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To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ 07458 Chapter 9 Linear.

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Presentation on theme: "To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ 07458 Chapter 9 Linear."— Presentation transcript:

1 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 9 Linear Programming: The Simplex Method The Simplex Method

2 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-2 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Learning Objectives Students will be able to Convert LP constraints to equalities with slack, surplus, and artificial variables. Set up and solve both maximization and minimization LP problems with simplex tableaus. Interpret the meaning of every number in a simplex tableau.

3 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-3 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Learning Objectives - continued Students will be able to Recognize cases of infeasibility, unboundedness, degeneracy, and multiple optimal solutions in a simplex output. Understand the relationship between the primal and dual and when to formulate and use the dual.

4 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-4 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter Outline 9.1 Introduction 9.2 How to Set Up the Initial Solution 9.3 Simplex Solution Procedures 9.4 The Second Simplex Tableau 9.5 Developing the Third Simplex Tableau

5 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-5 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter Outline - continued 9.6 Review of Procedures for Solving LP Maximization Problems 9.7 Surplus and Artificial Variables 9.8 Solving Minimization Problems 9.9 Review of Procedures for Solving LP Minimization Problems 9.10 Special Cases

6 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-6 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Flair Furniture Company Maximize: Objective:   XX  Hours Required to Produce One Unit Department X 1 Tables X 2 Chairs Available Hours This Week Carpentry Painting/Varnishing Profit/unit Constraints: $7$5 )varnishing & (painting    XX )(carpentry    XX

7 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-7 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Flair Furniture Company's Feasible Region & Corner Points Number of Chairs X X2X2 Number of Tables B = (0,80) C = (30,40) D = (50,0) Feasible Region    XX    XX

8 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-8 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Flair Furniture - Adding Slack Variables )varnishing & (painting    XX )(carpentry    XX Constraints: Constraints with Slack Variables ) varnishing & (painting )(carpentry       S XX SXX   XX  Objective Function Objective Function with Slack Variables   SSXX 

9 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-9 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Flair Furniture’s Initial Simplex Tableau Profit per Unit Column Prod. Mix Column Real Variables Columns Slack Variables Columns Constant Column CjCj Solution Mix X1X1 X2X2 S1S1 S2S2 Quantity $7$5$0 Profit per unit row $0 $7$5$0 S1S1 S2S2 ZjZj C j - Z j $0 Constraint equation rows Gross Profit row Net Profit row

10 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-10 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Pivot Row, Pivot Number Identified in the Initial Simplex Tableau CjCj Solution Mix X1X1 X2X2 S1S1 S2S2 Quantity $7$5$ $7$5$0 S1S1 S2S2 ZjZj C j - Z j $0 Pivot row Pivot number Pivot column

11 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-11 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Completed Second Simplex Tableau for Flair Furniture CjCj Solution Mix X1X1 X2X2 S1S1 S2S2 Quantity $7$5$0 11/ $7$7/2 $0 $3/2-$7/2$0 $7 $0 X1X1 S2S2 ZjZj C j - Z j $350

12 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-12 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Pivot Row, Column, and Number Identified in Second Simplex Tableau CjCj Solution Mix X1X1 X2X2 S1S1 S2S2 Quantity $7$5$0 11/ $7$7/2 $0 $3/2-$7/2$0 $7 $0 X1X1 S2S2 ZjZj C j - Z j $350 (Total Profit) Pivot row Pivot number Pivot column

13 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-13 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Calculating the New X 1 Row for Flair’s Third Tableau =-x 1 0 3/2 -1/ / (1/2)(1/2) (0) (1) (-2) (1) (40) =-x =-x =-x =-x

14 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-14 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Final Simplex Tableau for the Flair Furniture Problem CjCj Solution Mix X1X1 X2X2 S1S1 S2S2 Quantity $7$5$0 103/2-1/ $75$1/2$3/2 $0 -$1/2-$3/2 $7 $5 X1X1 X2X2 ZjZj C j - Z j $410

15 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-15 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Simplex Steps for Maximization 1. Choose the variable with the greatest positive C j - Z j to enter the solution. 2. Determine the row to be replaced by selecting that one with the smallest (non-negative) quantity-to-pivot- column ratio. 3. Calculate the new values for the pivot row. 4. Calculate the new values for the other row(s). 5. Calculate the C j and C j - Z j values for this tableau. If there are any C j - Z j values greater than zero, return to Step 1.

