A decision support system for the design of air quality monitoring networks

This paper describes the prototype of a decision support system oriented to air pollution network designers. The purpose is to help the users in the choice of the number and localization of instruments in an assigned area. The criterion employed is to locate instruments so as to evidence concentrations exceeding law standards. AREM runs on IBM PC's or compatibles and provides the following outputs: the number and the localization of the monitoring stations, their efficiency coefficient, the global network efficiency, and finally the station coverages. The user can change the constraints of the problem and examine the resulting different solutions. One example is shown for the town of Piacenza and its sorrounding industrial area, located in Northern Italy.     

A decision support system for real-time monitoring and control of dynamical processes

This article presents the concept and development of a prototype diagnostic decision support system for real-time control and monitoring of dynamical processes. This decision support system, known as Diagnostic Evaluation and Corrective Action (DECA), employs qualitative reasoning, in conjunction with quantitative models, for monitoring and diagnosis of malfunctions in dynamical processes under routine operations and emergency situations. DECA is especially suited for application to time-constrained environments where an immediate action is needed to avoid catastrophic failure(s). DECA is written in common Lisp and has been implemented on a Symbolics 3670 machine; its efficacy has been verified using the data from the Three Mile Island No. 2 Nuclear Reactor Accident.     

Development of an environmental flows decision support system

The Murray–Darling Basin in Australia is severely environmentally degraded as a result of a range of anthropogenic changes, most notably the regulation and extraction of surface water resources for irrigated agriculture. Environmental problems include eutrophication of rivers and storages, elevated salinity levels, widespread blooms of toxic blue–green algae, decline of native fish and bird populations, and reduction of area of riverine wetlands. Both the community and the government are committed to improving the state of the environment in the Basin, both for it's intrinsic ecological values, and to ensure the sustainability of production in what is Australia's most economically important agricultural region. To facilitate the on-going trade-off process between competing users of this resource, a decision support system (DSS) is being developed which will enable explicit prediction of the likely response of key features of the riverine environment to proposed flow management scenarios. The DSS is being developed using the RAISON shell (Lam, D.C.L., Mayfield, C.I., Swayne, D.A., Hopkins, K., 1994. A prototype information system for watershed management and planning. Journal of Biological Systems 2 (4), 499–517), and will integrate a range of simple models of riverine ecology which are being developed. These models will include qualitative and quantitative models representing the response of different aspects of the instream and floodplain ecology dependent upon the river flow regime. The DSS will not include a detailed model of river hydrology or hydraulics, but rather, will use the output from the range of such models currently in use in the Basin as inputs to the ecological models. The DSS will also provide a range of tools to allow user-defined evaluation of scenario results, as well as explanations and supporting information to elucidate the ecological modelling.     

A decision support system for college selection

In this changing society the importance of higher education is increasing more than ever before. The selection of an appropriate university or college is of vital importance to the student for acquisition of proper educational experience. There are thousands of universities and colleges in the United States and Canada. The information available to students about each college is plentiful but is rather tedious to be obtained. The decision process of college selection is further complicated by many factors such as tuition, location, rank, size of the universities, and so forth. These factors play an important role in the final selection of a college. Therefore, a computer-based decision support system is developed in this paper to help users make better decisions in the selection of a college.

This college selection decision support system is designed to be menu-driven and highly user-friendly with a “Help” utility. Many “what-if” scenarios are also available in this system. It can be run on any IBM XT/AT or compatible machines with a DOS environment. It will allow users to make better decisions in their college selection process.     

A manufacturing decision support system for flamecutting

The design of a Manufacturing Decision Support System (MDSS) for the control of a flamecutting operation is discussed. The MDSS incorporates the overall economics of a continuing inventory. It also takes into account the use of left-over offcuts or partial plates, and the possibility of producing as a flow shop instead of on a job shop cut-to-order basis. Some research results are available that aid the construction of such a DSS; these are mostly in the areas of two-dimensional parts' layout and torch path sequencing. The proposed DSS constructs parts' nests, generates cutting sequences, directs the use of trim margins, and updates and outputs the economics of the whole cutting process. Its major strength is its potential use at the shop floor level, using relatively inexpensive computing power to control cutting torches that are usually driven by far more expensive systems that take much longer to determine layouts and cutting sequences.     

