has objective and constraints Linear programming: An optimization problem whose objective function and constraints are linear. So, what is a linear relationship? (1) the function is a sum of terms (2) each term of the function has at most one decision variable (multiplied by a constant). Example: Linear?

Linear Function? Implications of linear relationships 1.Constant contribution of every decision variable. 2. The contribution of each decision variable is additive. Because of these (and some other) reasons, computers can solve big problems fast if they are linear programs What is Linear programming?

Step 1: Identify Decision Variables: Step 2: Determine Objective function: Step 3: Determine Constraints: Step 4: Sign Restrictions: LP “four”mulation

Things to keep in mind: (1)Objective and constraints must be linear functions (2)Decision variables are continuous (non-integer), and in solution may take on fractional values (3)Coefficients must be deterministic constants LP formulation

Ex1 Giapetto’s Woodcarving

3.2 The Graphical Solution of Two-variable LP

Binding and Nonbinding Constraints

The Graphical Solution to Minimization Problems  Ex 2 Dorian Auto

Ex 2 Dorian Auto

3.3 Special Cases  We encounter three types of LPs that do not have unique optimal solutions. 1. Some LPs have an infinite number of optimal solutions (alternative or multiple optimal solutions). 2. Some LPs have no feasible solutions (infeasible LPs) 3. Some LPs are unbounded: There are points in the feasible region with arbitrarily large (in a max problem) z-values.

Ex 3 Alternative or Multiple Optimal Solutions

Ex 4 Infeasible LP

Ex 5 Unbounded LP

Every LP with two variables (4 cases)  Case 1: The LP has a unique optimal solution.  Case 2: The LP has alternative or multiple optimal solution  Case 3: The LP is infeasible: the feasible region contains no points.  Case 4: The LP is unbounded: There are points in the feasible region with arbitrarily large z- values (max problem or arbitrarily small z-values (min problem)

3.4 A Diet Problem

3.5 A Work-Scheduling Problem  Ex 7 Post Office Problem

3.8 Blending Problems  Ex 12: Oil Blending

3.9 Production Process Models  Ex 13 Brute Production Process

Ex 14 Sailco Inventory

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