5.2 Mixed Integer Linear Programming

Slides:



Advertisements
Similar presentations
Algorithm Design Methods Spring 2007 CSE, POSTECH.
Advertisements

Basic Search Methods How to solve the control problem in production-rule systems? Basic techniques to find paths through state- nets. For the moment: -
Integer Optimization Basic Concepts Integer Linear Program(ILP): A linear program except that some or all of the decision variables must have integer.
Branch-and-Bound Technique for Solving Integer Programs
Pure, Mixed-Integer, Zero-One Models
DMOR Branch and bound. Integer programming Modelling logical constraints and making them linear: – Conjuction – Disjunction – Implication – Logical constraints.
Linear Programming Models & Case Studies
1 1 Slides by John Loucks St. Edward’s University Modifications by A. Asef-Vaziri.
Water Resources Development and Management Optimization (Integer Programming) CVEN 5393 Mar 11, 2013.
Branch & Bound Algorithms
Resolution Based Search John Hooker GSIA Feb 2003.
Computational Methods for Management and Economics Carla Gomes
Constraint Satisfaction Problems
Branch and Bound Algorithm for Solving Integer Linear Programming
NetworkModel-1 Network Optimization Models. NetworkModel-2 Network Terminology A network consists of a set of nodes and arcs. The arcs may have some flow.
Daniel Kroening and Ofer Strichman Decision Procedures An Algorithmic Point of View Deciding ILPs with Branch & Bound ILP References: ‘Integer Programming’
Integer programming Branch & bound algorithm ( B&B )
Decision Procedures An Algorithmic Point of View
Operations Research Assistant Professor Dr. Sana’a Wafa Al-Sayegh 2 nd Semester ITGD4207 University of Palestine.
Chapter 9 Integer Programming
Integer Programming Key characteristic of an Integer Program (IP) or Mixed Integer Linear Program (MILP): One or more of the decision variable must be.
WOOD 492 MODELLING FOR DECISION SUPPORT
11.5 Implicit Partitioning/Packing Problems  Given M = {1, …, m}, K implicitly described sets of feasible subsets of M. Find a maximum value packing or.
15.053Tuesday, April 9 Branch and Bound Handouts: Lecture Notes.
Integer Programming Li Xiaolei. Introduction to Integer Programming An IP in which all variables are required to be integers is called a pure integer.
Divide and Conquer Optimization problem: z = max{cx : x  S}
ENGM 631 Optimization Ch. 3: Introduction to Linear Programming.
D Nagesh Kumar, IIScOptimization Methods: M6L5 1 Dynamic Programming Applications Capacity Expansion.
Branch and Bound Algorithms Present by Tina Yang Qianmei Feng.
5.3 Mixed Integer Nonlinear Programming Models. A Typical MINLP Model.
Integer Programming, Branch & Bound Method
Operations Research By: Saeed Yaghoubi 1 Graphical Analysis 2.
Chapter 6 Optimization Models with Integer Variables.
0 Integer Programming Introduction to Integer Programming (IP) Difficulties of LP relaxation IP Formulations Branch and Bound Algorithms Reference: Chapter.
Solving IPs – Implicit Enumeration Similar to Binary IP Branch and Bound General Idea: Fixed variables – those for which a value has been fixed. Free Variable.
Water Resources Development and Management Optimization (Integer and Mixed Integer Programming) CVEN 5393 Mar 28, 2011.
Integer Programming An integer linear program (ILP) is defined exactly as a linear program except that values of variables in a feasible solution have.
5.3 Mixed-Integer Nonlinear Programming (MINLP) Models
EMGT 6412/MATH 6665 Mathematical Programming Spring 2016
6.5 Stochastic Prog. and Benders’ decomposition
CS137: Electronic Design Automation
Introduction to Operations Research
5.2 Mixed Integer Linear Programming
Manpower Planning: Task Scheduling
1.3 Modeling with exponentially many constr.
5.3 Mixed Integer Nonlinear Programming Models
Duality Theory and Sensitivity Analysis
Branch and Bound.
Integer Linear Programming
13.3 day 2 Higher- Order Partial Derivatives
13.3 day 2 Higher- Order Partial Derivatives
1.3 Modeling with exponentially many constr.
Constraint satisfaction problems
Chapter 6 Network Flow Models.
Slides by John Loucks St. Edward’s University.
ENGM 435/535 Integer Programming.
11.5 Implicit Partitioning/Packing Problems
Cynthia Phillips (Sandia National Laboratories)
11.5 Implicit Partitioning/Packing Problems
5.2 Mixed Integer Linear Programming
6.5 Stochastic Prog. and Benders’ decomposition
Brute force “Obvious” solution Enumerate all possibilities
We have the following incomplete B&B tree:
Branch-and-Bound Algorithm for Integer Program
Constraint satisfaction problems
Branch-and-Bound Technique for Solving Integer Programs
Integer Linear Programming
Basic Search Methods How to solve the control problem in production-rule systems? Basic techniques to find paths through state- nets. For the moment: -
CS137: Electronic Design Automation
Presentation transcript:

5.2 Mixed Integer Linear Programming 5.2.2 Implicit Enumeration

Assignment Problem

Assignment Problem [Theorem] Any basic feasible solution of the assignment problem has every xij equal to either zero or one. Implication: There are at most n variables that have the value 1.

Basic Concept The basic idea of implicit enumeration is to explicitly enumerate a small subset of all possible solutions while concluding that it is not necessary to explicitly investigate the remaining solutions, because they are either infeasible or will result in an objective value that is inferior to the best integer solution already found.

Search Rules A forward step is defined as the branching process of fixing a free variable to be 1. The backtracking step is defined as the process to trace back to the origin until encounter the 1st node with only one descending branch. The search process is continued until all pendant nodes are fathomed and each non-pendant node has exactly 2 branches.

Additional Terminologies Completion: Given a node and a partial solution, a completion of the partial solution is a solution in which values are specified for all the remaining free variables. Fathom: A partial solution is fathomed by either (1) demonstrating that there are no improving feasible completions or (2) finding the best feasible solution.

Standard Form

Zero Completion Test

Infeasibility Test If no feasible completions (may or may not be zero completion), then the node should be fathomed.

Example

Subproblem P0 All variables are free.

Subproblem P1 (x1=1) 1

Subproblem P1 (x1=1)

Subproblem P2 (x1=1, x2=1)

Subproblem P3 (x1=1, x2=0)

4

Subproblem P4 (x1=1, x2=0, x3=1)

Subproblem P5 (x1=1, x2=0, x3=0)

Subproblem P6 (x1=0)

Subproblem P7 (x1=0, x2=1)

Subproblem P8 (x1=0, x2=0)

Solution The optimal integer solution is given by the incumbent solution found at node 7. Note that the complete tree would have 5 levels and 2^5=32 pendant nodes.

Blending Products including Batch Sizes In a plant we have 2 production units designated as number 1 and 2, making product 1 and 2, respectively, from the 3 feedstocks as shown. Unit 1 has a maximum capacity of 8000 lb/day, and unit 2 of 10000 lb/day. To make 1 lb of product 1 requires 0.4 lb of A and 0.6 lb of B; to make 1 lb of product 2 requires 0.3 lb of B and 0.7 lb of C. A maximum 6000 lb/day of B is available, but there are no limits on the available amounts of A and C. Assume the net revenue after expenses from the manufacture of product 1 id $0.16/lb, and of product 2 is $0.20/lb. How much of products 1 and 2 should be produced per day, assuming that each must be made in batches of 2000lb?