Modeling For Decision Support In Network Based Services

This book originated from several recent workshops and related activities conducted by the IFIP Working Group 7.6 on "Optimization-Based Computer Aided Modeling and Design." This group has been active for 20 years with the stated objective of developing ”high-performance computer-aided systems to support modeling, decision analysis, optimization and multi-criteria decision making.” Recently, the group has turned its attention to the application of modeling and optimization to service science, management and engineering (SSME). SSME is still a young research field searching for its theoretical underpinnings, and one which offers many opportunities for analytical modeling to not only advance the understanding, but also to help form the foundation of a new discipline. This book is the result of the group's introductory foray into the application of quantitative modeling to the nascent field of service science with special emphasis on the network aspects of services. The 11 papers presented are grouped into sections on Network Science, Comptational and Analytical Modeling, and Knowledge Science. They showcase the value of modeling in a new and timely context and provide many seeds for further exciting research.

Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

Decision support systems (DSS) are widely touted for their effectiveness in aiding decision making, particularly across a wide and diverse range of industries including healthcare, business, and engineering applications. The concepts, principles, and theories of enhanced decision making are essential points of research as well as the exact methods, tools, and technologies being implemented in these industries. From both a standpoint of DSS interfaces, namely the design and development of these technologies, along with the implementations, including experiences and utilization of these tools, one can get a better sense of how exactly DSS has changed the face of decision making and management in multi-industry applications. Furthermore, the evaluation of the impact of these technologies is essential in moving forward in the future. The Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering explores how decision support systems have been developed and implemented across diverse industries through perspectives on the technology, the utilizations of these tools, and from a decision management standpoint. The chapters will cover not only the interfaces, implementations, and functionality of these tools, but also the overall impacts they have had on the specific industries mentioned. This book also evaluates the effectiveness along with benefits and challenges of using DSS as well as the outlook for the future. This book is ideal for decision makers, IT consultants and specialists, software developers, design professionals, academicians, policymakers, researchers, professionals, and students interested in how DSS is being used in different industries.

Decision Support Systems: Frequently Asked Questions is the authoritative reference guide to computerized Decision Support Systems. Author Dan Power has spent almost 30 years building, studying and teaching others about computerized Decision Support Systems. Dr. Power is first and foremost a Decision Support evangelist and generalist. From his vantage point as editor of DSSResources.COM, he tracks a broad range of contemporary DSS topics. In this DSS FAQ, Dr. Power answers 83 frequently asked questions about computerized decision support systems. The FAQ covers a broad range of contemporary topics and the questions are organized into 8 chapters. DSS FAQ helps readers understand questions like: What is a DSS? What kind of DSS does Mr. X need? Does data modeling differ for a Data-Driven DSS? Is a Data Warehouse a DSS? Is tax preparation software an example of a DSS? What do I need to know about Data Warehousing/OLAP? What is a cost estimation DSS? What is a Spreadsheet-based DSS? Decision Support Systems: Frequently Asked Questions is a useful resource for IT specialists, students, professors and managers. It organizes important Ask Dan! questions (with answers) published in DSS News from 2000 through 2004.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.