IP modeling techniques III In this handout, Modeling techniques:  Making choices with non-binary variables  Piecewise linear functions.

Slides:



Advertisements
Similar presentations
Tuesday, May 7 Integer Programming Formulations Handouts: Lecture Notes.
Advertisements

Solve a System Algebraically
Using linear programming to solve discrete problems
Introduction to Mathematical Programming Matthew J. Liberatore John F. Connelly Chair in Management Professor, Decision and Information Technologies.
IP modeling techniques I In this handout, Modeling techniques:  Using binary variables  Restrictions on number of options  Contingent decisions  Variables.
Linear Programming Problem
Branch-and-Bound In this handout,  Summary of branch-and-bound for integer programs Updating the lower and upper bounds for OPT(IP) Summary of fathoming.
1 Cutting Plane Technique for Solving Integer Programs.
Chapter 5 Linear Inequalities and Linear Programming
Linear Inequalities and Linear Programming Chapter 5 Dr.Hayk Melikyan/ Department of Mathematics and CS/ Linear Programming in two dimensions:
Chapter 5 Linear Inequalities and Linear Programming Section 3 Linear Programming in Two Dimensions: A Geometric Approach.
Chapter 2 Linear Programming Models: Graphical and Computer Methods © 2007 Pearson Education.
Thursday, April 25 Nonlinear Programming Theory Separable programming Handouts: Lecture Notes.
1 Chapter 4 Interpolation and Approximation Lagrange Interpolation The basic interpolation problem can be posed in one of two ways: The basic interpolation.
Agenda Duality (quickly) Piecewise linearity Start chapter 4.
Solving Integer Programs. Natural solution ideas that don’t work well Solution idea #1: Explicit enumeration: Try all possible solutions and pick the.
So far all our functions were linear. In many situations, it might not be the case. Example: Production cost. – c 1 = $11/unit for first 5 items – c 2.
Constrained Maximization
Integer Programming Integer programming is a solution method for many discrete optimization problems Programming = Planning in this context Origins go.
IP modeling techniques II
Mrs. McConaughyGeometry1 The Coordinate Plane During this lesson you will:  Find the distance between two points in the plane  Find the coordinates of.
1 Consultant hiring problem: Modeling on AMPL. 2 Outline of modeling process 1.Carefully read the problem to get a general idea 2.Analyze each paragraph.
The Distance and Midpoint Formulas Goal 1 Find the Midpoint of a Segment Goal 2 Find the distance between two points on a coordinate plane Goal 3 Find.
Table of Contents Recall that to solve the linear system of equations in two variables... we needed to find the values of x and y that satisfied both equations.
Objectives  Solve systems of linear equations in three variables using left-to-right elimination  Find solutions of dependent systems  Determine when.
Table of Contents Solving Linear Systems of Equations - Substitution Method Recall that to solve the linear system of equations in two variables... we.
Chapter 4 An Introduction to Optimization
Table of Contents Solving Linear Systems of Equations - Addition Method Recall that to solve the linear system of equations in two variables... we need.
Objectives  Solve systems of linear equations in three variables using left-to-right elimination  Find solutions of dependent systems  Determine when.
Decision Making via Linear Programming: A simple introduction Fred Phillips
Integer Programming Integer programming is a solution method for many discrete optimization problems Programming = Planning in this context Origins go.
Integer Programming Key characteristic of an Integer Program (IP) or Mixed Integer Linear Program (MILP): One or more of the decision variable must be.
Solving Linear Systems of Equations - Substitution Method Recall that to solve the linear system of equations in two variables... we need to find the value.
CDAE Class 13 Oct. 10 Last class: Result of Quiz 3 3. Linear programming and applications Class exercise 5 Today: 3. Linear programming and applications.
15.053Tuesday, April 9 Branch and Bound Handouts: Lecture Notes.
Linear Programming Solving Systems of Equations with 3 Variables Inverses & Determinants of Matrices Cramer’s Rule.
7.4. 5x + 2y = 16 5x + 2y = 16 3x – 4y = 20 3x – 4y = 20 In this linear system neither variable can be eliminated by adding the equations. In this linear.
Systems of Linear Equations in Two Variables. 1. Determine whether the given ordered pair is a solution of the system.
Integer Programming. Programming = Planning in this context Origins go back to military logistics in WWII (1940s). In a survey of Fortune 500 firms, 85%
Linear Programming-Bellwork
Ch. 3 Notes Page 19 P19 3.4b: Linear Programming.
EXAMPLE 4 Solve a system using substitution Marketing The marketing department of a company has a budget of $30,000 for advertising. A television ad costs.
LINEAR PROGRAMMING 3.4 Learning goals represent constraints by equations or inequalities, and by systems of equations and/or inequalities, and interpret.
Section 3.5 Solving Systems of Linear Equations in Two Variables by the Addition Method.
1 Chapter 4 Interpolation and Approximation Lagrange Interpolation The basic interpolation problem can be posed in one of two ways: The basic interpolation.
CHAPTER THREE: SYSTEMS OF LINEAR EQUATIONS AND INEQUALITIES ALGEBRA TWO Section Solving Systems of Linear Equations in Three Variables.
Chapter 9 Integer Programming to accompany Operations Research: Applications and Algorithms 4th edition by Wayne L. Winston Copyright (c) 2004 Brooks/Cole,
Tuesday, March 19 The Network Simplex Method for Solving the Minimum Cost Flow Problem Handouts: Lecture Notes Warning: there is a lot to the network.
WARM-UP. SYSTEMS OF EQUATIONS: ELIMINATION 1)Rewrite each equation in standard form, eliminating fraction coefficients. 2)If necessary, multiply one.
Part 4 Nonlinear Programming 4.3 Successive Linear Programming.
MIT and James Orlin © The Geometry of Linear Programs –the geometry of LPs illustrated on GTC.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
Find the distance of AB Geometry.
2.7 Linear Programming Objectives: Use linear programming procedures to solve applications. Recognize situations where exactly one solution to a linear.
Chapter 5 Linear Inequalities and Linear Programming
Math 1 Warm Up In the Practice Workbook… Practice 7-6 (p. 94)
Chapter Seven Revealed Preference.
Chapter 9 Integer Programming
Solving Linear Systems by Linear Combinations
Integer Programming (정수계획법)
Before: November 28, 2017 Solve each system by graphing. 1. y = 2x – 1
Integer Programming.
Integer Programming (정수계획법)
Part 4 Nonlinear Programming
Nature does nothing uselessly.
Binary Search Counting
Chapter 1. Formulations.
Branch-and-Bound Algorithm for Integer Program
Systems of three equations with three variables are often called 3-by-3 systems. In general, to find a single solution to any system of equations,
Presentation transcript:

