A financial resource allocation model for regional water systems

The public water supply sector is facing significant external pressures for change from decreasing water resource availability, stricter water quality regulations, decreasing federal subsidies, increasing public scrutiny, decreasing financial health, and increasing infrastructure replacement costs. These forces necessitate greater accountability by community water systems (CWS) to their stakeholders. This paper presents a method for comparative efficiency analysis to improve the accountability of CWS to their stakeholders while maintaining the level of service. The method is achieved through three objectives, namely: (1) to construct standard efficiency metric parameters based on the techniques of data envelopment analysis; (2) to incorporate these uniform efficiency metric parameters into a transparent decision support system (DSS) based on the standard linear programming resource allocation problem; and (3) to utilize the DSS to determine the efficient allocation of limited budgetary resources among CWS operating as a regional water system (RWS). The paper is a significant departure, in three ways, from the current planning and management approach, which treats CWS as independent entities. First, it provides an open and transparent method for planning and management of CWS; second, it provides a uniform and consistent method for evaluating relative efficiencies across the CWS. Third, the DSS facilitates comparative efficiency analysis across the RWS, and guides financial allocation decisions among CWS operating as a RWS.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

Towards best practice implementation and application of models for analysis of water resources management scenarios

Water resources management models are widely used to evaluate planning or operational scenarios to support water resource management decision-making. However, the approaches to modelling used in the past have led to problems, such as modellers having difficulty establishing the credibility of their model with stakeholders, and stakeholders having difficulty understanding and trusting model results. A best practice approach to the implementation and application of water resources management models based on a quality assurance procedure is an appropriate means of overcoming these difficulties, and there are a number of guidelines and papers available promoting this approach. However, guidance in these on the use of models to analyse water resource planning scenarios is limited or not provided. This paper therefore provides guidance on the implementation and application of water resources management models with an emphasis on scenario analysis. This guidance is principally intended for practising modellers, and also for peer reviewers and stakeholders such as managers, decision makers, and community-based groups. Adoption strategies and recommendations for future directions are also discussed.     

A decision support systems approach to adjust maintenance float system availability levels

In this paper, a decision support system (DSS) is designed to predict the system availability levels for equipment maintenance float problems. The DSS is developed by using the analytic hierarchy process (AHP) to prioritize the improvements made in the maintenance management practice through the adoption of total quality management (TQM). The resulting regression metamodel can be used to predict and explain system availability. Sensitivity analysis can be easily generated through the DSS. The application of this DSS helps to effectively control the cost of maintenance floats as a result of TQM implementation, generates quick solutions, and provides the decision maker with the flexibility of carrying out sensitivity analysis and planning for the future by looking at the long-term impacts of TQM on the maintenance float 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.     

Decision Support Systems for implementing the European Water Framework Directive: The MULINO approach

The EU Water Framework Directive, WFD (Dir. 2000/60/EC) introduces an innovative, integrated and holistic approach to the protection and management of water resources. New methodologies and tools are required to support implementation of the new policy. To fulfil these requirements, tools such as Decision Support Systems (DSSs) that integrate environmental, social and economic concerns and that facilitate the involvement of interested parties in the formulation of strategies may be useful. The MULINO project has developed a methodology and a DSS tool to tackle such problems. Focus is on connecting environmental tools and decision support methods by combining the DPSIR (Driving force, Pressure, State, Impact and Response) approach with multi-criteria analysis methods in a Decision Support System called mDSS. The proposed approach can be applied in decision processes in which a group of people (i.e. decision makers and stakeholders), share a common conceptual framework and procedure, to structure the problem, discuss the decision and communicate the proposed solution. In this paper, the MULINO approach is presented, focusing on its potential for the current implementation process of the WFD, according to the recently released guidance documents and the experience gained in several case studies carried out during the research project. The evaluation of the potential of the tool for applications in real-world management problems is carried out by taking into account the feedback from project partners and from end users, within and outside the research consortium.     

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.     

