Linear Programming: Model Formulation and …course1.winona.edu/mwolfmeyer/BA340/Chapt_02.pdfLinear...
Transcript of Linear Programming: Model Formulation and …course1.winona.edu/mwolfmeyer/BA340/Chapt_02.pdfLinear...
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Linear Programming: Model Formulation and
Graphical Solution
Chapter 2
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall
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Chapter Topics
Model Formulation
A Maximization Model Example
Graphical Solutions of Linear Programming Models
A Minimization Model Example
Irregular Types of Linear Programming Models
Characteristics of Linear Programming Problems
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Objectives of business decisions frequently involve maximizing profit or minimizing costs.
Linear programming uses linear algebraic relationships to represent a firm’s decisions, given a business objective, and resource constraints.
Steps in application:1. Identify problem as solvable by linear programming.2. Formulate a mathematical model of the unstructured
problem.3. Solve the model.4. Implementation
Linear Programming: An Overview
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Decision variables - mathematical symbols representing levels of activity of a firm.
Objective function - a linear mathematical relationship describing an objective of the firm, in terms of decision variables - this function is to be maximized or minimized.
Constraints – requirements or restrictions placed on the firm by the operating environment, stated in linear relationships of the decision variables.
Parameters - numerical coefficients and constants used in the objective function and constraints.
Model Components
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Summary of Model Formulation Steps
Step 1 : Clearly define the decision variables
Step 2 : Construct the objective function
Step 3 : Formulate the constraints
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LP Model FormulationA Maximization Example (1 of 4)
Product mix problem - Beaver Creek Pottery Company
How many bowls and mugs should be produced to maximize profits given labor and materials constraints?
Product resource requirements and unit profit:
Resource Requirements
ProductLabor
(Hr./Unit)Clay
(Lb./Unit)Profit
($/Unit)
Bowl 1 4 40
Mug 2 3 50
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LP Model FormulationA Maximization Example (2 of 4)
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LP Model FormulationA Maximization Example (3 of 4)
Resource 40 hrs of labor per dayAvailability: 120 lbs of clay
Decision x1 = number of bowls to produce per dayVariables: x2 = number of mugs to produce per day
Objective Maximize Z = $40x1 + $50x2Function: Where Z = profit per day
Resource 1x1 + 2x2 ≤ 40 hours of laborConstraints: 4x1 + 3x2 ≤ 120 pounds of clay
Non-Negativity x1 ≥ 0; x2 ≥ 0 Constraints:
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LP Model FormulationA Maximization Example (4 of 4)
Complete Linear Programming Model:
Maximize Z = $40x1 + $50x2
subject to: 1x1 + 2x2 ≤ 404x2 + 3x2 ≤ 120x1, x2 ≥ 0
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A feasible solution does not violate any of the constraints:
Example: x1 = 5 bowlsx2 = 10 mugsZ = $40x1 + $50x2 = $700
Labor constraint check: 1(5) + 2(10) = 25 < 40 hours Clay constraint check: 4(5) + 3(10) = 70 < 120 pounds
Feasible Solutions
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An infeasible solution violates at least one of the constraints:
Example: x1 = 10 bowlsx2 = 20 mugsZ = $40x1 + $50x2 = $1400
Labor constraint check: 1(10) + 2(20) = 50 > 40 hours
Infeasible Solutions
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Graphical solution is limited to linear programming models containing only two decision variables (can be used with three variables but only with great difficulty).
Graphical methods provide visualization of how a solution for a linear programming problem is obtained.
