© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 1 The Simplex Method Pivoting, an Animation.

Slides:



Advertisements
Similar presentations
February 14, 2002 Putting Linear Programs into standard form
Advertisements

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Lecture 18 Slide 1 Network Models Lecture 18 The Transportation Algorithm II.
Chapter 5: Linear Programming: The Simplex Method
Lecture 3 Linear Programming: Tutorial Simplex Method
Operation Research Chapter 3 Simplex Method.
© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Lecture 17v Slide 1 Network Models Lecture 17 (Part v.) Vogel’s Approximation.
SIMPLEX METHOD FOR LP LP Model.
Assignment (6) Simplex Method for solving LP problems with two variables.
Nonstandard Problmes Produced by E. Gretchen Gascon.
LECTURE 14 Minimization Two Phase method by Dr. Arshad zaheer
© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Lecture 17n. Slide 1 Network Models Lecture 17 (part n.) The Northwest Corner.
© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Lecture 17l. Slide 1 Network Models Lecture 17 (Part l.) The Least Cost Starting.
Chapter 6 Linear Programming: The Simplex Method
Dr. Sana’a Wafa Al-Sayegh
Sections 4.1 and 4.2 The Simplex Method: Solving Maximization and Minimization Problems.
1. The Simplex Method for Problems in Standard Form 1.
Linear Inequalities and Linear Programming Chapter 5
The Simplex Method: Standard Maximization Problems
5.4 Simplex method: maximization with problem constraints of the form
Operation Research Chapter 3 Simplex Method.
Minimization by Dr. Arshad zaheer
LINEAR PROGRAMMINGExample 1 MaximiseI = x + 0.8y subject tox + y  x + y  x + 2y  2400 Initial solution: I = 0 at (0, 0)
5.6 Maximization and Minimization with Mixed Problem Constraints
The Simplex Procedure Daniel B. Taylor AAEC 5024 Department of Agricultural and Applied Economics Virginia Tech.
MIT and James Orlin © Chapter 3. The simplex algorithm Putting Linear Programs into standard form Introduction to Simplex Algorithm.
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
LINEAR PROGRAMMING SIMPLEX METHOD.
Learning Objectives for Section 6.2
Simplex Linear Programming I. Concept II. Model Template III. Class Example IV. Procedure V. Interpretation MAXIMIZATION METHOD Applied Management Science.
Chapter 6 Linear Programming: The Simplex Method
8. Linear Programming (Simplex Method) Objectives: 1.Simplex Method- Standard Maximum problem 2. (i) Greedy Rule (ii) Ratio Test (iii) Pivot Operation.
EE/Econ 458 The Simplex Method using the Tableau Method
Chapter 6 Linear Programming: The Simplex Method Section 2 The Simplex Method: Maximization with Problem Constraints of the Form ≤
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Learning Objectives for Section 6.4 The student will be able to set up and solve linear programming problems.
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
Kerimcan OzcanMNGT 379 Operations Research1 Linear Programming: The Simplex Method Chapter 5.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
The Simplex Method Updated 15 February Main Steps of the Simplex Method 1.Put the problem in Row-Zero Form. 2.Construct the Simplex tableau. 3.Obtain.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Public Policy Modeling Simplex Method Tuesday, October 13, 2015 Hun Myoung Park, Ph.D. Public Management & Policy Analysis Program Graduate School of International.
The big M method LI Xiao-lei.
Simplex Method Adapting to Other Forms.  Until now, we have dealt with the standard form of the Simplex method  What if the model has a non-standard.
Mechanical Engineering Department 1 سورة النحل (78)
This presentation shows how the tableau method is used to solve a simple linear programming problem in two variables: Maximising subject to three  constraints.
Part 4 Nonlinear Programming 4.5 Quadratic Programming (QP)
1 1 Slide © 2005 Thomson/South-Western Linear Programming: The Simplex Method n An Overview of the Simplex Method n Standard Form n Tableau Form n Setting.
Chapter 4 Linear Programming: The Simplex Method
Chapter 6 Linear Programming: The Simplex Method Section 4 Maximization and Minimization with Problem Constraints.
THE SIMPLEX ALGORITHM Step 1 The objective row is scanned and the column containing the most negative term is selected (pivotal column) - indicate with.
1 1 Slide © 2005 Thomson/South-Western Simplex-Based Sensitivity Analysis and Duality n Sensitivity Analysis with the Simplex Tableau n Duality.
Gomory Cuts Updated 25 March Example ILP Example taken from “Operations Research: An Introduction” by Hamdy A. Taha (8 th Edition)“Operations Research:
Linear Inequalities and Linear Programming Chapter 5 Dr.Hayk Melikyan/ Department of Mathematics and CS/ 5.5 Dual problem: minimization.
Simplex Method for solving LP problems with two variables.
Simplex method : Tableau Form
 LP graphical solution is always associated with a corner point of the solution space.  The transition from the geometric corner point solution to the.
Copyright © 2006 Brooks/Cole, a division of Thomson Learning, Inc. Linear Programming: An Algebraic Approach 4 The Simplex Method with Standard Maximization.
Simplex Method Review. Canonical Form A is m x n Theorem 7.5: If an LP has an optimal solution, then at least one such solution exists at a basic feasible.
Decision Support Systems INF421 & IS Simplex: a linear-programming algorithm that can solve problems having more than two decision variables.
GOOD MORNING CLASS! In Operation Research Class, WE MEET AGAIN WITH A TOPIC OF :
The Simplex Method. and Maximize Subject to From a geometric viewpoint : CPF solutions (Corner-Point Feasible) : Corner-point infeasible solutions 0.
5.5 Dual problem: minimization with problem constraints of the form Associated with each minimization problem with constraints is a maximization problem.
Linear programming Simplex method.
Chapter 5 Simplex-Based Sensitivity Analysis and Duality
Chapter 4 Linear Programming: The Simplex Method
St. Edward’s University
Linear programming Simplex method.
LINEAR PROGRAMMING Example 1 Maximise I = x + 0.8y
Part 4 Nonlinear Programming
THE SIMPLEX ALGORITHM Step 1
Presentation transcript:

