Linear programming

I. INTRODUCTION In this article, the authors proposed to use a new technique – “resource bricolage” to solve the low performance problem caused by the un-balanced workloads in parallel database systems. When a parallel database system is first constructed, the set of machines are made identical, therefore, the default data partitioning strategy for this parallel database is uniform data partitioning, and will ignore the differences among machines. In this case, all these identical machines will have

MANAGERIAL REPORT - Assignment 1: - Bangalore Textile Company : Bangalore textile company has in it’s a possession a plant capable of producing 6 types of suit materials . this production is currently realized by using a total of 40 machines divided in two categories , which are regular machines and super machines . the regular machines can only afford the manufacturing of materials types 1 ; 2 and 3 while the super machine are more flexible because they generate all 6 material types . knowing

TUTORIAL 2 Linear Programming - Minimisation Special cases Simplex maximisation 1. Innis Investments manages funds for a number of companies and wealthy clients. The investment strategy is tailored to each client’s needs. For a new client, Innis has been authorised to invest up to $1.2 million in two investment funds: a stock fund and a money market fund. Each unit of the stock fund costs $50 and provides an annual rate of return of 10%; each unit of the money market fund costs

Optimal Power Flow Using Differential Evolution Algorithm S.Vidya Sagar Reddy Dr.P.Venkata Prasad, Professor Department of Electrical and Electronics Engineering Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute Of Technology(Autonomous) Chaitanya Bharathi Institute Of Technology(Autonomous) Hyderabad, India Hyderabad, India vidyasagar.sabbella@gmail.com pvp_reddy@yahoo.co.uk Abstract— This paper presents an efficient and reliable evolutionary based approach to solve

them in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is an evolutionary optimization algorithm, and is inspired by the flashing behavior of special flies called fireflies in nature. There are some noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Firefly algorithm is one of the new metaheuristic algorithms for these optimization problems. The algorithm is inspired by the flashing behavior of fireflies

USING STEPPING STONE AND MODI METHODS TO SOLVE TRANSPORTATION PROBLEMS BY ABDUSSALAM MUHAMMAD MUSTAPHA 09/211306009 A SEMINAR PAPER PRESENTED TO THE DEPARTMENT OF MATHEMATICS, FACULTY OF SCIENCE, USMANU DANFODIYO UNIVERSITY, SOKOTO IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF SCIENCE (MATHEMATICS) SUPERVISORY TEAM: MAJOR SUPERVISOR: DR. U. A. ALI CO – SUPERVISOR I: DR. I. J. UWANTA CO – SUPERVISOR II: DR. MU’AZU MUSA DATE: 07TH NOVEMBER, 2012 TIME:

how many. Being a business of course the owner wants to maximize his profit margins as his objective. As for the constraints we will look at the space restriction, supplier limitations, and customer demands. In the end we should have an integer programming

categories of modeling techniques includes optimization techniques? Prescriptive models What is the goal in optimization? Find the decision variable values that result in the best objective function and 
satisfy all constraints. The following linear programming problem has been written to plan the production of two products. The company wants to maximize its

The linear programming problem is solved using excel solver and attached in Appendix ‘D’. Also, the problem has been solved using LINGO as depicted below: MAX = xa1 + xb1 + xa1 - 3*xa2 xa1 + xa2 + xa3 + xa1 > xa2 > xa3 > 0.0822*(xa1 + xb1)+ 0.066*(xa2

Managerial Decision Modeling w/ Spreadsheets, 3e (Balakrishnan/Render/Stair) Chapter 4 Linear Programming Sensitivity Analysis 4.1 Chapter Questions 1) Surplus is typically associated with which type of constraints? A) ≤ B) ≥ C) = D) ≠ E) ± Answer: B Page Ref: 124 Topic: Sensitivity Analysis Using Graphs Difficulty: Easy 2) Assume that the shadow price of a non-binding "≤" constraint is 5. This implies that: A) if the right-hand side value of the constraint increases by 1 unit, the objective