A decision support system for adaptive real-time management of seasonal wetlands in California

This paper describes the development of a comprehensive flow and salinity monitoring system and application of a decision support system (DSS) to improve management of seasonal wetlands in the San Joaquin Valley of California. The Environmental Protection Agency regulates salinity discharges from non-point sources to the San Joaquin River using a procedure known as the total maximum daily load (TMDL) to allocate the assimilative capacity of the river for salt among watershed sources. Management of wetland sources of salt load will require the development of monitoring systems, more integrative management strategies and coordination with other entities. To obtain local cooperation, the Grassland Water District (GWD), whose primary function is to supply surface water to private duck clubs and manage wetlands, needs to communicate to local landowners the likely impacts of salinity regulation on the long-term health and function of wildfowl habitat. The project described in this paper will also provide this information. The models that form the backbone of the DSS, develop salinity balances at both a regional and local scale. The regional scale concentrates on deliveries to and exports from the GWD while the local scale focuses on an individual wetland unit where more intensive monitoring is being conducted. The design of the DSS is constrained to meet the needs of busy wetland managers and is being designed from the bottom up utilizing tools and procedures familiar to these individuals.     

Systems modelling for effective mine water management

Concerns about the difficulties in securing water have led the Australian coal mining industry to seek innovative ways to improve its water management and to adopt novel strategies that will lead to less water being used and more water being reused. Simulation tools are essential to assess current water management performance and to predict the efficiency of potential strategies. As water systems on coal mines are complex and consist of various inter-connected elements, a systems approach was selected, which views mine site water management as a system that obtains water from various sources (surface, groundwater), provides sufficient water of suitable quality to the mining tasks (coal beneficiation, dust suppression, underground operations) and maintains environmental performance. In this paper, the model is described and its calibration is illustrated. The results of applying the model for the comparison of the water balances of 7 coal mines in the northern Bowen Basin (Queensland, Australia) are presented. The model is used to assess the impact of applying specific water management strategies. Results show that a simple systems model is an appropriate tool for assessing site performance, for providing guidance to improve performance through strategic planning, and for guiding adoption of site objectives.     

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.     

Inductive program synthesis for therapy plan generation

Planning is investigated in an area where classical STRIPS-like approaches usually fail. The application domain is therapy (i.e. repair) for complex dynamic processes. The peculiarities of this domain are discussed in some detail for convincingly developing the characteristics of the inductive planning approach presented. Plans are intended to be run for process therapy. Thus, plans are programs. Because of the unavoidable vagueness and uncertainty of information about complex dynamic processes in the case of disturbance, therapy plan generation turns out to be inductive program synthesis. There is developed a graph-theoretically based approach to inductive therapy plan generation. This approach is investigated from the inductive inference perspective. Particular emphasis is put on consistent and incremental learning of therapy plans. Basic application scenarios are developed and compared to each other. The inductive inference approach is invoked to develop and investigate a couple of planning algorithms. The core versions of these algorithms are successfully implemented in Lisp and Prolog. The work has been partially supported by the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Wiscon onDevelopment of Methods for Intelligent Monitoring and Control under contract no. 413-4001-01 IW 204 B. Additionally, the second author’s work in learning theory received some support from the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Gosler onAlgorithmic Learning for Knowledge-Based Systems under contract no. 413-4001-01 IW 101 A. Oksana Arnold: She graduated from Leipzig University of Technology in 1990 with a Master’s Thesis on a rule interpreter for default reasoning. She received her PhD. in Computer Science in 1996 on therapy control for complex dynamic processes within a knowledge-based process supervision and control system. Recently, She works at the University of Leipzig within a research project on information and communication technologies for virtual enterprises. Her main scientific interest is both in knowledge-based process supervision and control, where she did a pioneering work on therapy plan generation, and in flexible information systems for new generation business applications. Klaus P. Jantke: He graduated from Humboldt University Berlin with a Master’s Thesis in 1975. He received his Ph. D. in Computer Science in 1979 and his Habilitation at Humboldt in 1984. He worked as the Head of a Research Laboratory in Theoretical Computer Science and as a Vice-Director of the Computing Center at Humboldt University. Since 1987, Dr. Jantke is full professor at Leipzig University of Technology. His main research interest is in algorithmic learning theory. Besides this, he contributes to case-based reasoning, where his special interest is in learning issues and in structural similarity, and to knowledge-based process supervision and control, especially to planning. Dr. Jantke is member of the ACM, the EATCS, and the GI.     

