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.

Slides:



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

Chapter 5: Linear Programming: The Simplex Method
Operation Research Chapter 3 Simplex Method.
LECTURE 14 Minimization Two Phase method by Dr. Arshad zaheer
Chapter 6 Linear Programming: The Simplex Method
The Simplex Method The geometric method of solving linear programming problems presented before. The graphical method is useful only for problems involving.
Dr. Sana’a Wafa Al-Sayegh
Copyright (c) 2003 Brooks/Cole, a division of Thomson Learning, Inc
Computational Methods for Management and Economics Carla Gomes Module 6a Introduction to Simplex (Textbook – Hillier and Lieberman)
Degeneracy and the Convergence of the Simplex Algorithm LI Xiao-lei.
Sections 4.1 and 4.2 The Simplex Method: Solving Maximization and Minimization Problems.
Linear Inequalities and Linear Programming Chapter 5
Computational Methods for Management and Economics Carla Gomes Module 6b Simplex Pitfalls (Textbook – Hillier and Lieberman)
The Simplex Method: Standard Maximization Problems
5.4 Simplex method: maximization with problem constraints of the form
The Simplex Algorithm An Algorithm for solving Linear Programming Problems.
Operation Research Chapter 3 Simplex Method.
Minimization by Dr. Arshad zaheer
Solving Linear Programs: The Simplex Method
Optimization Mechanics of the Simplex Method
Linear Programming (LP)
The Simplex Method.
5.6 Maximization and Minimization with Mixed Problem Constraints
MIT and James Orlin © Chapter 3. The simplex algorithm Putting Linear Programs into standard form Introduction to Simplex Algorithm.
LINEAR PROGRAMMING SIMPLEX METHOD.
Chapter 6 Linear Programming: The Simplex Method
The Two-Phase Simplex Method LI Xiao-lei. Preview When a basic feasible solution is not readily available, the two-phase simplex method may be used as.
8. Linear Programming (Simplex Method) Objectives: 1.Simplex Method- Standard Maximum problem 2. (i) Greedy Rule (ii) Ratio Test (iii) Pivot Operation.
Simplex Algorithm.Big M Method
Chapter 6 Linear Programming: The Simplex Method Section 2 The Simplex Method: Maximization with Problem Constraints of the Form ≤
ECE 556 Linear Programming Ting-Yuan Wang Electrical and Computer Engineering University of Wisconsin-Madison March
Topic III The Simplex Method Setting up the Method Tabular Form Chapter(s): 4.
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.
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.
Parametric Linear Programming-1 Parametric Linear Programming.
Chapter 6 Linear Programming: The Simplex Method Section R Review.
Duality Theory  Every LP problem (called the ‘Primal’) has associated with another problem called the ‘Dual’.  The ‘Dual’ problem is an LP defined directly.
1© 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Linear Programming: The Simplex Method.
Solving Linear Programming Problems: The Simplex Method
Mechanical Engineering Department 1 سورة النحل (78)
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.
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.
An-Najah N. University Faculty of Engineering and Information Technology Department of Management Information systems Operations Research and Applications.
Simplex Method Simplex: a linear-programming algorithm that can solve problems having more than two decision variables. The simplex technique involves.
Business Mathematics MTH-367 Lecture 16. Chapter 11 The Simplex and Computer Solutions Methods continued.
Copyright © 2006 Brooks/Cole, a division of Thomson Learning, Inc. Linear Programming: An Algebraic Approach 4 The Simplex Method with Standard Maximization.
1 Simplex algorithm. 2 The Aim of Linear Programming A Linear Programming model seeks to maximize or minimize a linear function, subject to a set of linear.
Decision Support Systems INF421 & IS Simplex: a linear-programming algorithm that can solve problems having more than two decision variables.
Artificial Variable Technique (The Big-M Method) ATISH KHADSE.
The Simplex Method. and Maximize Subject to From a geometric viewpoint : CPF solutions (Corner-Point Feasible) : Corner-point infeasible solutions 0.
Solving Linear Program by Simplex Method The Concept
Simplex Algorithm.Big M Method
Stat 261 Two phase method.
10CS661 OPERATION RESEARCH Engineered for Tomorrow.
The Two-Phase Simplex Method
The Simplex Method: Standard Minimization Problems
Solving Linear Programming Problems: Asst. Prof. Dr. Nergiz Kasımbeyli
Well, just how many basic
The Simplex Method The geometric method of solving linear programming problems presented before. The graphical method is useful only for problems involving.
LINEAR PROGRAMMING Example 1 Maximise I = x + 0.8y
Presentation transcript:

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 form?  Equality Constraints x 1 + x 2 = 8  Greater than Constraints x 1 + x 2 ≥ 8  Minimizing  How do we get the initial BF solution?