Graphical Solution of LP Models
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Coordinate AxesGraphical Solution of Maximization Model (1 of 12)
Figure 2.2 Coordinates for Graphical Analysis
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
X1 is bowls
X2 is mugs
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Labor ConstraintGraphical Solution of Maximization Model (2 of 12)
Figure 2.3 Graph of Labor Constraint
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Labor Constraint AreaGraphical Solution of Maximization Model (3 of 12)
Figure 2.4 Labor Constraint Area
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Clay Constraint AreaGraphical Solution of Maximization Model (4 of 12)
Figure 2.5 Clay Constraint Area
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Both ConstraintsGraphical Solution of Maximization Model (5 of 12)
Figure 2.6 Graph of Both Model Constraints
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Feasible Solution AreaGraphical Solution of Maximization Model (6 of 12)
Figure 2.7 Feasible Solution Area
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Objective Function Solution = $800Graphical Solution of Maximization Model (7 of 12)
Figure 2.8 Objection Function Line for Z = $800
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Alternative Objective Function Solution LinesGraphical Solution of Maximization Model (8 of 12)
Figure 2.9 Alternative Objective Function Lines
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Optimal SolutionGraphical Solution of Maximization Model (9 of 12)
Figure 2.10 Identification of Optimal Solution Point
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Optimal Solution CoordinatesGraphical Solution of Maximization Model (10 of 12)
Figure 2.11 Optimal Solution Coordinates
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Extreme (Corner) Point SolutionsGraphical Solution of Maximization Model (11 of 12)
Figure 2.12 Solutions at All Corner Points
Maximize Z = $40x1 + $50x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
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Optimal Solution for New Objective FunctionGraphical Solution of Maximization Model (12 of 12)
Maximize Z = $70x1 + $20x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
Figure 2.13 Optimal Solution with Z = 70x1 + 20x2
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Standard form requires that all constraints be in the form of equations (equalities).
A slack variable is added to a ≤ constraint (weak inequality) to convert it to an equation (=).
A slack variable typically represents an unused resource.
A slack variable contributes nothing to the objective function value.
Slack Variables
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Linear Programming Model: Standard Form
Max Z = 40x1 + 50x2 + s1 + s2subject to:1x1 + 2x2 + s1 = 40
4x2 + 3x2 + s2 = 120x1, x2, s1, s2 ≥ 0
Where:
x1 = number of bowlsx2 = number of mugss1, s2 are slack variables
Figure 2.14 Solution Points A, B, and C with Slack
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LP Model Formulation – Minimization (1 of 8)
Chemical Contribution
Brand Nitrogen (lb/bag)
Phosphate (lb/bag)
Super-gro 2 4
Crop-quick 4 3
Two brands of fertilizer available - Super-gro, Crop-quick.
Field requires at least 16 pounds of nitrogen and 24 pounds of phosphate.
Super-gro costs $6 per bag, Crop-quick $3 per bag.
Problem: How much of each brand to purchase to minimize total cost of fertilizer given following data ?
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LP Model Formulation – Minimization (2 of 8)
Figure 2.15 Fertilizing farmer’s field
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Decision Variables:x1 = bags of Super-grox2 = bags of Crop-quick
The Objective Function:Minimize Z = $6x1 + 3x2Where: $6x1 = cost of bags of Super-Gro
$3x2 = cost of bags of Crop-Quick
Model Constraints:2x1 + 4x2 ≥ 16 lb (nitrogen constraint)4x1 + 3x2 ≥ 24 lb (phosphate constraint)x1, x2 ≥ 0 (non-negativity constraint)
LP Model Formulation – Minimization (3 of 8)
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Minimize Z = $6x1 + $3x2subject to: 2x1 + 4x2 ≥ 16
4x2 + 3x2 ≥ 24x1, x2 ≥ 0
Figure 2.16 Graph of Both Model Constraints
Constraint Graph – Minimization (4 of 8)
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Figure 2.17 Feasible Solution Area
Feasible Region– Minimization (5 of 8)
Minimize Z = $6x1 + $3x2subject to: 2x1 + 4x2 ≥ 16
4x2 + 3x2 ≥ 24x1, x2 ≥ 0
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Figure 2.18 Optimum Solution Point
Optimal Solution Point – Minimization (6 of 8)
Minimize Z = $6x1 + $3x2subject to: 2x1 + 4x2 ≥ 16
4x2 + 3x2 ≥ 24x1, x2 ≥ 0
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A surplus variable is subtracted from a ≥ constraint to convert it to an equation (=).
A surplus variable represents an excess above a constraint requirement level.
A surplus variable contributes nothing to the calculated value of the objective function.
Subtracting surplus variables in the farmer problem constraints:
2x1 + 4x2 - s1 = 16 (nitrogen)4x1 + 3x2 - s2 = 24 (phosphate)
Surplus Variables – Minimization (7 of 8)
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Figure 2.19 Graph of Fertilizer Example
Graphical Solutions – Minimization (8 of 8)
Minimize Z = $6x1 + $3x2 + 0s1 + 0s2subject to: 2x1 + 4x2 – s1 = 16
4x2 + 3x2 – s2 = 24x1, x2, s1, s2 ≥ 0
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For some linear programming models, the general rules do not apply.