IP modeling techniques III In this handout, Modeling techniques:  Making choices with non-binary variables  Piecewise linear functions

Making choices with non-binary variables Recall the furniture manufacturer problem. Extra requirement: From the 3 possible products (tables, chairs, desks), at most two should be chosen to be produced. That is, at most two of x t, x c, x d can be non-zero. How to achieve this in the model? Introduce new binary variables. For i=t,c,d, To enforce the requirement, need the following constraint: y t + y c + y d  2 Need also to relate x i ’s and y i ’s. Add constraints: x i  My i for i=t,c,d and large positive M

Piecewise linear functions So far all our functions were linear. In many situations, it might not be the case. Example: Production cost. – c 1 = $11/unit for first 5 items – c 2 = $8/unit for next 4 items – c 3 = $5/unit for next 7 items – c 4 = $7/unit for next 10 items The cost of producing x items is an example of so-called piecewise linear function:

Piecewise linear functions How to include piecewise linear cost functions in an objective function of IP? Idea: Introduce a new variable for each cost segment. For i=1,2,3,4 y i = number of items produced at cost c i Then the total number of items is x = y 1 +y 2 +y 3 +y 4. We need constraints 0  y 1  5, 0  y 2  4, 0  y 3  7, 0  y 4  10,(*) and the production cost in the objective function is 11y 1 + 8y 2 + 5y 3 + 7y 4 What is the shortcoming of this model?

Piecewise linear functions We should require that –y 2 >0 implies that y 1 =5(1) –y 3 >0 implies that y 2 =4(2) –y 4 >0 implies that y 3 =7(3) Introduce new variables to translate these requirements into linear constraints. For i=1,2,3,4, Proper constraints relating w i and y i will provide that requirements (1)-(3) are satisfied. y 2  4w 1 and 5w 1  y 1 provide (1) y 3  7w 2 and 4w 2  y 2 provide (2) y 4  10w 3 and 7w 3  y 3 provide (3)

Piecewise linear functions Summarizing, the bound constraints in (*) should be substituted with 5w 1  y 1  5, 4w 2  y 2  4w 1, 7w 3  y 3  7w 2, 0  y 4  10w 3. Generalizing, suppose we have k segments with lengths L 1, L 2, …, L k. Then the necessary constraints: L 1 w 1  y 1  L 1, L i w i  y i  L i w i-1 for i = 2, …, k-1 0  y k  L k w k-1