Original Form Maximize Z = 3x 1 + 5x 2 Subject to: x 1 ≤ 4 2x 2 ≤ 12 3x 1 + 2x 2 = 18 x 1 ≥ 0, x 2 ≥ 0 Augmented Form Maximize Z = 3x 1 + 5x 2 Subject to: Z - 3x 1 - 5x 2 = 0 x 1 + x 3 = 4 2x 2 + x 4 = 12 3x 1 + 2x 2 = 18 x 1, x 2, x 3, x 4 ≥ 0

Original Form Maximize Z = 3x 1 + 5x 2 Subject to: x 1 ≤ 4 2x 2 ≤ 12 3x 1 + 2x 2 = 18 x 1 ≥ 0, x 2 ≥ 0 Artificial Form (Big M Method) Maximize Z = 3x 1 + 5x 2 Subject to: Z - 3x 1 - 5x 2 + Mx 5 = 0 x 1 + x 3 = 4 2x 2 + x 4 = 12 3x 1 + 2x 2 + x 5 = 18 x 1, x 2, x 3, x 4, x 5 ≥ 0

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z M0 x3x x4x x5x Z-3x 1 -5x 2 + Mx 5 = 0 x1x1 + x 3 = 4 x2x2 + x 4 = 12 3x 1 2x 2 + x 5 = 18

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z1 -3M-3-2M M x3x x4x x5x Select Initial Point nonbasic variables: x 1 and x 2 (origin) Initial BF solution: (x 1, x 2, x 3, x 4, x 5 ) = (0,0,4,12,18M) To get to initial point, remove x 5 coefficient (M) from Z - 3x 1 - 5x 2 + Mx 5 = 0 (-M) ( 3x 1 + 2x 2 + x 5 = 18) (-3M-3)x 1 + (-2M-5)x 2 = -18M

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z1 -3M-3-2M M x3x x4x x5x Optimality Test: Are all coefficients in row (0) ≥ 0? If yes, then STOP – optimal solution If no, then continue algorithm

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z1 -3M-3-2M M x3x x4x x5x Select Entering Basic Variable Choose variable with negative coefficient having largest absolute value Select Leaving Basic Variable 1. Select coefficient in pivot column > 0 2. Divide Right Side value by this coefficient 3. Select row with smallest ratio 4/1 = 4 18/3 = 6

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z1 -3M-3-2M M x3x x4x x5x Z10 -2M-53M M+12 x1x x4x x5x

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z10 -2M-53M M+12 x1x x4x x5x BF Solution: (x 1, x 2, x 3, x 4, x 5 ) = (4,0,0,12,6) Optimality Test: Are all coefficients in row (0) ≥ 0? If yes, then STOP – optimal solution If no, then continue algorithm

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z10 -2M-53M M+12 x1x x4x x5x Select Entering Basic Variable Choose variable with negative coefficient having largest absolute value Select Leaving Basic Variable 1. Select coefficient in pivot column > 0 2. Divide Right Side value by this coefficient 3. Select row with smallest ratio 12/2 = 6 6/2 = 3

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z10 -2M-53M M+12 x1x x4x x5x Z100-9/20 M+5/2 27 x x x /201/23

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z100-9/20 M+5/2 27 x x x /201/23 BF Solution: (x 1, x 2, x 3, x 4, x 5 ) = (4,3,0,6,0) Optimality Test: Are all coefficients in row (0) ≥ 0? If yes, then STOP – optimal solution If no, then continue algorithm

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z100-9/20 M+5/2 27 x x x /201/23 Select Entering Basic Variable Choose variable with negative coefficient having largest absolute value Select Leaving Basic Variable 1. Select coefficient in pivot column > 0 2. Divide Right Side value by this coefficient 3. Select row with smallest ratio 4/1 = 4 6/3 = 2

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z100-9/20 M+5/2 27 x x x /201/23 Z10003/2 M+1 36 x /31/32 x3 x /32 x2 x /206

Basic Variable Coefficient of: Right Side Zx1x1 x2x2 x3x3 x4x4 x5x5 Z10003/2 M+1 36 x /31/32 x3 x /32 x2 x /206 BF Solution: (x 1, x 2, x 3, x 4, x 5 ) = (2,6,2,0,0) Optimality Test: Are all coefficients in row (0) ≥ 0? If yes, then STOP – optimal solution If no, then continue algorithm

Minimize Z = 3x 1 + 5x 2 Multiply by -1 Maximize -Z = -3x 1 - 5x 2

x 1 - x 2 ≤ -1 Multiply by -1 -x 1 + x 2 ≥ 1

x 1 - x 2 ≥ 1 x 1 - x 2 - x 5 ≥ 1 Change Inequality x 1 - x 2 - x 5 ≤ 1 x 1 - x 2 - x 5 + x 6 ≤ -1 Big M Augmented Form

Original Form Minimize Z = 4x 1 + 5x 2 Subject to: 3x 1 + x 2 ≤ 27 5x 1 + 5x 2 = 60 6x 1 + 4x 2 ≥ 60 x 1 ≥ 0, x 2 ≥ 0 Adaption Form Minimize Z = 4x 1 + 5x 2 Maximize -Z = -4x 1 - 5x 2 Subject to: -Z + 4x 1 + 5x 2 + Mx 4 + Mx 6 = 0 3x 1 + x 2 + x 3 = 27 5x 1 + 5x 2 + x 4 = 60 6x 1 + 4x 2 - x 5 + x 6 = 60