A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

Implementation and evaluation of a decision support system for type II diabetes

A decision support system for the management of oral hypoglycaemic therapy in type II diabetes was evaluated. The ruleset contained therein forms the basis of a prototype computer programme, but in order to assess the robustness of the individual rules, it was decided it was necessary to use a paper-based form of the ruleset. A nurse with no previous experience of managing type II diabetes was trained to use the system and then undertook the exclusive management of half of all new type II diabetics, from a district population of 300 000, over a 16-month period. General practices within this area were divided into two groups, study and control, matching for size, geographical area and standards of existing diabetes care. Patients (n = 102) from the study group practices were then assigned to her care. Those patients (n = 116) in the control group of practices were treated according to their normal procedures. The decision support system for oral hypoglycaemic therapy was based on the following criteria: the current type of treatment (six levels); current glycaemic control (HbA₁ and FBS) — whether improving, steady or worsening; and weight — %IBW, whether rising, steady or falling. Each of these parameters was carefully defined on the basis of established practice and clinical experience. Patients after initial education were seen at their usual clinic by the nurse only, on a monthly basis, until satisfactory glycaemic control was established and thereafter reviewed 3 monthly. She was also responsible for ensuring the organisation of Diabetes Annual Review procedures. The medical records of the control group patients were examined at the end of the study and data on glycaemic control and Annual Reviews extracted. In the study group 98% patients achieved HbA₁ levels within the normal range and all patients had full annual reviews performed. The control practices achieved much poorer degrees of metabolic control (P < 0.01) and completed fewer annual reviews. The study group did not demonstrate a significantly increased frequency of clinical hypoglycaemia consequent upon better blood sugar control. No exceptions to the ruleset, as initially defined, were detected. In conclusion, this decision support system was successful at achieving standards of diabetes control and care equal to or better than conventional structures of diabetes care. Implementation of such a system, on a simple computer platform, could greatly assist and possibly improve diabetes management in general practice.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

Research on clinical decision support systems development for atrophic gastritis screening

The paper presents a pilot research on the application of clinical decision support systems in a atrophic gastritis screening task. Two different DSS learning strategies have been tested – a standalone classifier and classifier ensemble application. Such classification algorithms as C4.5, CART, JRip and Naive Bayes were used as base classifiers. The classifiers were evaluated on the respondent medical data from an inquiry form, containing 28 attributes and 840 records. The dataset was preprocessed using simple methods in initial data analysis as well as more complex data mining methods for feature selection. The obtained results are summarized and discussed in order to summarize an information on what learning strategies are more applicable to the present dataset and should be studied in more detail in primary research.     

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.     

A text-based decision support system for financial sequence prediction

Although most quantitative financial data are analyzed using traditional statistical, artificial intelligence or data mining techniques, the abundance of online electronic financial news articles has opened up new possibilities for intelligent systems that can extract and organize relevant knowledge automatically in a usable format. Most information extraction systems require a hand-built dictionary of templates and thus need continual modification to accommodate new patterns that are observed in the text. In this research, we propose a novel text-based decision support system (DSS) that (i) extracts event sequences from shallow text patterns, and (ii) predicts the likelihood of the occurrence of events using a classifier-based inference engine. The prediction relies on two major, but complementary, feature sets: adjacent events and a set of information-theoretic functions. In contrast to other approaches, the proposed text-based DSS gives explanatory hypotheses about its predictions from a coalition of intimations learned from the inference engine, while preserving robustness and without indulging in formalism. We investigate more than 2000 financial reports with 28,000 sentences. Experiments show that the prediction accuracy of our model outperforms similar statistical models by 7% for the seen data while significantly improving the prediction accuracy for the unseen data. Further comparisons substantiate the experimental findings.     

The functions of a decision support system

The growing literature on decision support systems outlines their principal characteristics and presents case studies of successful systems. This paper reviews the literature, describes six functions that a decision support system may perform for a manager or staff analyst, and introduces a new technique, functional mapping, for representing these systems.     

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.     

A decision support system for product design in concurrent engineering

Compared with the traditional sequential design method, concurrent engineering is a systematic approach to integrate concurrent design of products and their related processes. One of the key factors to successfully implement concurrent engineering is information technology. In order to design a product and its manufacturing process simultaneously, information on product features, manufacturing requirements, and customer demands must be processed while the design is concurrently going on. There is an increased understanding of the importance of the correct decisions being made at the conceptual design and development stages that involve many complex evaluation and decision-making tasks. In order to promote the efficiency in concurrent product development, appropriate evaluation and decision tools need to be provided. In this paper, the characteristics of fuzzy, multi-stage evaluation and decision making in concurrent product development process are analyzed and a decision support system for product design in concurrent engineering is presented. An example is given to illustrate the application of the system.     