16 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-16 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Surplus & Artificial Variables Constraints Constraints-Surplus & Artificial Variables       XX XXX       AXX ASXXX Objective Function   XXX  :Min   MA SXXX  :Min Objective Function-Surplus & Artificial Variables

17 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-17 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Simplex Steps for Minimization 1. Choose the variable with the greatest negative C j - Z j to enter the solution. 2. Determine the row to be replaced by selecting that one with the smallest (non-negative) quantity-to-pivot- column ratio. 3. Calculate the new values for the pivot row. 4. Calculate the new values for the other row(s). 5. Calculate the C j and C j - Z j values for this tableau. If there are any C j - Z j values less than zero, return to Step 1.

18 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-18 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Special Cases Infeasibility 02M +21 M C j - Z j M M 31 - M -285ZjZj A2A2 M X2X X1X1 5 QtyA2A2 A1A1 S2S2 S1S1 X2X2 X1X1 Sol Mix MM0085CjCj

19 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-19 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Special Cases Unboundedness Pivot Column CjCj 6900 Sol Mix X1X1 X2X2 S1S1 S2S2 Qty X1X S1S ZjZj C j - Z j

20 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-20 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Special Cases Degeneracy Pivot Column C j Solution Mix X 1 X 2 X 3 S 1 S 2 S 3 Qty 8X 2 1/ S 2 401/ S /50110 Z j C j -Z j

21 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-21 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Special Cases Multiple Optima CjCj 3200 Sol Mix X1X1 X2X2 S1S1 S2S2 Qty 2X1X1 3/ S2S2 101/213 ZjZj C j - Z j 00-20

22 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-22 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Sensitivity Analysis High Note Sound Company          XX XX X X :toSubject :Max

23 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-23 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Sensitivity Analysis High Note Sound Company

24 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-24 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Simplex Solution High Note Sound Company CjCj Sol Mix X1X1 X2X2 S1S1 S2S2 Qty 120X2X2 1/211/4020 0S2S2 5/20-1/4140 ZjZj C j - Z j

25 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-25 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Nonbasic Objective Function Coefficients CjCj Sol Mix X1X1 X2X2 S1S1 S2S2 Qty 120X2X2 1/211/4020 0S2S2 5/20-1/4140 ZjZj C j – Z j

26 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-26 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Basic Objective Function Coefficients CjCj Sol Mix X1X1 X2X2 S1S1 S2S2 Qty  X1X1 1/211/4020 0S2S2 5/20-1/4140 ZjZj 60+  /  30+  /  C j - Z j -10-  /  /4 0

27 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-27 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Simplex Solution High Note Sound Company Objective increases by 30 if 1 additional hour of electricians time is available. CjCj Sol Mix X1X1 X2X2 S1S1 S2S2 Qty X1X1 ½11/4020 S2S2 5/20- 1/4 140 ZjZj C j - Z j

28 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-28 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Shadow Prices Shadow Price: Value of One Additional Unit of a Scarce Resource Found in Final Simplex Tableau in C-Z Row Negatives of Numbers in Slack Variable Column

29 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-29 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Steps to Form the Dual To form the Dual: If the primal is max., the dual is min., and vice versa. The right-hand-side values of the primal constraints become the objective coefficients of the dual. The primal objective function coefficients become the right-hand-side of the dual constraints. The transpose of the primal constraint coefficients become the dual constraint coefficients. Constraint inequality signs are reversed.

30 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-30 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Primal & Dual Primal:Dual       XX XX Subject to:       UU UU Subject to:    XX :Max    UU :Min

31 To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 9-31 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Comparison of the Primal and Dual Optimal Tableaus Primal’s Optimal Solution


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