'Objective function value' diaporamas de présentation

LINEAR PROGRAMMING

LINEAR PROGRAMMING. These visual aids will assist in class participation for the section on Linear Programming. They have no new information and less detail than the Word Document. Concepts and geometric interpretations Basic assumptions and limitations Post-optimal, results analysis

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Managerial Decision Modeling with Spreadsheets

Managerial Decision Modeling with Spreadsheets. Chapter 4 Linear Programming Sensitivity Analysis. Learning Objectives.

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Counting position weight matrices in a sequence & an application to discriminative motif finding

Counting position weight matrices in a sequence & an application to discriminative motif finding. Saurabh Sinha Computer Science University of Illinois, Urbana-Champaign. GENE. A C A G TG A. PROTEIN. Transcriptional Regulation. TRANSCRIPTION FACTOR. GENE. A C A G TG A. PROTEIN.

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Chapter 8

Chapter 8. Strategic Allocation of Resources (Linear Programming). A company makes 3 products: A, B and C. A B C Available Profit 35 45 25 Labor Hrs 5 7 3 2000 hrs Fiberglass 18 25 12 7000 lbs

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Special Conditions in LP Models (sambungan BAB 1)

Special Conditions in LP Models (sambungan BAB 1). A number of anomalies can occur in LP problems: Alternate Optimal Solutions Redundant Constraints Unbounded Solutions Infeasibility. X 2. 250. objective function level curve. 450X 1 + 300X 2 = 78300. 200. 150. 100.

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Local Search and Optimization

Local Search and Optimization. CS 271: Fall 2007 Instructor: Padhraic Smyth. Outline. Local search techniques and optimization Hill-climbing Gradient methods Simulated annealing Genetic algorithms Issues with local search Homework 1: due today

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Introduction to Mathematical Programming

Amirkabir University of Technology Computer Engineering & Information Technology Department. Introduction to Mathematical Programming. Dr. Saeed Shiry. Introduction. Mathematical Programming considers the problem of allocating limited resources among competing activities.

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Feature Selection in Nonlinear Kernel Classification

Feature Selection in Nonlinear Kernel Classification. Olvi Mangasarian Edward Wild University of Wisconsin Madison. Problem. Input space feature selection in nonlinear kernel classification Mixed-integer programming problem Inherently difficult (NP-hard) Most research of recent vintage.

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Chapter 2 Introduction to Linear Programming

Chapter 2 Introduction to Linear Programming. Example of Linear Programming Problem. Each week the company can obtain: All needed raw material. Only 100 finishing hours. Only 80 carpentry hours. Also: Demand for the trains is unlimited.

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Linear Programming: Applications

Linear Programming: Applications. Chapter 4. Blending Problem.

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Sensitivity

Sensitivity . Output from Computer Solution : Changing Cells . Final Value The value of the variable in the optimal solution Reduced Cost Increase in the objective function value per unit increase in the value of a zero-valued variable (a product that the model has

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Feature Selection in Nonlinear Kernel Classification

Workshop on Optimization-Based Data Mining Techniques with Applications IEEE International Conference on Data Mining Omaha, Nebraska, October 28, 2007. Feature Selection in Nonlinear Kernel Classification. Olvi Mangasarian & Edward Wild University of Wisconsin Madison.

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ISM 206 Lecture 3

ISM 206 Lecture 3. The Simplex Method. Announcements. Homework due 6pm Thursday Thursday 6pm lecture. Outline. LP so far Correction: Standard form Why we can look only at basic feasible solutions Optimality conditions The simplex method The step from one bfs to the next

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Ch 11 Resource Constraints and Linear Programming

Ch 11 Resource Constraints and Linear Programming. The process of finding an optimum outcome from a set of constrained resources, where the objective function and the constraints can be expressed as linear equations. . Drawing the Linear Model. Adding the Linear Constraints. Feasible Region.

542 views • 15 slides

Tabu Search

Tabu Search. Subset of Slides from Lei Li, HongRui Liu, Roberto Lu. Introduction. Glover, F. 1986. Future Paths for Integer Programming and Links to Artificial Intelligence. Computers and Operations Research. Vol. 13, pp. 533-549.

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Manufacturing Cell Design - Problem Formulation

Manufacturing Cell Design - Problem Formulation. Team #2 Mingang Fu Lin Ben Kuowei Chen. L. Lathe department. G. Milling department. M. D. Drilling department. Grinding department. Introduction. Traditional Manufacturing Processes Product Layout Functional Layout.

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Integer Programming

Integer Programming. Integer Programming. Programming = Planning in this context Origins go back to military logistics in WWII (1940s). In a survey of Fortune 500 firms, 85% of those responding said that they had used linear or integer programming. Why is it so popular?

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Practice: Given the following Sensitivity Analysis Report

Practice: Given the following Sensitivity Analysis Report . Sensitivity Analysis. 0(15) 500(20) 375(25) = 19375 (e). What is the optimal objective function value for this problem? a. It cannot be determined from the given information. b. $900. c. $987.5. d. $875. e. $19375.

346 views • 6 slides