Design and implementation of an environmental decision support system

An environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. The system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed.     

Trends extraction and analysis for complex system monitoring and decision support

This paper presents an effective trend extraction procedure, based on a simple, yet powerful, representation. Its usefulness for complex system monitoring and decision support is illustrated by three examples. The method extracts semi-qualitative temporal episodes on-line, from any univariate time series. Three primitives are used to describe the episodes: {Increasing, Decreasing, Steady}. The method uses a segmentation algorithm, a classification of the segments into seven temporal shapes and a temporal aggregation of episodes. It acts on noisy data, without prefiltering. The first illustration is devoted to decision support in intensive care units. The signals contain information and noise at very different frequencies, and smoothing must not mask some interesting high-frequency data features. The second illustration is dedicated to a food industry process. On-line trends of key variables represent a very useful monitoring tool to control the end product quality despite high variations of raw materials at the input and a long delay. The last example concerns operator support and predictive maintenance. The results issued from a diagnostic module are complemented by the extrapolation of the key variable trends, which gives an idea of the time left to repair or reconfigure the process.     

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.     

A decision support system for dynamic truck dispatching

The feasibility of developing and implementing a real-time Decision Support System (DSS) on a microcomputer to assist line managers in making decisions, which have many subjective and intangible constraints, is demonstrated. A dynamic truck dispatching system was chosen as a prototype example.

The results of the research presented here are twofold. First, insight was gained in how tasks should be divided between the computer and the human operator and in the required colorgraphics interface between them in complex decision making processes. Second, a prototype DSS for a real-time vehicle dispatching system was developed on a microcomputer based upon these insights. The DSS was designed to accommodate all the characteristics of a large corporation's national dispatch system using contract carriers.     

A decision support system for evaluating intangible investments

In this research project we have connected the procedural and substantive decision support by means of modern information technology. We have showed how the semistructured strategic decisions concerning intangible investments can be supported effectively by relating appropriate analysis methods to the different phases of the investment process. Because for the intangible investments there is no well-defined solution procedure available, a variety of methods must be integrated to support the solution process.

As a demonstrative example we showed how the intangible investments in logistic systems can be managed. It was highlighted that the logistics systems are complex, cross-functional systems that affect all major functions or departments within the firm beginning from the raw material deliveries and ending with the deliveries of finished goods. Investments in the logistics systems have corporatewide, cross-functional effects, and the investment decisions are strategic decisions. Consequently, the investments in the logistics systems should be strategically justified, not only cost-justified. The cross-functional effects imply that the investment analysis concerning the whole logistics system must be fulfilled under multiple, diversified criteria. We can not, however, ignore the financial, cost and revenue oriented, traditional investment criteria. They just have to be used in a new, flexible way in relation to the goals and objectives of the firm.

Strategic planning is a managerial area where semistructured problems frequently exist. One of the key problems faced in strategic management is the issue of investment decisions. Investments in information systems, logistic systems, flexible production systems, corporate image, etc, are examples of complex investment problems that are difficult to quantify and analyze with traditional techniques. Modern computer technology can enrich the analysis and improve the quality of strategic decision making.     

A fuzzy decision support system for credit scoring

Neural Computing and Applications - Credit score is a creditworthiness index, which enables the lender (bank and credit card companies) to evaluate its own risk exposure toward a particular...     

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.     

A decision support system for computer-aided process planning

A decision support system framework is presented to aid the Decision Maker to resolve conflicting, non-commensurate objectives to set up parameters for machining operations, using a Multiple Criteria Decision Making approach.     

