Decision support system

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors' experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.

Decision-making is a cognitive process stemming from the analysis of available courses of action related to a given problem. Rationally, potential courses of action are derived from deliberate analysis of available data related to a problem and its potential outcomes. Decisions are generally characterized as normative, descriptive or naturalistic. Normative decisions are based on evidence, logic and analysis; descriptive decisions are based on intuition and experience. Naturalistic decisions are formed as a hybrid of normative and descriptive processes.

For decision-makers the revolution in information technology and the amount and scope of the data they provide has become overwhelming, leading to cognitive overload, confusion and an inability to function effectively. Decision Support Systems (DSS) facilitate data analysis and selection of a viable course of action rapidly and effectively. However, like the human decision- makers they serve, DSS capabilities may also suffer from cognitive overload, inaccuracy, and complexity, especially in time-sensitive environments.

This study explores DSS employment for time-sensitive decision processes using Digitally Aided Close Air Support (DACAS) as a model. The study explores decision theory, models, DSS development and time-sensitive environments. Using DACAS as a model the paper reveals the obstacles and opportunities associated with the employment of DSS for time- sensitive processes. Finally, the study makes recommendations for improved employment of DSS in time-sensitive environments.

Computerised clinical decision support systems (CDSS) are increasingly being used by nurses to support their clinical practice. One of the factors which may affect how nurses use technology such as CDSS may be their clinical experience. This paper uses data from a wider study examining how nurses use CDSS to examine the role of experience in nurses' use of CDSS. Data was drawn from two sources; a secondary analysis of interviews from a study examining nurses' use of CDSS in telephone triage and the analysis of observations and interviews of nurses using CDSS in two case sites. Two themes arose from the analysis of the data; the integration of knowledge from CDSS and how experience affects CDSS use. The implications of these results are discussed in relation to our knowledge of the characteristics of the development of expertise in nursing.

Guido Maria Bazzani, Vittorio Gallerani, Davide Viaggi, Meri Raggi, Fabio Bartolini* The sustainability of irrigated agriculture in Italy under water and agricultural policy scenarios 1. Introduction Italy is characterised by a variety of environmental conditions, with a prevailing Mediterranean ...

The minimisation of the water cost is examined in the framework of an integrated water resources planning and management model, implemented within the decision support system for the management of the Athens water supply system. The mathematical framework employs a simulation-optimisation scheme, where simulation is applied to faithfully represent the system operation, whereas optimisation is applied to derive the optimal management policy, which simultaneously minimises the risk and cost of decision-making. Real economic criteria in addition with virtual costs are appropriately assigned to preserve the physical constraints and water use priorities, ensuring also the lowest-cost transportation of water from the sources to the consumption. The proposed model is tested in the hydrosystem of Athens, in order to minimise the expected operational cost for several system configurations.

This Shrimp Partial Harvesting Model, often hereafter referred to as "Model," includes worksheets to simplify data entry and navigation. This manual describes the data entry procedures, equations unique to the Model, and a simplified financial analysis.

Information accessibility has changed the way that chemical process plants are being managed. The traditional pyramidal hierarchy that is usually the core of any management structure is being threatened by the potential of centralized decisionmaking covering more and more possibilities. But although present tools may have access to any significant piece of objective information, and can process it at growing speeds, they are still termed as ªdecision supportº systems. Sensible decision-making is not only the result of knowledge plus information; it is also important to consider the effects of the different factors that are usually embraced by the term ªhuman factorsº, probably because they are difficult to comprehend and include in a mathematical model. Current research in these areas, usually related to human and/or artificial intelligence, may provide significant advances in the near future.

This paper intends to present a contribution to the development of techniques of decision-making in international projects of exploration and production of petroleum. A case study of the Multi-Attribute Utility Theory (MAUT) was developed and applied to define priorities among ten offshore exploration investment opportunities around the world offered to a Brazilian petroleum multinational company, according to decision-makers' preferences. The MAUT model proposed shows for the first time a combination of operational, political and technological risks, resulting in a quite complete formulation to describe quantitatively the decision-making process. It is important to emphasize that this paper presents an unpublished model to evaluate political risks whose detailed analysis is currently of extreme importance for international investments in petroleum projects.

We study the paper of Xu [Z. Xu, On consistency of the weighted geometric mean complex judgement matrix in AHP, European Journal of Operational Research 126 (2000) 683–687] for the group consistency in analytic hierarchy process of multicriteria decision-making. The purpose of this note is threefold. First, we point out the questionable results in this paper. Second, for three by three comparison matrices, we provide a patchwork for his method. Third, we constructed a counter example to show that in general his method is wrong. Numerical examples are provided to illustrate our findings. If there are four or more alternatives, then we may advise researchers to ignore his results to avoid questionable estimation of group consistency.