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 1 The Simplex Method Pivoting, an Animation

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 2 Use the down pointer key to move forward in the animation and the up pointer key to backtrack. Assume the Following Initial Basic Feasible Tableau for a Maximization Problem.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 3 Step 1. Identify the Entering Variable. Step 1, What is the Entering Variable? Rule 1, In a Maximization, the Entering Variable is the one with the most +ve (Cj-Zj) value! So, x2 is the Entering Variable as 20 is the most +ve (Cj-Zj) value! The x2 Column is the “Pivot Column” for this Pivot!

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 4 Step 2. Identify the Leaving Variable. Step 2, What is the Leaving Variable? Rule 2, the Leaving Variable is the one with the least +ve Ratio Test result! Take the Bi value in each row and divide it by the value in the row in the Pivot Column. Top row B1 = 200. Value in the pivot column is 2, so the Ratio Test value is 200/2 or 100. Middle row B2 = 72. Value in the pivot column is 0.6, so the Ratio Test value is 72/0.6 or 120. Bottom row B3 = 180. Value in the pivot column is 0.6, so the Ratio Test value is 180/0.6 or 300. So, the minimum Ratio Test value is 100 and S1 is the Leaving Variable.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 5 Naming of Parts The x2 Column is the “Pivot Column”, the S1 Row is the “Pivot Row” and the orange box is the “Pivot Element”.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 6 The Pivot Row We need the Pivot Row to have a 1 in the Pivot Column. To achieve this we will divide all the values in the Pivot Row (except the Cj on the left)! by the value in the Pivot Element.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 7 The Second Row We need the Second Row to have a 0 in the Pivot Column.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 8 The Second Row Multiply the Pivot Row (somewhere off the Tableau!!) by the value in the Pivot Column and Row 2. AND then subtract the result from row 2.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 9 The Third Row We need the Third Row to have a 0 in the Pivot Column.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 10 The Third Row Multiply the Pivot Row (somewhere off the Tableau!!) by the value in the Pivot Column and Row 3. AND then subtract the result from row 3.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 11 Resetting and Recalculating Replace S1 with x2.Update the Cj col. Calculate the Zj row.And the Cj-Zj.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 12 The New Entering and Leaving Variables x1’s (Cj-Zj) value is the largest +ve value so x1 is the Entering Variable and s2 is the leaving variable because its Ratio Test value is the min +ve value.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 13 Running The Next Pivot Should Get You to Here!! The optimal solution is x1 = , x2 = , s3 = , s2 = 0, s1 = 0 and Z = 2,189.5.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 14 Pivoting In a Minimization The only difference is in the choice of the Entering Variable, which involves selecting the variable with the most negative (Cj-Zj) value! In all other ways the procedure is the same.

© Copyright Andrew Hall, 2002 FOMGT 353 Introduction to Management Science Pivoting Slide 15 The End Use the ESC key to exit!