Decision Support Systems Decision Support Systems Edited by Chiang S. Jao Intech IV Published by Intech Intech Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the Intech, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 Intech Free online edition of this book you can find under www.sciyo.com Additional copies can be obtained from: publication@sciyo.com First published January 2010 Printed in India Technical Editor: Teodora Smiljanic Cover designed by Dino Smrekar Decision Support Systems, Edited by Chiang S. Jao p. cm. ISBN 978-953-7619-64-0 Preface 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: a knowledge base, a computerized model, and a 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. At the dawn of the 21st century, more sophisticated applications of computer-based DSS have been evolved and they have been adopted in diverse areas to assist in decision making and problem solving. Empirical evidence suggests that the adoption of DSS results in positive behavioral changes, significant error reduction, and the saving of cost and time. This book provides an updated view of the state-of-art computerized DSS applications. The book seeks to identify solutions that address current issues, to explore how feasible solutions can be obtained from DSS, and to consider the future challenges to adopting DSS. Overview and Guide to Use This Book 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. Our goals in writing this text were to introduce the basic concepts of DSS and to illustrate their use in a variety of fields. Chapter 1 first discusses the motivational framework that highlights the significance of motivational factor, a psychological construct, in explaining and facilitating the comprehension of DSS use and decision performance. The motivational framework translates several user-related factors (task motivation, user perception of a DSS, motivation to use DSS, DSS adoption, and decision performance) to the driving force in using DSS to improve task processing effectively and efficiently. To understand thoroughly the motivation framework will assist system designers and end users in reducing the barriers of system design and adoption. VI Chapters 2 and 3 introduce complicated decision support processes. Chapter 2 explores how to apply intelligent multi-agent paradigm architecture in DSS for the distributed environment. Intelligent multi-agent technology is adopted to develop DSS in enhancing the system operation in a dynamic environment and in supporting the adaptability of the system under complicated system requirements. An intelligent agent is capable to adapt DSS on the new situation through effective learning and reasoning. Employing multi-agents will simplify the complex decision making process and expedite the operation more efficiently. Chapter 3 applies a hybrid decision model using generic algorithm and fuzzy logic theory to provide decision makers the ability to formulate nearly optimal sets of knowledge base and to improve the efficiency of warehouse management. This model incorporates error measurement to reduce the complexity of process change during the development and selection of the best warehouse design for a given application. Chapter 4 reviews connectionist models of decision support in a clinical environment. These models connect the implementation and the adoption of DSS to establish effective medical management, maintenance and quality assurance and to predict potential clinical errors. These models aim to provide clinicians effective drug prescribing actions and to ensure prescription safety. The implementation of DSS accompanies the advantages of staff education and training to promote user acceptance and system performance. Chapter 5 integrates DSS with data mining (DM) methodology for customer relationship management (CRM) and global system for mobile communication (GSM) for the business service requirements. Data mining is appropriate for analyzing massive data to uncover hypothetical patterns in the data. A data mining DSS (DMDSS) offers an easy-to- use tool to enable business users to exploit data with fundamental knowledge, and assists users in decision making and continual data analysis. Chapter 6 highlights the importance of DSS evaluation using various testing methods. Integrating several testing methods would help detect primary errors generally found in the DSS adoption. A gold standard knowledge source is critical in choosing DSS testing methods. Correct use of these testing methods can detect significant errors in DSS. At this point, you are able to understand how to design and evaluate DSS for general purposes. Chapter 7 adopts artificial neural network (ANN) model in developing DSS for pharmaceutical formulation development. The use of ANNs provides the predictive “black- box” model function that supports the decision difficult to explain and justify because numerous system parameters are under consideration. Integrating DSS with ANNs applies data mining methodology and fuzzy logic algorithm, mentioned in Chapter 3, for decision making under multiple influential factors after performing statistical sensitivity analysis on feasible decision making mechanisms. The ANN in DSS is especially useful in improving drug substance original characteristics for optimized pharmaceutical formulation. Chapters 8 and 9 introduce the application of DSS in the clinical domain. Chapter 8 investigates the characteristics of clinical DSS (CDSS) and illustrates the architecture of a CDSS. An example of embedding CDSS implementation within computerized physician order entry (CPOE) and electronic medical record (EMR) is demonstrated. A CDSS aims to assist clinicians making clinical errors visible, augmenting medical error prevention and promoting patient safety. Chapter 9 introduces the importance of knowledge bases that provide useful contents for clinical decision support in drug prescribing. Knowledge bases are critical for any DSS in VII providing the contents. Knowledge bases aim to fulfill and be tailored timely to meet specific needs of end users. Standards are vital to communicate knowledge bases across different DSS so that different EMRs can share and exchange patient data on different clinical settings. Knowledge