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Posts Tagged ‘Optimization’

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As optimization techniques have developed, a gap has arisen between the people devising the methods and the people who actually need to use them. Research into methods is necessarily long-term and located usually in academic establishments; whereas the application of an optimization technique, normally in an industrial environment, has to be justified financially in the short term. The gap is probably inevitable; but there is no need for textbooks to reflect it. Tea… More >>

Optimization in Industry: Optimization techniques

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Special features of the book 1. A very comprehensive and accessible approach in the presentation of the material. 2. A variety of solved examples to illustrate the theoretical results. 3. A large number of unsolved exercises for the students are given for practice at the end of each section. 4. Solution to each unsolved examples are given at the end of each exercise. Readership: This book is written to meet the requirements of Engineering/Science and Manageme… More >>

Optimization Techniques in Operation Research

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This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
The vehicle scheduling problem, arising in public transport bus companies, addresses the task of assigning buses to cover a given set of timetabled trip… More >>

A time-space network based exact optimization model for multi-depot bus scheduling

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Biologically inspired optimization methods constitute a rapidly expanding field of research, with new applications appearing on an almost daily basis, as optimization problems of ever-increasing complexity appear in science and technology. This book provides a general introduction to such optimization methods, along with descriptions of the biological systems upon which the methods are based. The book also covers classical optimization methods, making it possible fo… More >>

Biologically Inspired Optimization Methods: An Introduction

New Simulation Tool Could Shorten Manufacturing Design Process
Novel research on improving the simulation performance of hardware models created in a language called SystemC, often used to shorten manufacturing design cycles to improve the time it takes to bring a product to the marketplace, has garnered a best paper award at the 15th Asia and South Pacific Design Automation Conference (ASP-DAC) for a team led by Sandeep Shukla, Virginia Tech associate …

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• ISBN13: 9780486402581
• Condition: NEW
• Notes: Brand New from Publisher. No Remainder Mark.

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This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NP-complete problems, more. All chapters are supplemented by thought-provoking problems. A useful work for gradua… More >>

Combinatorial Optimization: Algorithms and Complexity

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Modern Heuristic Optimization Techniques: Theory and Ap

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This completely revised and updated edition of Applied Risk Analysis includes new case studies in modeling risk and uncertainty as well as a new risk analysis CD-ROM prepared by Dr. Mun. On the CD-ROM you’ll find his Risk Simulator and Real Options Super Lattice Solver software as well as many useful spreadsheet models. “Johnathan Mun’s book is a sparkling jewel in my finance library. Mun demonstrates a deep understanding of the underlying mathematical… More >>

Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization Techniques

Verint Systems and Novantas Form Strategic Alliance for Retail Financial Services Workforce Optimization Solutions
NEW YORK & MELVILLE, N.Y.—-Following continued momentum for its Impact 360® Workforce Optimization solutions in the retail banking industry, including reinvestments by several large customers and new mid-market and community bank customers, Verint® Systems Inc. today announced a new strategic alliance with Novantas LLC focused on delivering comprehensive solutions to the retail banking sector.

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Town A is 30 kms east of town B. “Jo” drives to A twice a week, and B once a week. Where is the best place to have a house built so that travelling time will be minimised?
Assuming that house can be built anywhere on the straight line between A and B, I need to devise a formula, differentiate and set to 0 so that I can find the optimum place to build. The problem is, my formulas either differentiate to a number value or involve “speed” which is unknown. I’m missing something somewhere and would appreciate any suggestions!
Once I’ve solved this, I then have to take into account two journeys to town C situated 50kms north of town B, so the house would be built somwhere in the right angled triangle ABC.
Thanks in advance to anyone who could point me in the right direction!

The following is a summary of some of the more frequently used decision methods and an explanation of why they are flawed:

1. Follow the clicks: This is the most elementary of all decision methods. Under this method advertisers equate clicks with success. If a keyword is generating a lot of clicks then that ad must be working.

Advertisers who use this methodology will eliminate ads with little click activity and will spend more on keywords that generate the most clicks. This method completely ignores what matters most, PROFIT. Clicks don’t pay your bills, profits do. If you’re not making decisions based on conversions (at the very least), you will be quickly out of the online advertising game.

2. Conversions mean success: All online advertisers want their advertisements to convert to sales. But all conversions aren’t created equal. It is very possible that you can get a lot of conversions but still end up losing money. For example if you’re spending $20 to get a conversion but you are selling a $20 item, you’re losing money. Conversion rates alone do not tell you enough information to know which ads are working and which are not.

3. Products sales mean related ads are working: This assumption seems to make sense. If I am selling a lot of product A, then my ads for product A must be working. But some of the research suggests that over almost half of all purchase result from an unrelated search. Advertisers that employ this form of optimization are turning off ads that are the real drivers of their sales and spending more on ads related to items sold assuming that that relationship really exists.

4. Return on Ad Spend (ROAS): ROAS optimization requires a technology that can report on the revenue generated by each online ad. Using ROAS is a decent optimization method though it is far from perfect. ROAS ignores the fact that most companies don’t have a flat profit margin across all products. Therefore, you basically look at the revenue by keyword, multiply by your average profit margin, and subtract out the cost of the advertising. In some cases you will overvalue the performance on the ad if it sold items that were below your average margin. In other cases you will undervalue the ad if it sold items that were above your average profit margin. Finally, ROAS does not allow you to see the relationships between the products sold and the ad that was responsible.

