Modelling And Simulation In Management Sciences

This book includes a collection of selected papers presented at the International Conference on Modelling and Simulation in Engineering, Economics, and Management, held at the Faculty of Economics and Business at the University of Girona, Spain, 28-29 June 2018.The conference was organized by the Association for the Advancement of Modelling and Simulation Techniques in Enterprises (AMSE) and the University of Girona with the aim of promoting research in the field of modelling, simulation and management science. This book presents original research studies related to fuzzy logic, soft computing and uncertainty, as well as a number of papers in the field of bibliometrics in social sciences. Presenting new advances in these areas, with a special focus on management, economics and social sciences. It is of great interest to researchers and Ph.D. students working in the field of fuzzy logic, soft computing, uncertainty and bibliometrics.

Computer Simulation in Management Science Michael Pidd The Management School. University of Lancaster, UK The fourth edition of this book reflects its continued popularity and standing in the field. It provides a clear guide to the role of modelling in the computer simulation methods used in management science. Readers will find an in-depth coverage of the modelling, computing and statistical aspects of discrete simulation and systems dynamics. Part I is a general introduction to the simulation methods commonly used in management science. Part II gives a detailed exposition of discrete event simulation, and Part III provides a description of the methods of system dynamics as an approach to policy modelling within organisations. Overall, the book shows why computer simulation within organisations. Overall, the book shows why computer simulation models are popular and gives a thorough guide to their construction and use. Revisions to this edition include a completely new chapter on computer simulation in practice, which discusses how best to make use of computer simulation models in achieving real benefits within organisations. Updated areas include: *three-phase and other methods *sampling methods *output analysis and experimentation *discrete simulation software *system dynamics simulation There are also links to software libraries in Turbo Pascal, C, C++, Visual BASIC and Java on the World Wide Web.

This book is a comprehensive introduction of the reader into the simulation and modelling techniques and their application in the management of organisations. The book is rooted in the thorough understanding of systems theory applied to organisations and focuses on how this theory can apply to econometric models used in the management of organisations. The econometric models in this book employ linear and dynamic programming, graph theory, queuing theory, game theory, etc. and are presented and analysed in various fields of application, such as investment management, stock management, strategic decision making, management of production costs and the lifecycle costs of quality and non-quality products, production quality Management, etc.

Table of contents

Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. The book can serve as the primary text for a variety of courses; for example: • A first course in simulation at the junior, senior, or beginning-graduate-student level in engineering, manufacturing, business, or computer science (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses. • A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research. • An introduction to simulation as part of a general course in operations research or management science (part of Chaps. 1, 3, 5, 6, and 9).

Simulation Modelling has been used for many years in the manufacturing sector but has now become a mainstream tool in business situations. This is partly because of the popularity of Business Process Reengineering (BPR) and other process based improvement methods that use simulation to help analyse changes in process design. This text book includes case studies in both manufacturing and service situations to demonstrate the usefulness of the approach. A further reason for the increasing popularity of the technique is the development of business orientated and user-friendly windows-based software. This text provides a guide to the use of ARENA, SIMUL8 and WITNESS simulation software systems which are widely used in industry and available to students. Overall this text provides a practical guide to building and implementing the results from a simulation model. All the steps in a typical simulation study are covered including data collection, input data modelling and experimentation.

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This must-read text/reference provides a practical guide to processes involved in the development and application of dynamic simulation models, covering a wide range of issues relating to testing, verification and validation. Illustrative example problems in continuous system simulation are presented throughout the book, supported by extended case studies from a number of interdisciplinary applications. Topics and features: provides an emphasis on practical issues of model quality and validation, along with questions concerning the management of simulation models, the use of model libraries, and generic models; contains numerous step-by-step examples; presents detailed case studies, often with accompanying datasets; includes discussion of hybrid models, which involve a combination of continuous system and discrete-event descriptions; examines experimental modeling approaches that involve system identification and parameter estimation; offers supplementary material at an associated website.