"Mathematical optimization" Essays and Research Papers

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    Management of High Tech Services (HTS) would like to develop a model that will help allocate its technician’s time between service calls to regular contract customers and new customers. A maximum of 80 hours of technician time is available over the two-week period. To satisfy cash flow requirements‚ at least $800 in revenue must be generated through technician time during the two-week period. Technician time for regular customers generates $25 per hour. However‚ technician time for new customers

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    Optimization Exam Paper

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    MATH-2640 MATH-264001 This question paper consists of 3 printed pages‚ each of which is identified by the reference MATH-2640 Only approved basic scientific calculators may be used. c UNIVERSITY OF LEEDS Examination for the Module MATH-2640 (January 2003) Introduction to Optimisation Time allowed: 2 hours Attempt four questions. All questions carry equal marks. In all questions‚ you may assume that all functions f (x1 ‚ . . . ‚ xn ) under consideration are sufficiently ∂2f ∂2f continuous to

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    ORIGINS OF OPERATIONS MANAGEMENT AND OPERATIONS RESEARCH comparison of the origins of operations management and operations research reveals that both are an innovation of the 20th century. The origin of operations research was in England‚ circa 1937‚ and has its roots in scientific management‚ with its first significant applications to military operations in both World War I and World War II. Operations management had its origins in the early factory system‚ and was more associated with physical

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    FORECASTING Managers are always trying to reduce uncertainty and make better estimates of what will happen in the future; this is the main purpose of forecasting. Some firms use subjective methods‚ seat-of-the pants methods‚ intuition‚ and experience. There are also several quantitative techniques‚ moving averages‚ exponential smoothing‚ trend projections‚ and least squares regression analysis. Eight steps to forecasting: * Determine the use of the forecast—what objective are we trying to

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    A theoretical approach to supply chain optimization through solution of an LP-problem 1. Key Questions • What are the number of mfg nodes‚ distr. nodes‚ cust. nodes and inventory nodes? [M‚ D‚ I] = N‚ locations [x] Where is demand located per manufacturing type / platform? [c] What are the reliability / infrastructure factors associated between each node? [r] What are the cost factors associated with each possible country & distribution node? [cc‚ dc] What is the overall flexibility from the

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    Multi-Objective Optimization

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    CHAPTER 1 INTRODUCTION Competitiveness in today’s marketplace depends heavily on the ability of a company to handle the several important challenges like reducing total supply chain operating cost and reducing lead-times‚ increasing customer service levels‚ and improving product quality. In Figure a typical non integral supply chain is shown‚ in which the goods flow starts as raw materials at natural resources and ends with products at final customers. Raw material winners keep

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    Mathematical modelling of a hyperboloid container Mathematical model is a method of simulating real-life situations with mathematical equations to forecast their future behaviour. Eykhoff (1974) defined a mathematical model as ’a representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in usable form’. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics‚ biology

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    Steven E. Shreve Stochastic Calculus for Finance I Student’s Manual: Solutions to Selected Exercises December 14‚ 2004 Springer Berlin Heidelberg NewYork Hong Kong London Milan Paris Tokyo Contents 1 1 Probability Theory on Coin Toss Space . . . . . . . . . . . . . . . . . . . . 7 2.9 Solutions to Selected Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 1.7 Solutions to Selected Exercises . . . . . . . . . . . . . . . . . . . . . . . .

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    Convex Optimization Convex Optimization Stephen Boyd Department of Electrical Engineering Stanford University Lieven Vandenberghe Electrical Engineering Department University of California‚ Los Angeles cambridge university press Cambridge‚ New York‚ Melbourne‚ Madrid‚ Cape Town‚ Singapore‚ S˜o Paolo‚ Delhi a Cambridge University Press The Edinburgh Building‚ Cambridge‚ CB2 8RU‚ UK Published in the United States of America by Cambridge University Press‚ New York http://www.cambridge

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    Project Report #1 (Advanced Operation Research) Title : Production Planning Optimization Using Goal Programming Method I. Introduction Today many companies that produce herbs because in the future opportunities for the herbal industry is still wide open and the public mind back to nature and go green‚ so consumer more like the various products especially natural herbal medicine. Therefore‚ in order to survive in existing market competition‚ PT. NM are required to

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