There is only one decision method that is guaranteed to optimize the results of your online marketing campaigns, profit-based optimization This is best of all optimization methodologies; in fact it is the perfect method. Under this method an advertiser matches each sale and the products sold in that sale to a specific online ad. This method allows the advertiser to take into account the unique profit margin that each product has instead of using a flat margin in the ROAS method. By using the specific margin on each item sold an advertiser will never overvalue or undervalue an ad. They will be able to determine exactly how much profit each ad delivered.

When you understand which ads are your most profitable, then you know where to spend the majority of your ad dollars. You also know which ads are not worth investing in any longer because they are not profitable. This method removes any assumptions that the other methods force you to make.

Microsoft® Windows® 2000 Installation, Configuration and Administration Part 3: Resource Optimization CBT Training CD

Dear all, Please send me if you have a pdf format or scanned attachment or text that shows the Optimization Model for Operation of Aswan High Dam Reservoir. While surfing the internet, I noted there is a book on this issue. I need to know the process of formulating an optimization model for the operation of the Aswan High Dam Reservior.

Product Description
This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission… More >>

Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

Product Description
Metaheuristics for Hard Optimization comprises of three parts. The first part is devoted to the detailed presentation of the four most widely known metaheuristics: • the simulated annealing method, • tabu search, • the evolutionary algorithms, • ant colony algorithms. Each one of these metaheuristics is actually a family of methods, of which the essential elements are discussed. In the second part, the book presents some other less widespread metaheuris… More >>

Metaheuristics for Hard Optimization: Methods and Case Studies

Product Description
Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathe… More >>

Optimization Methods in Finance

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Provides the technology and applications of combined heating, cooling and power. Textbook. … More >>

Combined Heating, Cooling & Power Handbook: Technologies & Applications: An Integrated Approach to Energy Resource Optimization

Predictive analytics is a solution used by many businesses today to gain more value out of large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it’s customer base, it’s products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

Predictive models examine patterns found in historical and transactional data to identify opportunities and risks. Predictive models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

There are some basic and more complex predictive analytics techniques. Three basic techniques include:

-Data Profiling and Transformations
-Sequential Pattern Analysis
-Time Series Tracking

Data profiling and transformations are functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Sequential pattern analysis discovers relationships between rows of data. Sequential pattern analysis is used to identify frequently observed sequential occurrence of items across ordered transactions over time. Such a frequently observed sequential occurrence of items (called a sequential pattern) must satisfy a user-specified minimum support. Understanding long-term customer purchase behavior is an example of the sequential pattern analysis. Other examples include customer shopping sequences, click-stream sessions, and telephone calling patterns.

Time series tracking tracks metrics that represent key behaviors or business strategies. It is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Examples include patterning customer sales that indicate product satisfaction and buying habits, budgetary analysis, stock market analysis, census analysis, and workforce projections.

More advanced predictive analytics techniques include:

-Time Series Forecasting
-Data Profiling and Transformations
-Bayesian Analytics
-Regression
-Classification
-Dependency or Association Analysis
-Simulation
-Optimization

Time series forecasting predicts the future value of a measure based on past values. Time series forecasting uses a model to forecast future events based on known past events. Examples include stock prices and sales revenue.

Data profiling and transformation uses functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Bayesian analytics capture the concepts used in probability forecasting. It is a statistical procedure which estimate parameters of an underlying distribution based on the observed distribution. An example is used in a court setting by an individual juror to coherently accumulate the evidence for and against the guilt of the defendant, and to see whether, in totality, it meets their threshold for ‘beyond a reasonable doubt’.

Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another-the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate.

Classification used attributes in data to assign an object to a predefined class or predict the value of a numeric variable of interest. Examples include credit risk analysis, likelihood to purchase. Examples include acquisition, cross-sell, attrition, credit scoring and collections.

Clustering or segmentation separates data into homogeneous subgroups based on attributes. Clustering assigns a set of observations into subsets (clusters) so that observations in the same cluster are similar. An example is customer demographic segmentation.

Dependency or association analysis describes significant associations between data items. An example is market basket analysis. Market basket analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

Simulation models a system structure to estimate the impact of management decisions or changes. Simulation model behavior will change in each simulation according to the set of initial parameters assumed for the environment. Examples include inventory reorder policies, currency hedging, military training.

Optimization models a system structure in terms of constraints to find the best possible solution. Optimization models form part of a larger system which people use to help them make decisions. The user is able to influence the solutions which the model produces and reviews them before making a final decision as to what to do. Examples include scheduling of shift workers, routing of train cargo, and pricing airline seats.

Product Description
This book is specifically designed to change the way deterministic optimization is taught to introductory students. Toward this end, it exposes students to the broad scope of the topic, reinforces the basic principles, sparks students’ enthusiasm about the field, provides tools of immediate relevance and develops the skills necessary to use those tools…. More >>

Optimization in Operations Research

IBM’s new service line Business Analytics and Optimization is capturing information and turning it into intelligence. It’s identifying patterns faster, pulling insights from noise, converting data into action, analyzing, optimizing, mitigating, finding and preventing. Business Analytics and Optimization is helping people predict with greater confidence. www-935.ibm.com