A DSS generator for district planning

This paper describes the experience of the authors in designing a decision support system (DSS) for district planning. The iterative nature of the planning process, the need for flexible analysis of large volume of detailed data, the need for frequent interaction with the database, and the potential use of interactive graphics make the context ideal for design of a DSS. The evolution of a DSS Generator through several stages of software designs is highlighted. A proposal is then made for a DSS Generator which will add enough power to existing software that already provides the flexibility needed to cover many application domains in district planning.     

Application of a regionalized knowledge-based model for classifying the impacts of nitrogen,sulfur, and organic acids on lakewater chemistry

To maintain healthy ecosystems, it is increasingly imperative that federal land managers be prepared to monitor and assess levels of atmospheric pollutants and ecological effects in national parks, wildlife refuges, and wilderness areas. Atmospheric deposition of sulfur and/or nitrogen has the potential to damage sensitive terrestrial, and especially aquatic, ecosystems and can affect the survival of in-lake and in-stream biota. Federal land managers have a need to assess, at the individual park or wilderness area level, whether surface water resources are sensitive to air pollution degradation and the extent to which they have been impacted by atmospheric deposition of sulfur or nitrogen or influenced by other complicating factors. The latter can include geologic sources of sulfur, natural organic acidity, and the influence of disturbance and land use on water quality. This paper describes a knowledge-based decision support system (DSS) network for classifying lakewater resources in five acid-sensitive regions of the United States. The DSS allows federal land managers to conduct a preliminary assessment of the status of individual lakes prior to consulting an acid–base chemistry expert. The DSS accurately portrays the decision structure and assessment outcomes of domain experts while capturing interregional differences in acidification sensitivity and historic acid deposition loadings. It is internally consistent and robust with respect to missing water chemistry input data.     

Modeling emergency evacuation of individuals with disabilities (exitus): An agent-based public decision support system

In this paper, we present a public decision support system (DSS) distinguished from various DSSs in the private business sector in terms of its ownership, data scarcity, and beneficiaries. In particular, our system is intended to play a vital role in assessing and optimizing emergency response plans for rare but catastrophic events such as the September 11th attacks. While taking an agent-based microscopic simulation approach in a hierarchical framework, we used our model to estimate the effectiveness of alternative evacuation strategies to support emergency response planning as a part of business continuity planning for all private business organizations. The presented model is unique because it considers individuals with disabilities explicitly in terms of speed, ability to negotiate the environment, and normalcy bias depending on type of disability. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions which have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency.Through a series of simulation experiments, our system identified specific locations (e.g., the NW landing) on the 3rd floor of the test bed building as possible bottleneck spots under certain conditions (e.g., the sharp increase in individuals with disabilities among residents). This way, our system provides the architect with tools to test the structure’s design to determine how well it meets the identified requirements for emergency evacuation to accommodate this shifting demographic. In particular, our system strongly demonstrated the effectiveness of new emergency evacuation strategies for individuals with disabilities such as assisted evacuations which allows other healthy people to play more active roles compared to traditional strategies in which individuals with disabilities are helplessly waiting for assistants such as fire fighters at the designated area. Our system also revealed that people using wheelchairs and those with lower stamina were at the greatest risk. People with lower stamina such as the elderly, people with chronic health conditions, or those with temporary injuries are at a greater risk mainly because they are not easily identifiable. Ultimately, the proposed DSS system can be used to inform public policy professionals of more effective, evidence-based evacuation planning and environmental design methods based on a better understanding of the behavior of individuals with disabilities.     

