Decision and Prediction Analysis Powered With Operations Research

Decision and Prediction Analysis Powered With Operations Research
Author: Bubevski, Vojo
Publisher: IGI Global
Total Pages: 298
Release: 2024-07-16
Genre: Business & Economics
ISBN:

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Organizations today face complex decisions and uncertainties that can have a profound impact on their financial stability and strategic direction. Traditional decision-making methods often fall short when it comes to addressing multifaceted issues like financing, product manufacturing, and facility location. These challenges demand a robust framework that quantifies factors, assesses risks, and provides optimal solutions. Without advanced tools and techniques, businesses are at risk of making uninformed decisions that could lead to significant financial losses and missed opportunities. The urgency to equip yourself with these tools is clear. Decision and Prediction Analysis Powered With Operations Research offers a comprehensive solution to these challenges. This book integrates operations research techniques to reframe and solve complex business problems. It provides a detailed exploration of decision analysis tools, such as influence diagrams and decision trees, which help visualize and assess various decision scenarios. By applying these tools, organizations can better understand uncertainties, evaluate risks, and make decisions that maximize expected utility and achieve strategic objectives.

Optimal Decision Making in Operations Research and Statistics

Optimal Decision Making in Operations Research and Statistics
Author: Irfan Ali
Publisher: CRC Press
Total Pages: 434
Release: 2021-11-29
Genre: Business & Economics
ISBN: 1000404722

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The book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decision­making problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics.

NASA SP-7500

NASA SP-7500
Author: United States. National Aeronautics and Space Administration
Publisher:
Total Pages: 156
Release: 1973
Genre:
ISBN:

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Operations Research Models and Methods

Operations Research Models and Methods
Author: Paul A. Jensen
Publisher: John Wiley & Sons
Total Pages: 708
Release: 2002-10-08
Genre: Technology & Engineering
ISBN: 0471380040

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In a rapidly developing field like Operations Research, its easy to get overwhelmed by the variety of topics and analytic techniques. Paul Jensen and Jonathan Bard help you master the expensive field by focusing on the fundamental models and methodologies underlying the practice of Operations Research. Bridging the gap between theory and practice, the author presents the quantitative tools and models most important to understanding modern operations research. You'll come to appreciate the power of OR techniques in solving real-world problems and applications in your own field. You'll learn how to translate complex situations into mathematical models, solve models and turn models into solutions. This text is designed to bridge the gap between theory and practice by presenting the quantitative tools and models most suited for modern operations research. The principal goal is to give analysts, engineers, and decision makers a larger appreciation of their roles by defining a common terminology and by explaining the interfaces between the underlying methodologies. Features Divides each subject into methods and models, giving you greater flexibility in how you approach the material. Concise and focused presentation highlights central ideas. Many examples throughout the text will help you better understand mathematical material.

Predictive and Prescriptive Analytics in Operations Management

Predictive and Prescriptive Analytics in Operations Management
Author: Omar Skali Lami
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:

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The recent surge in data availability and advances in hardware and software and the recent developments and democratization of analytics highlight the critical importance of prediction and prescription in harnessing the power of data to create value through optimal, data-driven decision making. This thesis proposes novel Machine Learning (ML) and optimization methods in (i) predictive analytics, (ii) prescriptive analytics, and (iii) their high-impact applications in operations management.

Operations Research in Progress

Operations Research in Progress
Author: Gustav Feichtinger
Publisher: Springer Science & Business Media
Total Pages: 512
Release: 2012-12-06
Genre: Social Science
ISBN: 9400979010

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Proceedings of the Joint Meeting of the Austrian and Swiss Operations Research Societies, Vienna, September 22-24, 1980

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
Author: Qing Duan
Publisher: Springer
Total Pages: 165
Release: 2015-06-13
Genre: Technology & Engineering
ISBN: 3319187384

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This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.

Applications of Operations Research and Management Science for Military Decision Making

Applications of Operations Research and Management Science for Military Decision Making
Author: William P. Fox
Publisher: Springer Nature
Total Pages: 498
Release: 2019-08-28
Genre: Business & Economics
ISBN: 303020569X

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Based on many years of applied research, modeling and educating future decision makers, the authors have selected the critical set of mathematical modeling skills for decision analysis to include in this book. The book focuses on the model formulation and modeling building skills, as well as the technology to support decision analysis. The authors cover many of the main techniques that have been incorporated into their three-course sequence in mathematical modeling for decision making in the Department of Defense Analysis at the Naval Postgraduate School. The primary objective of this book is illustrative in nature. It begins with an introduction to mathematical modeling and a process for formally thinking about difficult problems, illustrating many scenarios and illustrative examples. The book incorporates the necessary mathematical foundations for solving these problems with military applications and related military processes to reinforce the applied nature of the mathematical modeling process.

Product Research

Product Research
Author: N. R. Srinivasa Raghavan
Publisher: Springer Science & Business Media
Total Pages: 304
Release: 2010-03-11
Genre: Technology & Engineering
ISBN: 9048128609

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7. 1. 1 Background Uncertainty can be considered as the lack of adequate information to make a decision. It is important to quantify uncertainties in mathematical models used for design and optimization of nondeterministic engineering systems. In general, - certainty can be broadly classi?ed into three types (Bae et al. 2004; Ha-Rok 2004; Klir and Wierman 1998; Oberkampf and Helton 2002; Sentz 2002). The ?rst one is aleatory uncertainty (also referred to as stochastic uncertainty or inherent - certainty) – it results from the fact that a system can behave in random ways. For example, the failure of an engine can be modeled as an aleatory uncertaintybecause the failure can occur at a random time. One cannot predict exactly when the engine will fail even if a large quantity of failure data is gathered (available). The second one is epistemic uncertainty (also known as subjective uncertainty or reducible - certainty) – it is the uncertainty of the outcome of some random event due to lack of knowledge or information in any phase or activity of the modeling process. By gaining information about the system or environmental factors, one can reduce the epistemic uncertainty. For example, a lack of experimental data to characterize new materials and processes leads to epistemic uncertainty.