bases and CDSS have been proved to be helpful in daily decision making process for clinicians when instituting and evaluating the drug therapy of a patient. Chapters 10 and 11 introduce the concepts of spatial DSS. Chapter 10 introduces the framework of a web service-based spatial DSS (SDSS) that assists decision makers to generate and evaluate alternative solutions to semi-structured spatial problems through integrating analytical models, spatial data and geo-processing resources. This framework aims to provide an environment of resource sharing and interoperability technically through web services and standard interfaces so as to alleviate duplication problems remotely and to reduce related costs. Chapter 11 introduces another SDSS for banking industry by use of geographic information systems (GIS) and expert systems (ES) to decide the best place for locating a new commence unit in the banking industry. This SDSS aims to improve the decision making process in solving issues of choosing a new commence location for the banking industry, expanding possibilities through spatial analysis, and assisting domain experts in managing subjective tasks. Chapters 12 and 13 introduce DSS adoption in monitoring the environment. Chapter 12 introduces a web-based DSS for monitoring and reducing carbon dioxide (CO 2 ) emissions to the environment using an intelligent data management and analysis model to incorporate human expert heuristics and captured CO 2 emission data. Using object-linking and embedding (OLE) technology, this DSS aims to automatically filter and process massive raw data in reducing significant operating time. Chapter 13 illustrates case studies of Canadian environmental DSS (EDSS). The EDSS makes informed resource management decisions available to users after integrating scientific data, information, models and knowledge across multimedia, multiple-disciplines and diverse landscapes. The EDSS is also using GIS mentioned in Chapter 11 to deal with temporal and spatial consistency among different component models. The EDSS can solve complex environmental issues by providing informed resource and perform data analysis effectively. The schematic EDSS concepts of an EDSS can assist in developing a good EDSS with required functions to achieve the goals of environmental monitoring. Chapters 14 to 21 illustrate several examples of DSS adoption in diverse areas (including business partnership, internet search, wine management, agribusiness, internet data dependencies, customer order enquiry, construction industry, and disaster management) to solve problems in the current world. Chapter 14 presents a set of different DSS that extend the decision support process outside a single company. An automatic speech synthesis interface is adopted in the web- based DSS for the operational management of virtual organizations. Incorporating different business partners can provide decision support in multiple useful scenarios and extend the interoperability in a centralized cooperative and distributed environment. This trend is very useful to meet decision support requirements for global business in the 21st century. Chapter 15 introduces a DSS for analyzing prominent ranking auction markets for internet search services. This strategy has been broadly adopted by the internet search service provider like Google. This DSS aims to analyze ranking auction by the bidding VIII behavior of a set of business firms to display the searched information based on the ranking by bids strategy. You will be able to understand how the searching information being displayed on the internet by the searching engine, just like what you have seen by using Google Search. Chapter 16 introduces a DSS for evaluating and diagnosing unstructured wine management in the wine industry. This DSS offers effective performance assessment of a given winery and ranks the resource at the different levels of aggregation using statistical data. It aims in improved resource utilization and significant operational cost and time reduction. Fuzzy logic theory is adopted in the decision support process to compute a give winery performance in term of several dependent factors. Chapter 17 introduces a DSS adopted in agribusiness (hop industry) concerning issues related to personnel safety, environmental protection and energy saving. This DSS aims to monitor all functions of an agricultural process and to satisfy specific performance criteria and restrictions. Automation Agents DSS (AADSS) is adopted to support decision making in the range of the agribusiness operation, production, marketing and education. The AADSS facilitates the support to farmers in e-commence activities and benefits effective labor and time management, environmental protection, better exploitation of natural sources and energy saving. Chapter 18 introduces a framework for automating the building of diagnostic Bayesian Network (BN) model from online data sources using numerical probabilities. An example of a web-based online data analysis tool is demonstrated that allows users to analyze data obtained from the World Wide Web (WWW) for multivariate probabilistic dependencies and to infer certain type of causal dependencies from the data. You will be able to understand the concept in designing the user interface of DSS. Chapter 19 introduces a DSS based on knowledge management framework to process customer order enquiry. This DSS is provided for enquiry management to minimize cost, achieve quality assurance and enhance product development time to the market. Effective and robust knowledge management is vital to support decision making at the customer order enquiry stage during product development. This DSS highlights the influence of negotiation on customer due dates in order to achieve forward or backward planning to maximize the profit. Chapter 20 introduces a web-based DSS for tendering processes in construction industry. This DSS is used to benefit the security of tender documents and to reduce administrative workload and paperwork so as to enhance productivity and efficiency in daily responsibilities. This DSS is used in reducing the possibility of tender collusion. Chapter 21 introduces the concept of DSS used in disaster management based on principles