An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy

Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product–services (P–S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P–S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P–S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the logical data model that conceptualizes the context of planning a P–S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its data. As the input data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. “Market_1 is more competitive than Market_2”), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P–S strategy planning expressed in the article as performance questions.     

Understanding and controlling software costs

A discussion is presented of the two primary ways of understanding software costs. The black-box or influence-function approach provides useful experimental and observational insights on the relative software productivity and quality leverage of various management, technical, environmental, and personnel options. The glass-box or cost distribution approach helps identify strategies for integrated software productivity and quality improvement programs using such structures as the value chain and the software productivity opportunity tree. The individual strategies for improving software productivity are identified. Issues related to software costs and controlling them are examined and discussed. It is pointed out that a good framework of techniques exists for controlling software budgets, schedules, and work completed, but that a great deal of further progress is needed to provide an overall set of planning and control techniques covering software product qualities and end-user system objectives     

Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks

Object-Oriented Bayesian Networks (OOBNs) have been used increasingly over the past few decades in fields as diverse as medicine, transport and aeronautics. In this paper, OOBNs are applied to the domain of integrated water management and used as a Decision Support System (DSS). This pioneering study, set in the Altiplano region of Murcia in Southern Spain, describes a method for the integrated analysis of a complex water system supplied by groundwater from four aquifers. This method is based on the development of a multivariable integrated technique based on Bayes' theorem. After identifying all relevant factors related to water management in the area these were then translated to variables within a Bayesian Network (BN) and the relationships between them investigated. Each network represented one of the four aquifer units. These individual BNs were then linked to form an OOBN which was used to represent the complex real-world situation. In this way a DSS to simulate the entire water system was constructed using a group of conventional Bns, linked to produce an OOBN. The main stakeholders of the region contributed to network design and construction throughout the entire process. The paper shows how this type of DSS can be used to evaluate the impacts of a range of management strategies that are available to local planners.     

Scenario analysis and management options for sustainable river basin management: Application of the Elbe DSS

We developed a decision support system (DSS) for sustainable river basin management in the German Elbe catchment (～100,000 km²), called Elbe-DSS. The system integrates georeferenced simulation models and related data sets with a user friendly interface and includes a library function. Design and content of the DSS have been developed in close cooperation with end users and stakeholders. The user can evaluate effectiveness of management actions like reforestation, improvement of treatment plant technology or the application of buffer strips under the influence of external constraints on climate, demographic and agro-economic changes to meet water management objectives such as water quality standards and discharge control. The paper (i) describes the conceptual design of the Elbe-DSS, (ii) demonstrates the applicability of the integrated catchment model by running three different management options for phosphate discharge reduction (reforestation, erosion control and ecological-farming) under the assumption of regional climate change based on IPCC scenarios, (iii) evaluates the effectiveness of the management options, and (iv) provides some lessons for the DSS-development in similar settings. The georeferenced approach allows the identification of local inputs in sub-catchments and their impact on the overall water quality, which helps the user to prioritize his management actions in terms of spatial distribution and effectiveness.     

A simulation interface designed for improved user interaction and learning in water quality modelling software

Traditional simulation software that supports management decisions is configured and run by experienced scientists. However, it is often criticised for its lack of interactivity, not only in the application of decisions but also in the display of results. This paper presents the simulation interface of software with management strategy evaluation capabilities and its capacity to enable resource managers to learn about water quality management as evaluated in a workshop setting. The software ‘MSE Tool’ is not intended to produce definitive real-world advice but provides a test-bed for managers to interactively design strategies and explore the complexities inherent to water quality management using a simple, yet effective, user interface. MSE Tool has been used in a pilot application that simulated the effects of management strategies applied in catchments and their effects on riverine, estuarine and marine water quality in South East Queensland, Australia. The approach and the software are suitable for reuse in other management strategy evaluation projects.