A decision support system for Evidence Based Medicine

We present a decision support system to let medical doctors analyze important clinical data, like patients medical history, diagnosis, or therapy, in order to detect common patterns of knowledge useful in the diagnosis process. The underlying approach mainly exploits case-based reasoning (CBR), which is useful to extract knowledge from previously experienced cases. In particular, we used sequence data mining to detect common patterns in patients histories and to highlight the effects of medical practices, based on evidence.We also exploited data warehousing techniques, such OLAP queries to let medical doctor analyze diagnosis along several measures, and recent visual data integration approaches and tools to effectively support the complex task of integrating and reconciling data from different medical data sources. In addition, due to massive presence of textual information within the clinical records of many hospitals, text mining techniques have been devised. In particular, we performed lexical analysis of free text in order to extract discriminatory terms and to derive encoded information. Finally, the system provides user friendly mechanisms to manage the protection of confidential medical data.System validation has been performed, mainly focusing on usability issues, by running experiments based on a large database from a primary public hospital.     

A Korean group decision support system

A Group Decision Support System (GDSS) can be used to improve communication in many languages. Heretofore, the vast majority of research with these systems has been conducted on American groups using English. Here, we demonstrate how groups of Korean students used a GDSS developed at the University of Mississippi to exchange comments in Korean and English anonymously and simultaneously. The study found no significant differences between the English and Korean systems in terms of self-assessed ratings of evaluation apprehension, production blocking, and process satisfaction. Participants rated both systems favourably, supporting our hypothesis that Korean groups can benefit from the use of a GDSS.     

A decision support system for diagnosis related groups coding

This study demonstrates that the utilization of a computer-aided decision support system (DSS) for diagnosis related groups (DRGs) coding called DRG-DSS, can both help coders to increase their coding quality and also reduce the time required to train them. However, an information system is of no real value if its intended users are not prepared to avail themselves of it. A quasi-experiment was conducted to evaluate the performance of the DRG-DSS in a hospital. Twelve DRGs coders were recruited and randomly assigned to experimental or control group to code 180 respiratory cases. Both object and subject-based indicators were used to measure the performance of DRG-DSS through a two-stage evaluation process. The results show that the DRG-DSS can effectively enhance the coding quality and all the users had a positive attitude with respect to the system’s perceived ease of use and usefulness. Following presentation of the research findings, their implications and limitations are discussed, along with suggestions for future work.     

A decision support system for the bimodal dial-a-ride problem

A bimodal dial-a-ride problem (BDARP) considered in this paper is a dial-a-ride problem that involves two transportation modes: paratransit vehicles and fixed route buses. Riders in such a system might be transferred between different transportation modes during the service process. The motivation of this research is that by efficiently coordinating paratransit vehicles with fixed route buses we can improve the accessibility and efficiency of a dial-a-ride system. In this paper, we design a decision support system (DSS) which automatically constructs efficient paratransit vehicle routes and schedules for the BDARP. This DSS has been tested using actual data from the Ann Arbor Transportation Authority (AATA) in Ann Arbor, MI. The results show that this DSS produces an average increase of 10% in the number of requests that can be accommodated and an average decrease of 10% in the number of paratransit vehicles required, as compared to the manual results where no fixed route buses are involved     

A decision support system for coordinated disaster relief distribution

This paper presents a simulation and optimization based decision-support system (DSS) to facilitate disaster relief coordination between private and relief organizations. The DSS simulates disasters and plans shipments of relief goods via transfer points to demand points in the affected area. This enables decision-makers to analyze the last-mile distribution of goods by scheduling and routing trucks, off-road as well as unmanned aerial vehicles. Roads which are closed or opened during a disaster are considered allowing a dynamic adaptation to real world conditions and a comprehensive analysis over a rolling time-horizon. A mixed-integer problem formulation, an agent-based simulation, a heuristic-based scheduling and routing procedure as well as a Tabu Search metaheuristic are applied to analyze the given decision problem. Coordination between private and relief organization shows to be especially beneficial if time-losses resulting from closed roads are high and substantial unexpected demand occurs. Furthermore, results highlight the importance of selecting suitable transfer points and the potential of simulation and optimization based DSSs to improve disaster relief distribution.     

A knowledge-based decision support system for shipboard damage control

The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.