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Modeling Operations Research and Business Analytics

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Release : 2024-08-30
Genre : Mathematics
Kind : eBook
Book Rating : 136/5 ( reviews)

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Book Synopsis Modeling Operations Research and Business Analytics by : William P Fox

Download or read book Modeling Operations Research and Business Analytics written by William P Fox. This book was released on 2024-08-30. Available in PDF, EPUB and Kindle. Book excerpt: This book provides sample exercises, techniques, and solutions to employ mathematical modeling to solve problems in Operations Research and Business Analytics. Each chapter begins with a scenario and includes exercises built on realistic problems faced by managers and others working in operations research, business analytics, and other fields employing applied mathematics. A set of assumptions is presented, and then a model is formulated. A solution is offered, followed by examples of how that model can be used to address related issues. Key elements of this book include the most common problems the authors have encountered over research and while consulting the fields including inventory theory, facilities' location, linear and integer programming, assignment, transportation and shipping, critical path, dynamic programming, queuing models, simulation models, reliability of system, multi-attribute decision-making, and game theory. In the hands of an experienced professional, mathematical modeling can be a powerful tool. This book presents situations and models to help both professionals and students learn to employ these techniques to improve outcomes and to make addressing real business problems easier. The book is essential for all managers and others who would use mathematics to improve their problem-solving techniques. No previous exposure to mathematical modeling is required. The book can then be used for a first course on modeling, or by those with more experience who want to refresh their memories when they find themselves facing real-world problems. The problems chosen are presented to represent those faced by practitioners. The authors have been teaching mathematical modeling to students and professionals for nearly 40 years. This book is presented to offer their experience and techniques to instructors, students, and professionals.

Business Analytics with Management Science Models and Methods

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Release : 2015
Genre : Business & Economics
Kind : eBook
Book Rating : 359/5 ( reviews)

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Book Synopsis Business Analytics with Management Science Models and Methods by : Arben Asllani

Download or read book Business Analytics with Management Science Models and Methods written by Arben Asllani. This book was released on 2015. Available in PDF, EPUB and Kindle. Book excerpt: This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems.

Introduction to Business Analytics Using Simulation

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Release : 2022-02-06
Genre : Business & Economics
Kind : eBook
Book Rating : 173/5 ( reviews)

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Book Synopsis Introduction to Business Analytics Using Simulation by : Jonathan P. Pinder

Download or read book Introduction to Business Analytics Using Simulation written by Jonathan P. Pinder. This book was released on 2022-02-06. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making Explains the processes needed to develop, report and analyze business data Describes how to use and apply business analytics software Offers expanded coverage on the value and application of prescriptive analytics Includes a wealth of illustrative exercises that are newly organized by difficulty level Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition

Mathematical Modeling for Business Analytics

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Release : 2017-12-15
Genre : Business & Economics
Kind : eBook
Book Rating : 230/5 ( reviews)

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Book Synopsis Mathematical Modeling for Business Analytics by : William P. Fox

Download or read book Mathematical Modeling for Business Analytics written by William P. Fox. This book was released on 2017-12-15. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Modeling for Business Analytics is written for decision makers at all levels. This book presents the latest tools and techniques available to help in the decision process. The interpretation and explanation of the results are crucial to understanding the strengths and limitations of modeling. This book emphasizes and focuses on the aspects of constructing a useful model formulation, as well as building the skills required for decision analysis. The book also focuses on sensitivity analysis. The author encourages readers to formally think about solving problems by using a thorough process. Many scenarios and illustrative examples are provided to help solve problems. Each chapter is also comprehensively arranged so that readers gain an in-depth understanding of the subject which includes introductions, background information and analysis. Both undergraduate and graduate students taking methods courses in methods and discrete mathematical modeling courses will greatly benefit from using this book. Boasts many illustrative examples to help solve problems Provides many solutions for each chapter Emphasizes model formulation and helps create model building skills for decision analysis Provides the tools to support analysis and interpretation

Big Data Analytics Using Multiple Criteria Decision-Making Models

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Release : 2017-07-12
Genre : Computers
Kind : eBook
Book Rating : 752/5 ( reviews)

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Book Synopsis Big Data Analytics Using Multiple Criteria Decision-Making Models by : Ramakrishnan Ramanathan

Download or read book Big Data Analytics Using Multiple Criteria Decision-Making Models written by Ramakrishnan Ramanathan. This book was released on 2017-07-12. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.

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