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Modelling Operations Research and Business Analytics

Author: William P Fox

Publisher:

Published: 2024-08-19

Total Pages: 0

ISBN-13: 9781032735924

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 consulting 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 for 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 forty years. This book is presented to offer their experience and techniques to instructors, students, and professionals.

Big Data Analytics Using Multiple Criteria Decision-Making Models

Author: Ramakrishnan Ramanathan

Publisher: CRC Press

Published: 2017-07-12

Total Pages: 370

ISBN-13: 1498753752

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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.

Business Analytics with Management Science Models and Methods

Author: Arben Asllani

Publisher: Pearson Education

Published: 2015

Total Pages: 401

ISBN-13: 0133760359

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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.

Mathematical Modeling for Business Analytics

Author: William P. Fox

Publisher: CRC Press

Published: 2017-12-15

Total Pages: 336

ISBN-13: 1351368230

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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

Operations Management and Data Analytics Modelling

Author: Lalit Kumar Awasthi

Publisher: CRC Press

Published: 2021-12-30

Total Pages: 206

ISBN-13: 1000530744

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Operations Management and Data Analytics Modelling: Economic Crises Perspective addresses real operation management problems in thrust areas like the healthcare and energy management sectors and Industry 4.0. It discusses recent advances and trends in developing data-driven operation management-based methodologies, big data analysis, application of computers in industrial engineering, optimization techniques, development of decision support systems for industrial operation, the role of a multiple-criteria decision-making (MCDM) approach in operation management, fuzzy set theory-based operation management modelling and Lean Six Sigma. Features Discusses the importance of data analytics in industrial operations to improve economy Provides step-by-step implementation of operation management models to identify best practices Covers in-depth analysis using data-based operation management tools and techniques Discusses mathematical modelling for novel operation management models to solve industrial problems This book is aimed at graduate students and professionals in the field of industrial and production engineering, mechanical engineering and materials science.

Introduction to Business Analytics Using Simulation

Author: Jonathan P. Pinder

Publisher: Academic Press

Published: 2022-03-15

Total Pages: 475

ISBN-13: 9780323917179

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Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It 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 the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. 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, newly organized by difficulty level Winner of the 2017 Textbook and Academic Authors Association (TAA) Most Promising New Textbook Award in the prior edition

Essentials of Business Analytics

Author: Bhimasankaram Pochiraju

Publisher: Springer

Published: 2019-07-10

Total Pages: 971

ISBN-13: 3319688375

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This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters. The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text. Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter.

Simulation Modelling

Author: Andrew Greasley

Publisher: Taylor & Francis

Published: 2022-09-21

Total Pages: 414

ISBN-13: 1000648826

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While simulation has a vast area of application, this textbook focuses on the use of simulation to analyse business processes. It provides an up-to-date coverage of all stages of the discrete-event simulation (DES) process, covering important areas such as conceptual modelling, modelling input data, verification and validation and simulation output analysis. The book is comprehensive yet uncomplicated, covering the theoretical aspects of the subject and the practical elements of a typical simulation project, demonstrated by cases, examples and exercises. It also shows how simulation relates to new developments in machine learning, big data analytics and conceptual modelling techniques. Guidance is provided on how to build DES models using the Arena, Simio and Simul8 simulation software, and tutorials for using the software are incorporated throughout. Simulation Modelling offers a uniquely practical and end-to-end overview of the subject, which makes it perfect required or recommended reading for advanced undergraduate and postgraduate students studying business simulation and simulation modelling as part of operations research, business analytics, supply chain management and computer science courses.

Metaheuristics for Business Analytics

Author: Abraham Duarte

Publisher: Springer

Published: 2017-11-24

Total Pages: 136

ISBN-13: 3319681192

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This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code. Extra Material can be downloaded at http://extras.springer.com/978-3-319-68117-7.

Operations Research: Introduction To Models And Methods

Author: Richard Johannes Boucherie

Publisher: World Scientific

Published: 2021-10-26

Total Pages: 512

ISBN-13: 9811239363

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This attractive textbook with its easy-to-follow presentation provides a down-to-earth introduction to operations research for students in a wide range of fields such as engineering, business analytics, mathematics and statistics, computer science, and econometrics. It is the result of many years of teaching and collective feedback from students.The book covers the basic models in both deterministic and stochastic operations research and is a springboard to more specialized texts, either practical or theoretical. The emphasis is on useful models and interpreting the solutions in the context of concrete applications.The text is divided into several parts. The first three chapters deal exclusively with deterministic models, including linear programming with sensitivity analysis, integer programming and heuristics, and network analysis. The next three chapters primarily cover basic stochastic models and techniques, including decision trees, dynamic programming, optimal stopping, production planning, and inventory control. The final five chapters contain more advanced material, such as discrete-time and continuous-time Markov chains, Markov decision processes, queueing models, and discrete-event simulation.Each chapter contains numerous exercises, and a large selection of exercises includes solutions.