Linear Program MAX C B X B + C NB X NB s.t. BX B + A NB X NB = b X B, X NB ≥ 0.

Slides:



Advertisements
Similar presentations
Integer Optimization Basic Concepts Integer Linear Program(ILP): A linear program except that some or all of the decision variables must have integer.
Advertisements

Geometry and Theory of LP Standard (Inequality) Primal Problem: Dual Problem:
Linear Programming, 1 Max c 1 *X 1 +…+ c n *X n = z s.t. a 11 *X 1 +…+ a 1n *X n  b 1 … a m1 *X 1 +…+ a mn *X n  b m X 1, X n  0 Standard form.
Solving LP Models Improving Search Special Form of Improving Search
Linear Programming Problem
Linear Programming – Simplex Method
Introduction to Algorithms
Copyright (c) 2003 Brooks/Cole, a division of Thomson Learning, Inc
Optimization of thermal processes2007/2008 Optimization of thermal processes Maciej Marek Czestochowa University of Technology Institute of Thermal Machinery.
Linear Programming Fundamentals Convexity Definition: Line segment joining any 2 pts lies inside shape convex NOT convex.
Computational Methods for Management and Economics Carla Gomes Module 6b Simplex Pitfalls (Textbook – Hillier and Lieberman)
The Simplex Method: Standard Maximization Problems
Design and Analysis of Algorithms
Chapter 10: Iterative Improvement
Simplex Method LP problem in standard form. Canonical (slack) form : basic variables : nonbasic variables.
Linear Programming (LP)
ISM 206 Lecture 4 Duality and Sensitivity Analysis.
D Nagesh Kumar, IIScOptimization Methods: M3L4 1 Linear Programming Simplex method - II.
Chapter 3 Introduction to optimization models. Linear Programming The PCTech company makes and sells two models for computers, Basic and XP. Profits for.
FORMULATION AND GRAPHIC METHOD
Linear Programming.
LINEAR PROGRAMMING SIMPLEX METHOD.
The application of mathematics and the scientific
Linear Programming Topics General optimization model LP model and assumptions Manufacturing example Characteristics of solutions Sensitivity analysis Excel.
3.5 – Solving Systems of Equations in Three Variables.
1 Introduction to Linear and Nonlinear Programming.
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.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Chapter 6 Linear Programming: The Simplex Method Section R Review.
Introduction to Linear Programming BSAD 141 Dave Novak.
Chapter 7 Duality and Sensitivity in Linear Programming.
Linear Programming – Simplex Method
Notes on Linear Programming Uwe A. Schneider. Linear Programming, 1 Max c 1 *X 1 +…+ c n *X n = z s.t. a 11 *X 1 +…+ a 1n *X n  b 1 … a m1 *X 1 +…+ a.
Linear Programming Erasmus Mobility Program (24Apr2012) Pollack Mihály Engineering Faculty (PMMK) University of Pécs João Miranda
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
1 Chapter 4 The Simplex Algorithm PART 2 Prof. Dr. M. Arslan ÖRNEK.
1.3 Solving with Variables on Both Sides. What We Will Learn Solve linear equations that have variables on both sides Identify special solutions.
C&O 355 Mathematical Programming Fall 2010 Lecture 5 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A.
1 Simplex Method (created by George Dantzig in late 1940s) A systematic way of searching for an optimal LP solution BMGT 434, Spring 2002 Instructor: Chien-Yu.
OR Chapter 7. The Revised Simplex Method  Recall Theorem 3.1, same basis  same dictionary Entire dictionary can be constructed as long as we.
3 Components for a Spreadsheet Optimization Problem  There is one cell which can be identified as the Target or Set Cell, the single objective of the.
OR Relation between (P) & (D). OR optimal solution InfeasibleUnbounded Optimal solution OXX Infeasible X( O )O Unbounded XOX (D) (P)
1. 2 We studying these special cases to: 1- Present a theoretical explanation of these situations. 2- Provide a practical interpretation of what these.
9.1 Solving Differential Equations Mon Jan 04 Do Now Find the original function if F’(x) = 3x + 1 and f(0) = 2.
Copyright © 2006 Brooks/Cole, a division of Thomson Learning, Inc. Linear Programming: An Algebraic Approach 4 The Simplex Method with Standard Maximization.
Mathematical Programming Formulations Based on McCarl and Spreen.
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.
The Simplex Method. and Maximize Subject to From a geometric viewpoint : CPF solutions (Corner-Point Feasible) : Corner-point infeasible solutions 0.
EMGT 6412/MATH 6665 Mathematical Programming Spring 2016
Special Cases in simplex method applications
Linear Programming Topics General optimization model
6.5 Stochastic Prog. and Benders’ decomposition
MBA 651 Quantitative Methods for Decision Making
Linear Programming Topics General optimization model
Linear Programming Topics General optimization model
Linear Programming in Two Dimensions
Chap 9. General LP problems: Duality and Infeasibility
Chapter 5. Sensitivity Analysis
Linear Programming Topics General optimization model
The application of mathematics and the scientific
Lecture 4 Part I Mohamed A. M. A..
Chapter 5. The Duality Theorem
Linear Programming Problem
6.5 Stochastic Prog. and Benders’ decomposition
Graphical solution A Graphical Solution Procedure (LPs with 2 decision variables can be solved/viewed this way.) 1. Plot each constraint as an equation.
Presentation transcript:

Linear Program MAX C B X B + C NB X NB s.t. BX B + A NB X NB = b X B, X NB ≥ 0

Important LP Equations

Important LP Derivatives

Duality

Unbounded Solution

Infeasible Solution

Multiple Optima

Degeneracy

Complementary Slackness derived from duality

Reduced Cost Negative derivative of objective function with respect to a variable At optimality: –Zero for all basic variables –Non-negative for all non-basic variables (max) –Non-positive for all non-basic variables (max)

Multi-input, Multi-output

Mixing / Blending

Spatial Equilibrium (GAMS Ex.)

Sequencing

Storage

Lexicographic preferences

Weighted Preferences

Well behaved, Separable Function

Disequilibrium – Known Life

Disequilibrium – Unknown Life

Equilibrium ‑ Unknown Life

Fixed Costs

Fixed Capacity

Minimum Habitat Size

Warehouse

Mutual exclusive products

Either-Or-Active constraints

Distinct Variable Values

Badly behaved non-linear functions

Non-linear Programming Specification often straightforward Solving more difficult –scaling (manual vs. computer) –lower bounds to avoid division by zero and other illegal operations –local versus global extremes