Computer Simulation In Management Science

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 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.

This Handbook is a collection of chapters on key issues in the design and analysis of computer simulation experiments on models of stochastic systems. The chapters are tightly focused and written by experts in each area. For the purpose of this volume “simulation refers to the analysis of stochastic processes through the generation of sample paths (realization) of the processes. Attention focuses on design and analysis issues and the goal of this volume is to survey the concepts, principles, tools and techniques that underlie the theory and practice of stochastic simulation design and analysis. Emphasis is placed on the ideas and methods that are likely to remain an intrinsic part of the foundation of the field for the foreseeable future. The chapters provide up-to-date references for both the simulation researcher and the advanced simulation user, but they do not constitute an introductory level ‘how to’ guide. Computer scientists, financial analysts, industrial engineers, management scientists, operations researchers and many other professionals use stochastic simulation to design, understand and improve communications, financial, manufacturing, logistics, and service systems. A theme that runs throughout these diverse applications is the need to evaluate system performance in the face of uncertainty, including uncertainty in user load, interest rates, demand for product, availability of goods, cost of transportation and equipment failures. * Tightly focused chapters written by experts * Surveys concepts, principles, tools, and techniques that underlie the theory and practice of stochastic simulation design and analysis * Provides an up-to-date reference for both simulation researchers and advanced simulation users

Current Issues in Computer Simulation is a collection of papers dealing with computer simulation languages, statistical aspects of simulation, linkage with optimization and analytical models, as well as theory and application of simulation methodology. Some papers explain the General Purpose Simulation System (GPSS), a programming package incorporating a language to simulate discrete systems; and the SIMSCRIPT, a general-purpose simulation language using English commands, for example, FORTRAN. Another simulation language is the General Activity Simulation Program (GASP), providing for an organizational structure to build models to simulate the dynamic performance of systems on a digital computer. Other papers discuss simulation models of real systems, including corporate simulation models, multistage consumer choice process, determination of maximum occupancy for hospital facilities, and the juvenile court system. Many computer simulations are statistical sampling experiments performed on a model of the system under investigation. Other papers discuss some of the variables involved in the statistical design and analysis of simulation experiments such as variance reduction techniques, generation of random variates, and experimental layout. For example, one application simulates inventory systems when many items are stocked in various locations. The collection is suitable for programmers, computer engineers, businessmen, hospital administrators, schools officials, and depositories of huge volumes of information or data.

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A key goal of fisheries management is to regulate extractive pressure on a resource so as to ensure social, economic and ecological sustainability. This text provides an accessible entry point for students and professionals to management science as developed in fisheries, in order to facilitate uptake of the latest ideas and methods. Traditional management approaches have relied upon a stock assessment based on existing understanding of resource status and dynamics, and a prediction of the likely future response to a static management proposal. However all such predictions include an inherent degree of uncertainty, and the last few decades have seen the emergence of an adaptive approach that uses feedback control to account for unknown future behaviour. Feedback is achieved via a control rule, which defines a relationship between perceived status of the resource and a management action. Evaluations of such rules usually include computer simulation testing across a broad range of uncertainties, so that an appropriate and robust rule can be selected by stakeholders and managers. The book focuses on this approach, which is usually referred to as Management Strategy Evaluation. The book is enriched by case study examples from different parts of the world, as well as insights into the theory and practice from those actively involved in the science of fisheries management.

"This book investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation, exploring different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product"--