Health care systems are complex and, as a result, it is often unclear what the effects of changes in policy or service provision might be. At the same time, resources for health care tend to be in short supply, which means that public health practitioners have to make difficult decisions. This book describes the quantitative and qualitative methods that can help decision-makers to structure and clarify difficult problems and to explore the implications of pursuing different options. The accompanying CD ROM provides the opportunity to try out some of the proposed solutions. The book examines: Models and decision-making in health care Methods for clarifying complex decisions Models for service planning and resource allocation Modelling for evaluating changes in systems Series Editors: Rosalind Plowman and Nicki Thorogood.

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.

The purpose of this book is to provide readers with an introduction to the fields of decision making, location analysis, and project and machine scheduling. The combination of these topics is not an accident: decision analysis can be used to investigate decision seenarios in general, location analysis is one of the prime examples of decision making on the strategic Ievel, project scheduling is typically concemed with decision making on the tactical Ievel, and machine scheduling deals with decision making on the operational Ievel. Some of the chapters were originally contributed by different authors, and we have made every attempt to unify the notation, style, and, most importantly, the Ievel of the exposition. Similar to our book on Integer Programming and Network Models (Eiselt and Sandblom, 2000), the emphasis of this volume is on models rather than solution methods. This is particularly important in a book that purports to promote the science of decision making. As such, advanced undergraduate and graduate students, as weil as practitioners, will find this volume beneficial. While different authors prefer different degrees of mathematical sophistication, we have made every possible attempt to unify the approaches, provide clear explanations, and make this volume accessible to as many readers as possible.

This book fills a void for a balanced approach to spreadsheet-based decision modeling. In addition to using spreadsheets as a tool to quickly set up and solve decision models, the authors show how and why the methods work and combine the user's power to logically model and analyze diverse decision-making scenarios with software-based solutions. The book discusses the fundamental concepts, assumptions and limitations behind each decision modeling technique, shows how each decision model works, and illustrates the real-world usefulness of each technique with many applications from both profit and nonprofit organizations. The authors provide an introduction to managerial decision modeling, linear programming models, modeling applications and sensitivity analysis, transportation, assignment and network models, integer, goal, and nonlinear programming models, project management, decision theory, queuing models, simulation modeling, forecasting models and inventory control models. The additional material files Chapter 12 Excel files for each chapter Excel modules for Windows Excel modules for Mac 4th edition errata can be found at https://www.degruyter.com/view/product/486941

Through years of consulting, operating their own companies, and working in a number of corporate environments, the authors have developed insights into which methods work and which do not in the "real world." The result of this experience is the development of these fundamental rules for building analysis models to solve complex corporate problems. This book describes some common-sense principles that can be used to ensure that the models and solutions that are developed meet the decision maker's needs. These principles will help you avoid mistakes and help you develop better usable solutions.