Composite decision support by combining cost-benefit and multi-criteria decision analysis

This paper concerns composite decision support based on combining cost-benefit analysis (CBA) with multi-criteria decision analysis (MCDA) for the assessment of economic as well as strategic impacts within transport projects. Specifically a composite model for assessment (COSIMA) is presented as a decision support system (DSS). This COSIMA DSS ensures that the assessment is conducted in a systematic, transparent and explicit way. The modelling principles presented are illuminated with a case study concerning a complex decision problem. The outcome demonstrates the approach as a valuable DSS, and it is concluded that appraisals of large transport projects can be effectively supported using a combination of CBA and MCDA. Finally, perspectives of the future modelling work are given.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

Conflicting information integration for decision support

The integration of information from multiple sources is a topic of increasing importance to the design of advanced decision support systems for a variety of situation assessment tasks. The integration of information is especially difficult when the multiple sources provide conflicting information. In general, approaches to this problem have used either probability-based techniques or techniques based on one of the newer theories of uncertainty. This paper surveys these current approaches to the conflicting information problem and then describes a new approach based on information theory.     

Decision rules for ambulance scheduling decision support systems

This paper studies some decision rules for ambulance scheduling. The scheduling decision rules embedded in the decision support systems for emergency ambulance scheduling consider the criteria on the average response time and the percentage of the ambulance requests that are responded within 15 min, which is usually ignored in traditional scheduling policies. The challenge in designing the decision rules lies in the stochastic and dynamic nature of request arrivals, fulfillment processes, and complex traffic conditions as well as the time-dependent spatial patterns of some parameters complicate the decisions in the problem. To illustrate the proposed decision rules’ usage in practice, a simulator is developed for performing some numerical experiments to validate the effectiveness and the efficiency of the proposed decision rules.     

Human-centred decision support: The IDIOMS system

The requirement for anthropocentric, or human-centred decision support is outlined, and the IDIOMS management information tool, which implements several human-centred principles, is described. IDIOMS provides a flexible decision support environment in which applications can be modelled using both ‘objective’ database information, and user-centred ‘subjective’ and contextual information. The system has been tested on several real applications, demonstrating its power and flexibility. IDIOMS (Intelligent Decision-making In On-line Management Systems) is a collaboration between the National Transputer Support Centre, Sheffield University, Strand Software Technologies Ltd., Bristol Transputer Centre and a high street bank, partially funded by the DTI under the Information Engineering Advanced Technology Programme. The project has demonstrated several technical features which are not detailed in this paper, including a multi-user interface allowing dynamic shared access to data; machine learning strategies for three banking applications; a scalable, modular database engine; and realistic transactions being handled while on-line management information queries are made.     

Consumer decision support systems: Internet versus in-store application

This paper reports a study of consumer decision support in the context of Internet and in-store applications. A sample (n = 30) of experienced runners made running shoe selections in either ‘product only’, ‘decision support system only’, or ‘decision support system and product’ conditions. Participants’ decisions tended to be more uniform and of better quality when the DSS was available. Decision making was clearly influenced by DSS recommendations, but these were not always accepted. In this latter circumstance participants reported themselves to be relatively less happy with and less confident in their decision. Consistent with previous literature, abstract attributes were considered more frequently and given higher weightings when using the decision support system. However, predicted differences between conditions with respect to the types of attributes considered and the importance ascribed to different types of attributes were not found.     

A conceptual framework for society-oriented decision support

Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks.     

Adaptive decision support systems

The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations     

Development of a decision support system for neuro-electrostimulation: Diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for physician performing neuro-electrostimulation of neck neural formations for patients suffering from cardiovascular system disorders. The arterial hypertension was used as the clinical model of the disorders. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recorded while performing tilt-test functional load. Heart rate variability signal is an indirect mean of accessing autonomic nervous system functioning. Disturbances of the autonomic nervous system are essential in pathology of arterial hypertension. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate treatment efficiency. Predictions based on the selected heart rate variability features have a high level of agreement with the arterial pressure dynamics. The results indicate the potential of the proposed decision support system.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

A dynamic decision support system to predict the value of customer for new product development

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.