derived from ecology, including preservation of ecological balance, biodiversity, reduction of natural pollution in air, soil and water, and exploitation of natural resources. This DSS provides complex environment management and public dissemination of environment-related information. The book concludes in Chapter 22 with the introduction of a theoretical DSS framework to secure a computer system. This CDSS framework adopts an accurate game-theoretic model to identify security primitives of a given network and assesses its security enhancement. Through the set-up of a game matrix, the DSS provides the capability of analysis, optimization and prediction of potential network vulnerability for security assessment. Five examples are provided to assist you in comprehending the concept of how to construct networks with optimal security settings for your computer system. IX It is exciting to work in the development of DSS that is increasingly maturing and benefits our society to some degree. There is still ample opportunity remaining for performance enhancement and user acceptance as new computer technologies evolve and more modern problems in the current world are being faced. In light of the increasing sophistication and specialization required in decision support, it is no doubt that the development of practical DSS needs to integrate multi-disciplined knowledge and expertise in diverse areas. This book is dedicated to providing useful DSS resources that produce useful application tools in decision making, problem solving, outcome improvement, and error reduction. The ultimate goals aim to promote the safety of beneficial subjects. Editor Chiang S. Jao National Library of Medicine United States [...]... Mining and Decision Support: An Integrative Approach 063 Rok Rupnik, Matjaž Kukar 6 Testing Methods for Decision Support Systems 087 Jean-Baptiste Lamy, Anis Ellini, Jérôme Nobécourt, Alain Venot, Jean-Daniel Zucker 7 Decision Support Systems for Pharmaceutical Formulation Development Based on Artificial Neural Networks 099 Aleksander Mendyk, Renata Jachowicz 8 Clinical Decision Support Systems: An... objective outcomes, better understanding of the decision problem, or user perception of the system’s usefulness (Lilien et al., 2004) Previous research on decision support has also used decision performance as a means of comparing systems (e.g., Lilien et al., 2004; Todd & Benbasat, 1994) and comparing other facets of decision support, such as 12 Decision Support Systems data representations (e.g., Vessey,... Chuanrong Zhang 11 Spatial Decision Support System for Bank-Industry Based on GIS and Expert Systems Integration 185 Ana Maria Carnasciali, Luciene Delazari 12 A Web-Based Data Management and Analysis System for CO2 Capture Process 201 Yuxiang Wu, Christine W Chan 13 Case Studies of Canadian Environmental Decision Support Systems 217 William Booty, Isaac Wong 14 Expanding Decision Support Systems Outside Company... 19 Decision Support System Based on Effective Knowledge Management Framework To Process Customer Order Enquiry 335 Chike F Oduoza 20 Decision Support for Web-based Prequalification Tender Management System in Construction Projects 359 Noor Maizura Mohamad Noor, Rosmayati Mohemad 21 Decision Support Systems used in Disaster Management 371 Marius Cioca, Lucian-Ionel Cioca 22 Security as a Game – Decisions... Framework: Insights into Decision Support System Use and Decision Performance V 001 Siew H Chan, Qian Song 2 New Architecture for Intelligent Multi-Agents Paradigm in Decision Support System 025 Noor Maizura Mohamad Noor, Rosmayati Mohemad 3 A Hybrid Decision Model for Improving Warehouse Efficiency in a Process-oriented View 035 Cassandra X.H Tang, Henry C.W Lau 4 Connectionist Models of Decision Making 049... can affect the riskiness of decision outcomes This theory suggests that the way information is presented may influence a user’s judgment or decision In addition, the cognitive fit theory (Vessey, 1991; Vessey & Galletta, 1991) 4 Decision Support Systems indicates that the level of complexity in a given task is reduced effectively when the problem-solving tools or techniques support the methods or processes... Wigoutschnigg 391 1 Motivational Framework: Insights into Decision Support System Use and Decision Performance Siew H Chan and Qian Song Washington State University United States 1 Introduction The purpose of this chapter is to discuss how characteristics of a decision support system (DSS) interact with characteristics of a task to affect DSS use and decision performance This discussion is based on the... M (1994) The Work Preference 18 Decision Support Systems Inventory: Assessing intrinsic and extrinsic motivational orientations Journal of Personality and Social Psychology, 66, 5, 950-967 Amer, T (1991) An experimental investigation of multi-cue financial information display and decision making Journal of Information Systems, 5, 18-34 Arnold, V (1997) Judgment and decision making, Part I: The impact... to expend on a Motivational Framework: Insights into Decision Support System Use and Decision Performance 3 Fig 1 A Motivational Framework for Understanding DSS Use and Decision Performance (Adapted from Chan (2005)) task by incorporating the ease of use characteristic into a DSS so that more effort can be allocated to other activities to improve decision performance DSS use may decline if increased... Implementation of a Decision Support System for Analysing Ranking Auction Markets for Internet Search Services 261 Juan Aparicio, Erika Sanchez, Joaquin Sanchez-Soriano, Julia Sancho 16 A Fuzzy – Based Methodology for Aggregative Waste Minimization in the Wine Industry 281 Ndeke Musee, Leon Lorenzen, Chris Aldrich 17 Prospects of Automation Agents in Agribusiness (Hop Industry) Decision Support Systems Related . Decision Support Systems Decision Support Systems Edited by Chiang S. Jao Intech IV . designed by Dino Smrekar Decision Support Systems, Edited by Chiang S. Jao p. cm. ISBN 978-953-7619-64-0 Preface Decision support systems (DSS) have evolved over. 7. Decision Support Systems for Pharmaceutical Formulation Development Based on Artificial Neural Networks 099 Aleksander Mendyk, Renata Jachowicz 8. Clinical Decision Support Systems: