Production decision support system for computerized manufacturing systems

The introduction of Computerized Manufacturing Systems (CMS) into the mid-volume manufacturing industry has been an important new step in the development of automated manufacturing. CMS's are computer-integrated systems which incorporate Direct Numerical Control (DNC) capabilities with automatic material handling to manufacture parts. Due to its complex nature, the task of production control in an CMS cannot rely on human effort alone. It is essential to develop a software package in the form of a Production Decision Support System (PDSS) to assist the production decision maker in operating this complex manufacturing facility.     

The roles of computerized support systems: a decision subprocess-based analysis

This paper analyses the potential roles of computerized systems in supporting the decision-making process. Toward this end, we propose an expository process model of decision-making and develop a framework that provides the infrastructure for the analysis. The proposed process model draws on two well-known models in the literature and enumerates eight distinct phases in decision-making. The framework developed is based on an integration of this process model with Simon's notion of 'decision-structuredness'. Unlike any of the existing frameworks, the suggested framework permits a micro-level analysis of support system roles. The analysis is intended as a pre-design guide, to help systems developers and users to identify support potential and possibilities, and to target their activities accordingly.     

An associative memory approach to medical decision support systems

Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.     

Fuzzy cognitive map architectures for medical decision support systems

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.     

A medical decision support system for disease diagnosis under uncertainty

This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians’ expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.     

A web-based high-performance multicriteria decision support system for medical diagnosis

Multicriteria decision making (MCDM) methods can be powerful aids for evaluating patients' medical information in medical diagnostic systems. Technique ordered preference by similarity to the ideal solution (TOPSIS) is one of the more widely used MCDM methods in decision support systems. For the purpose of this work, the TOPSIS method is modified into a more suitable form and used for the implementation of a web-based medical diagnostic system. In our modified TOPSIS method, we have utilized fuzzy logic so that users can more accurately describe their symptoms. The data given to the modified TOPSIS method are often massive in proportions and may take a considerable amount of time to generate a ranking of alternatives. TOPSIS lends itself to parallel computation because it is virtually a combination of matrix computations. Therefore, computer parallelism is implemented so that a large amount of input data can be handled simultaneously, hence decreasing overall execution time. In addition, to make our MCDM system more accessible, we have designed our system to be web based. The web-based medical diagnosis system includes a dynamically generated web-based user interface, while the parallel implementation of the modified TOPSIS component, in conjunction with the Common Gateway Interface, forms the back end of the system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1083–1099, 2007.     

The use of computer-based information systems by German managers to support decision making

We surveyed German managers to investigate their use of information technology (IT), including their perception of the value provided by computer-based information systems (CBIS) and their satisfaction with CBIS in supporting decisions. Participants were predominantly male, the majority were older than 45, most had taken two or more college computer courses, and over 90% were middle and top-level managers. They represented a variety of functional areas. Results show that these managers were relatively heavy IT users, with over 10 h of use per week. Overall, there was a significant correlation between the amount of use of IT and perceived value of the CBIS, as well as between satisfaction with and perceived value of the CBIS. Hypothesis tests for task-technology fit (TTF) showed that the greatest TTF was related to resource allocation, evaluating alternatives, identifying problems, and making short-term decisions. For middle and top managers, information reporting systems (IRS), which provide regularly scheduled reports, had greater TTF than more flexible inquiry and analysis systems, such as decision support systems (DSS).     

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes.     

A multilayer perceptron-based medical decision support system for heart disease diagnosis

The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (>90%) and comparably small intervals (<5%), proving its usefulness in support of clinic decision process of heart diseases.     

The effect of positive mood on intention to use computerized decision aids

While psychology research has indicated that positive mood enhances cognition and behavior, MIS researchers have largely ignored such potential effects on user acceptance of new IT. Using two cognitive theories on mood and memory, positive mood theory and the affect infusion model (AIM), we developed hypotheses about the contribution of these mood conditions on user acceptance of new IT with two levels of uncertainty. These hypotheses were investigated via an experiment using a computerized decision making aid. We found that positive mood increased acceptance, under both levels of uncertainty. These results held for both induced and naturally occurring positive mood. The results were consistent with positive mood theory but not with the AIM.     