Special types of problems include those with:
Multiple optimal solutions
Infeasible solutions
Unbounded solutions
Irregular Types of Linear Programming Problems
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Figure 2.20 Example with Multiple Optimal Solutions
Multiple Optimal Solutions Beaver Creek Pottery
The objective function is parallel to a constraint line.
Maximize Z=$40x1 + 30x2subject to: 1x1 + 2x2 ≤ 40
4x2 + 3x2 ≤ 120x1, x2 ≥ 0
Where:x1 = number of bowlsx2 = number of mugs
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An Infeasible Problem
Figure 2.21 Graph of an Infeasible Problem
Every possible solution violates at least one constraint:
Maximize Z = 5x1 + 3x2subject to: 4x1 + 2x2 ≤ 8
x1 ≥ 4x2 ≥ 6x1, x2 ≥ 0
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An Unbounded Problem
Figure 2.22 Graph of an Unbounded Problem
Value of the objectivefunction increases indefinitely:
Maximize Z = 4x1 + 2x2subject to: x1 ≥ 4
x2 ≤ 2x1, x2 ≥ 0
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Characteristics of Linear Programming Problems
A decision amongst alternative courses of action is required. The decision is represented in the model by decision variables. The problem encompasses a goal, expressed as an objective
function, that the decision maker wants to achieve. Restrictions (represented by constraints) exist that limit the
extent of achievement of the objective. The objective and constraints must be definable by linear
mathematical functional relationships.
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Proportionality - The rate of change (slope) of the objective function and constraint equations is constant.
Additivity - Terms in the objective function and constraint equations must be additive.
Divisibility -Decision variables can take on any fractional value and are therefore continuous as opposed to integer in nature.
Certainty - Values of all the model parameters are assumed to be known with certainty (non-probabilistic).
Properties of Linear Programming Models
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Problem StatementExample Problem No. 1 (1 of 3)
■ Hot dog mixture in 1000-pound batches.
■ Two ingredients, chicken ($3/lb) and beef ($5/lb).
■ Recipe requirements:
at least 500 pounds of “chicken”
at least 200 pounds of “beef ”
■ Ratio of chicken to beef must be at least 2 to 1.
■ Determine optimal mixture of ingredients that will minimize costs.
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Step 1:
Identify decision variables.
x1 = lb of chicken in mixture
x2 = lb of beef in mixture
Step 2:
Formulate the objective function.
Minimize Z = $3x1 + $5x2where Z = cost per 1,000-lb batch$3x1 = cost of chicken$5x2 = cost of beef
SolutionExample Problem No. 1 (2 of 3)
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Step 3:
Establish Model Constraintsx1 + x2 = 1,000 lbx1 ≥ 500 lb of chickenx2 ≥ 200 lb of beef
x1/x2 ≥ 2/1 or x1 - 2x2 ≥ 0x1, x2 ≥ 0
The Model: Minimize Z = $3x1 + 5x2subject to: x1 + x2 = 1,000 lb
x1 ≥ 50x2 ≥ 200 x1 - 2x2 ≥ 0x1,x2 ≥ 0
SolutionExample Problem No. 1 (3 of 3)
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Solve the following model graphically:Maximize Z = 4x1 + 5x2subject to: x1 + 2x2 ≤ 10
6x1 + 6x2 ≤ 36x1 ≤ 4x1, x2 ≥ 0
Step 1: Plot the constraintsas equations
Example Problem No. 2 (1 of 3)
Figure 2.23 Constraint Equations
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Example Problem No. 2 (2 of 3)
Maximize Z = 4x1 + 5x2subject to: x1 + 2x2 ≤ 10
6x1 + 6x2 ≤ 36x1 ≤ 4x1, x2 ≥ 0
Step 2: Determine the feasible solution space
Figure 2.24 Feasible Solution Space and Extreme Points
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Example Problem No. 2 (3 of 3)
Maximize Z = 4x1 + 5x2subject to: x1 + 2x2 ≤ 10
6x1 + 6x2 ≤ 36x1 ≤ 4x1, x2 ≥ 0
Step 3 and 4: Determine the solution points and optimal solution
Figure 2.25 Optimal Solution Point
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