Capacitated Plant Location Problem:

Similar documents
D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1

7.0 Equality Contraints: Lagrange Multipliers

Algorithms behind the Correlation Setting Window

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

Some Different Perspectives on Linear Least Squares

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

Non-degenerate Perturbation Theory

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x)

Coherent Potential Approximation

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

Special Instructions / Useful Data

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.

International Journal of Mathematical Archive-3(5), 2012, Available online through ISSN

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

Designing a Supply Chain Network Model with Uncertain Demands and Lead Times

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

The theoretical background of

Pinaki Mitra Dept. of CSE IIT Guwahati

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Parallelized methods for solving polynomial equations

Debabrata Dey and Atanu Lahiri

Binary classification: Support Vector Machines

An Innovative Algorithmic Approach for Solving Profit Maximization Problems

-Pareto Optimality for Nondifferentiable Multiobjective Programming via Penalty Function

Two Uncertain Programming Models for Inverse Minimum Spanning Tree Problem

Econometric Methods. Review of Estimation

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

Standard Deviation for PDG Mass Data

Duality Theory for Interval Linear Programming Problems

Support vector machines II

18.413: Error Correcting Codes Lab March 2, Lecture 8

Homework Assignment Number Eight Solutions

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM)

Support vector machines

Solving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points

A COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Salih Fadıl 1, Burak Urazel 2. Abstract. 1. Introduction. 2. Problem Formulation

Stationary states of atoms and molecules

Rademacher Complexity. Examples

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

Bayes (Naïve or not) Classifiers: Generative Approach

Functions of Random Variables

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 17

Solutions to problem set ); (, ) (

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Point Estimation: definition of estimators

Construction of Composite Indices in Presence of Outliers

A Note on Ratio Estimators in two Stage Sampling

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

1. Introduction. Keywords: Dynamic programming, Economic power dispatch, Optimization, Prohibited operating zones, Ramp-rate constraints.

Lecture 8 IEEE DCF Performance

A conic cutting surface method for linear-quadraticsemidefinite

A New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM

LINEAR REGRESSION ANALYSIS

Module 7. Lecture 7: Statistical parameter estimation

The Application of Transportation Algorithm with Volume Discount on Distribution Cost (A case study of Port Harcourt flour mills Company Ltd.

Physics 114 Exam 2 Fall Name:

Numerical Analysis Formulae Booklet

EECE 301 Signals & Systems

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

Long blade vibration model for turbine-generator shafts torsional vibration analysis

Runtime analysis RLS on OneMax. Heuristic Optimization

1 Onto functions and bijections Applications to Counting

Parameter, Statistic and Random Samples

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

PTAS for Bin-Packing

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

SEMI-TIED FULL-COVARIANCE MATRICES FOR HMMS

3.1 Introduction to Multinomial Logit and Probit

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

A New Measure of Probabilistic Entropy. and its Properties

Ideal multigrades with trigonometric coefficients

1 Lyapunov Stability Theory

An Implementation of Integer Programming Techniques in Clustering Algorithm

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

The Occupancy and Coupon Collector problems

Newton s Power Flow algorithm

8.1 Hashing Algorithms

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

CH E 374 Computational Methods in Engineering Fall 2007

Simple Linear Regression

Transcription:

. L. Brcker, 2002 ept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/ 5/29/2002 page CPL/ 5/29/2002 page 2 Capactated Plat Locato Proble: where Mze F + C subect to = = =, =, S, =,... 0, =, ; =, 0,, =, f a plat s bult at ste = 0 otherwse = quatty suppled by plat at ste to custoer The followg addtoal costrats are redudat but are potetally useful, depedg upo how we choose the Lagraga relaxato:, =, ;, These costrats, together wth S, =,..., could be used stead of the costrats S, =,... order to force shpets fro a plat to be zero f that plat has ot bee opeed! CPL/ 5/29/2002 page 3 CPL/ 5/29/2002 page 4

The costrat = s redudat, but s useful order to geerate tral solutos ( whch are guarateed to gve feasble subprobles! A alterate forulato of the CPL: Mze F + C subect to = = =, =, S, =, =, =, ;, (lkg costrats 0,, ;, = =, 0,, =, CPL/ 5/29/2002 page 5 CPL/ 5/29/2002 page 6 Our goal s to separate the proble by relaxg the costrats lkg the ad decsos. A Lagraga Relaxato of the CPL: ( µ = Mu F + C + µ subect to = = = Rearragg ters the obectve fucto: = ( µ = ( µ + ( + µ Mu F C subect to 0,, =, = = =, =, S, =, 0, =, ; =, =, =, S, =, = 0, =, ; =, 0,, =, CPL/ 5/29/2002 page 7 CPL/ 5/29/2002 page 8

Ths separates to two subprobles: ( µ = ( µ + ( µ.e., the trasportato proble ( µ = ( + µ Mu C = subect to =, =, S, =, 0, =, ; =, By usg the copleets of the varables,.e.,, the subproble ( µ ca be expressed as a 0- kapsack proble: ( µ = ( µ Maxu ( µ F F = = S S = = subect to, 0, ad the proble: ( µ = ( µ Mu F = = subect to S, 0, CPL/ 5/29/2002 page 9 CPL/ 5/29/2002 page 0 Cosder stll aother forulato of CPL: Mze F + C subect to = = =, =, S, =, = S, =, (lkg costrats 0, =, ; =, 0,, =, A Lagraga Relaxato of the CPL, where µ 0: ( µ = Mu F + C + µ S = = = or ( µ = ( µ + ( + µ Mu F S C subect to = = =, =, S, =, = 0, =, ; =, 0,, =, CPL/ 5/29/2002 page CPL/ 5/29/2002 page 2

Ths separates to two subprobles: ( µ = ( µ + ( µ.e., the trasportato proble ( µ = ( + µ Mu C = subect to =, =, S, =, 0, =, ; =, As before, by usg the copleets of the varables,.e., (, µ ca be expressed as a 0- kapsack proble: ( µ = ( µ Maxu ( µ F S S F = = = = subect to S S, 0, ad the proble: ( µ = ( µ Mu F S = = subect to S, 0, CPL/ 5/29/2002 page 3 CPL/ 5/29/2002 page 4 Cross-ecoposto Ether Lagraga subproble ca be used to geerate tral solutos ( for the Beders' subprobles (whch tur geerates Lagraga ultplers for the Lagraga subprobles a Cross-ecoposto schee! Note that f lower bouds are ot requred, oly the Lagraga subproble the varables (.e., the kapsack proble eeds to be solved at each terato to provde the tral soluto for Beders' subproble STALLING "Stallg" ca occur,.e., the sae tral varables or the sae dual varables ay be geerated ultple tes, whch case t s ecessary to resort to the Beders' (or Lagraga Master Proble. I order to avod ths, ea values ay be passed fro the pral subproble to dual subproble, ad/or vce versa. CPL/ 5/29/2002 page 5 CPL/ 5/29/2002 page 6

Note: Cuts or costrats for Beders Master Proble ay be derved as follows. Assue that the subproble s feasble,.e., = Trasfor the supply & dead costrats to equatos by defg a duy dead pot (#+ : Let U & V be the optal dual varables. The the optal value v( s F + U S + V + V S + = = = ( + ( + = U + V S + V V = + F + Mze C 0 = = subect to = =, =, = S, =,... = S, + = = Thus, the lear (uder-estate of the optal value fucto v( s α +β, where α = U + V S, =, ( + ( + β= V V CPL/ 5/29/2002 page 7 CPL/ 5/29/2002 page 8