Computing agents for decision support systems

In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional computing resources instead of additional time. In this paper, we show how multi-agent systems can fulfil these requirements. We recall as an example the concept of Evolutionary Multi-Agent Systems, which combines evolutionary and agent computing paradigms. We describe several possible implementations and present experimental results demonstrating how additional resources improve the efficacy of such systems.     

Graded logic for decision support systems

We present the soft computing graded logic (GL) as a mathematical infrastructure for a soft computing propositional calculus, and GL aggregation. The GL aggregation of degrees of truth (or fuzzy membership) is a crucial component of complex criterion functions used in decision support systems. In the context of GL we propose aggregation functions that integrate means and t-norms/conorms, combining logic and probabilistic reasoning. We also propose a set of necessary and sufficient basic logic functions (models of simultaneity, substitutability, and complementing of degrees of truth) and their analytic forms. GL is developed as a seamless soft computing generalization of classical Boolean logic. The proposed generalizations include: (1) the continuous parameterized transition from drastic conjunction to drastic disjunction, integrating the regions of hyperconjunction, hard and soft partial conjunction, logic neutrality, soft and hard partial disjunction, and hyperdisjunction and (2) semantic generalization where we assign a degree of importance to each degree of truth, in a way that is consistent with observable properties of human intuitive reasoning. These generalizations reflect the fact that degrees of truth are not anonymous real numbers, but values that have semantic identity derived from goals and requirements of decision-maker. The basic goal of GL is to provide humancentric mathematical models of logic aggregators that can be used in the decision process of comparison and selection of alternatives based on the evaluation of each alternative. This paper is structured to present the characteristic properties of entire GL in a way that is both concise and complete.     

Database technology for decision support systems

Decision support systems form the core of business IT infrastructures because they let companies translate business information into tangible and lucrative results. Collecting, maintaining, and analyzing large amounts of data, however, involves expensive technical challenges that require organizational commitment. Many commercial tools are available for each of the three major data warehousing tasks: populating the data warehouse from independent operational databases, storing and managing the data, and analyzing the data to make intelligent business decisions. Data cleaning relates to heterogeneous data integration, a problem studied for many years. More work must be done to develop domain-independent tools that solve the data cleaning problems associated with data warehouse development. Most data mining research has focused on developing algorithms for building more accurate models or building models faster. However, data preparation and mining model deployment present several engaging problems that relate specifically to achieving better synergy between database systems and data mining technology     

Value-driven expert systems for decision support

Values are an inherent part of all decision processes. Hence, values are at least implicity included in all expert systems intended for decision support. This paper outlines the concepts and methodology, which are based on the principles and procedures of decision analysis, to address explicity the values in an expert system logically and consistently. Implementation of the concepts and methodology involves the elicitation of values using the same general approach as that used by knowledge engineers to explicate expert knowledge.     

The use of vicinal-risk minimization for training decision trees

We propose the use of Vapnik's vicinal risk minimization (VRM) for training decision trees to approximately maximize decision margins. We implement VRM by propagating uncertainties in the input attributes into the labeling decisions. In this way, we perform a global regularization over the decision tree structure. During a training phase, a decision tree is constructed to minimize the total probability of misclassifying the labeled training examples, a process which approximately maximizes the margins of the resulting classifier. We perform the necessary minimization using an appropriate meta-heuristic (genetic programming) and present results over a range of synthetic and benchmark real datasets. We demonstrate the statistical superiority of VRM training over conventional empirical risk minimization (ERM) and the well-known C4.5 algorithm, for a range of synthetic and real datasets. We also conclude that there is no statistical difference between trees trained by ERM and using C4.5. Training with VRM is shown to be more stable and repeatable than by ERM.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

OR and AI approaches to decision support systems

First the concept of a decision support system (dss) is described. Then the first approach, in which the development of a network of models forms the kernel, is given. An example illustrates this approach. Then, architectures of decision support systems according to the first approach are given. Afterwards a second approach is considered, in which an (abstract) dss machine is described that can be tuned to specific decision situations. Finally the applicability of this approach is illustrated with an example.     

An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems

The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.