Optimal Decision Making In Operations Research And Statistics

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.

This textbook is comprised of detailed case studies covering challenging real world applications of OR techniques. Among the overall goals of the book is to provide readers with descriptions of the history and other background information on a variety of industries, service or other organizations in which decision making is an important component of their daily operations. The book considers all methods of optimum decision making in order to improve performances. It also compares possible solutions obtained by different approaches, concluding with a recommendation of the best among them for implementation. By exposing students to a variety of applications in a variety of areas and explaining how they can be modeled and solved, the book helps students develop the skills needed for modeling and solving problems that they may face in the workplace. Each chapter of "Case Studies in Operations Research: Applications of Optimal Decision Making" also includes additional data provided on the book’s website on Springer.com. These files contain a brief description of the area of application, the problem and the required outputs. Also provided are links to access all the data in the problem. Finally there are project exercises for students to practice what they have learnt in the chapter, which can also be used by instructors as project assignments in their courses.

Operation research is methods which allow us to produce an optimum plan under given conditions. This book is intended to help the readers, especially economists and planners, to understand the basis of these methods ...

Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.

Statistical distributions are essential tools to model the characteristics of datasets, such as right or left skewness, bi-modality or multi-modality observed in different applied sciences, such as engineering, medicine, and finance. The well-known distributions like normal, Weibull, gamma and Lindley are extensively used because of their simple forms and identifiability properties. In the last decade, researchers have focused on the more complex and flexible distributions, referred to as Generalized or simply G families of probability distributions, to increase the modelling capability of these distributions by adding one or more shape parameters. The main aim of this edited book is to present new contributions by researchers in the field of G families of probability distributions. The book will help researchers to: Develop new univariate continuous and discrete G families of probability distributions. Develop new bivariate continuous and discrete G families of probability distributions. Derive beneficial mathematical properties such as ordinary and incomplete moments, moment generating functions, residual life and reversed residual life functions, order statistics, quantile spread ordering and entropies, and some bivariate and multivariate extensions of the new and existing models using a simple-type copula.

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.