Models And Methods For Management Science

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.

Models & Methods for Project Selection systematically examines in this book treatment the latest work in the field of project selection modeling. The models presented are drawn from mathematical programming, decision theory, and finance. These models are examined in two categorical streams: the management science stream and the financial model stream. The book describes the assumptions and limitations of each model and provides appropriate solution methodologies. Its organization follows three main themes: *Criteria for Choice: Chapters 1-3 investigate the effect of the choice of optimization criteria on the results of the portfolio optimization problem. *Risk and Uncertainty: Chapters 4-7 deal with uncertainty in the project selection problem. *Non-Linearity and Interdependence: These chapters deal with problems of non-linearity and interdependence as they arise in the project selection problem. Chapters 8, 9 and 10 present solution methodologies, which can be used to solve these most general project selection models.

This textbook introduces systems science as an entry point to present a basic introduction to research models and methods in management science (operation research). This textbook selects the classic quantitative models and methods as well as rich cases and detailed examples, which are suitable for students with a certain management and economics knowledge for further study, and helps to develop the abilities of using the basic models in real life.

Combines topics from two traditionally distinct quantitative subjects, probability/statistics and management science/optimization, in a unified treatment of quantitative methods and models for management. Stresses those fundamental concepts that are most important for the practical analysis of management decisions: modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources optimally.

This work explores differing historical patterns in the adoption of the three major models of organizational management: scientific management; human relations; and structural analysis. The author takes a fresh look at how managers have used these models in four countries during the 20th century.

This book presents research tested models, methods and tools that can make the work of the facilities manager more robust and sustainable, help long-term strategic planning and support students and practitioners in FM to improve the way they approach and deal with challenges in practice. The 34 models, methods and tools are presented in relation to five typical challenges for facilities managers: Strategy development Organisational design Space planning Building projects Optimisation The chapters are short and concise, presenting a central illustration of one model, method or tool with explanatory text and short, exemplary case studies. Each chapter includes references to further reading, and the book includes a keyword index. Essential reading for all involved in the management of built assets, this book bridges the gap between robust academic research and practical industry tools. It can also be used as a handy student reference.

Operations Research (OR) began as an interdisciplinary activity to solve complex military problems during World War II. Utilizing principles from mathematics, engineering, business, computer science, economics, and statistics, OR has developed into a full fledged academic discipline with practical application in business, industry, government and m

The Analytic Hierarchy Process (AHP) is a prominent and powerful tool for making decisions in situations involving multiple objectives. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, 2nd Edition applies the AHP in order to solve problems focused on the following three themes: economics, the social sciences, and the linking of measurement with human values. For economists, the AHP offers a substantially different approach to dealing with economic problems through ratio scales. Psychologists and political scientists can use the methodology to quantify and derive measurements for intangibles. Meanwhile researchers in the physical and engineering sciences can apply the AHP methods to help resolve the conflicts between hard measurement data and human values. Throughout the book, each of these topics is explored utilizing real life models and examples, relevant to problems in today’s society. This new edition has been updated and includes five new chapters that includes discussions of the following: - The eigenvector and why it is necessary - A summary of ongoing research in the Middle East that brings together Israeli and Palestinian scholars to develop concessions from both parties - A look at the Medicare Crisis and how AHP can be used to understand the problems and help develop ideas to solve them.

Businesses have to cut costs, increase revenue and be profitable. The aim of this book is to introduce Management Science to analyse business challenges and to find solutions analytically. Important topics in modelling, optimisation and probability are covered. These include: linear and integer programming, network flows and transportation; essential statistics, queueing systems and inventory models. The overall objectives are: to enable the reader to increase the efficiency and productivity of businesses; to observe and define challenges in a concise, precise and logical manner; to be familiar with a number of classical and state-of-the art operational research techniques and tools; to devise solutions, algorithms and methods that offer competitive advantage to businesses and organisations; and to provide results to management for decision making and implementation. Numerous examples and problems with solutions are given to demonstrate how these concepts can be applied in a business context.

Introduction to Management Science, 2e offers a unique case study approach and integrates the use of Excel. Each chapter includes a case study that is meant to show the students a real and interesting application of the topics addressed in that chapter. This most recent revision has been thoroughly updated to be more "user-friendly" and more technologically advanced. These changes include, a completely new chapter on the art of modeling with spreadsheets. This unique chapter goes far beyond anything found in other textbooks and are based on the award winning methodologies used by Mark Hillier in his own course. The technology package has also been greatly enhanced to include, Crystal Ball 2000 (Professional Edition) a Management Science Online Learning Center, and an Excel add-in called Alver Table for performing sensitivity analysis. Crystal Ball is the most popular Excel add-in for computer simulation and includes OptQuest (an optimizer with simulation) as well as a forecasting module. The Management Science Online Learning Center (website) includes several modules that enable students to interactively explore certain management science techniques in depth. Solver Table is an Excel add-in developed by the author to help perform sensitivity analysis systematically, as well as substantially expanded coverage of computer simulation, including Crystal Ball. We now have two chapters on computer simulation instead of one, where the second chapter features the use of Crystal Ball.all.