In large canal irrigation project areas, integrated management of surface and groundwater resources can improve water use efficiencies and agricultural productivity and also control water logging. Such integrated management requires an estimation of spatial distribution of recharge and ground water flow in the underlying aquifer. Recharge occurs both as percolation losses from fields and seepage losses from the water distribution network. Percolation losses are influenced by weather, soil properties, land use, and canal water and groundwater use. Seepage losses depend on the conditions of flow in the water distribution system. In large irrigation project areas all the factors influencing the recharge of groundwater vary spatially. In this study, a geographical information systems (GIS) is used to map the spatial distribution of recharge which then serves as input to a regional groundwater flow model for simulating the behavior of the underlying aquifer. The basis is that the project...

Computational intelligent techniques applied to economics have played an important role in the last years. In this paper we propose a framework based on an intelligent conversational agent embedded with a decision support system, aimed at suggesting the best managing strategies for a game-based model of a virtual town. The agent tries to prospect the future evolutions of particular choices taken by the user. Interaction is conducted through a natural language interface built as an Alice-based conversational agent.

School timetabling is a classical combinatorial optimization problem, which consists in assigning lessons to time slots, satisfying a set of constraints of various kinds. Due mostly to the constraints this problem falls in the category of NP-Complete problems. In this paper we try to show an implementation of a decision support system that solves real timetabling problems from various schools in Portugal. This implementation is based on the Simulated Annealing meta-heuristic. The constraints we use were obtained after inquiries made to several schools in Portugal. We show the results on three schools from different levels of teaching.

The purpose of this paper is to describe potential improvements in patient safety resulting from design decisions in the development of a computerized decision support system (DSS) for managing opioid therapy for chronic noncancer pain. ATHENA-DSS is an automated decision support system developed in a collaboration between Stanford University and the U.S. Department of Veterans Affairs (VA) to increase guideline-adherent prescribing and to change physician behavior. Based on data in patients' computerized medical record and knowledge of the clinical domain encoded in a knowledge base, the system gives patient-specific recommendations to primary care providers at the point of care. ATHENA-Opioid Therapy is based on a previous system, ATHENA-Hypertension, and is designed to follow the VA/Department of Defense clinical practice guideline for the management of opioid therapy for chronic noncancer pain. We describe the rationale for development of decision support system elements and a graphical user interface to increase patient safety during primary care treatment for chronic pain. The ATHENA-Opioid Therapy system focuses on reducing patient risk in four main ways by: (1) identifying patients with comorbidities or concurrent prescriptions that raise risk for overdose and recommending more conservative dosing; (2) identifying patients with mental health problems that increase risk of medication abuse and recommending referral to psychiatric care and close monitoring; (3) assisting doctors with complex pharmacologic calculations to reduce the risk of mistakes when initiating, titrating, or switching medications; and (4) presenting relevant information to clinicians in an easy-to-use format. We describe a system evaluation plan that we believe is essential to ensure that deployment of ATHENA-Opioid Therapy leads to improvements in patient safety and increases in guideline-concordant prescribing, and we discuss the limitations of this system for patient safety efforts.

Extensive research has been devoted to resource constrained project scheduling problem (RCPSP). Resources are renewable and there is a unique way to perform the activities. This work develops a population based evolutionary algorithm namely differential evolution (DE) to schedule project activities to minimize makespan subject to precedence constraints and resources availability. The proposed DE uses a priority value based representation to encode a project schedule and a serial generation scheme to obtain the schedule. The DE algorithm is compared with some existing algorithms available in the literature on the basis of a computational experiment performed on Patterson's test bed. Obtained results show that the performance of the proposed DE is quite satisfactory.

A Decision Support System (DSS) to support sustainable development planning processes in the agricultural sector has been developed in the context of the research project entitled WATER MAP (Development and utilization of vulnerability maps for the monitoring and management of groundwater resources in the Archimed area), which is an Interreg -Archimed project. The model is based on vulnerability maps and facilitates and optimizes the decision-making process relating to the problems of land use, water management and environmental protection. The spatial integration of the vulnerability maps in the DSS enables regional authorities to design optimal spatial development policies. The DSS is based on a Multicriteria Mathematical Programming model and can achieve the optimum agricultural production plan in the area combining different criteria to a utility function under a set of constraints concerning different categories of land, labour, available capital, etc. The model is further used to simulate different scenarios and policies due to changes on different social, economic and environmental parameters. In this way we get alternative production plans and agricultural land uses as well as the economic, social and environmental impact of different policies.

Data warehousing and Online Analytical Processing (OLAP) technologies are now moving onto handling complex data that mostly originate from the web. However, integrating such data into a decision-support process requires their representation in a form processable by OLAP and/or data mining techniques.

Aquatic weeds in lakes can cause different problems both to lake ecology and food webs and interfere with human activities. This drove our interest to exploit recursive satellite imagery to retrieve optical parameters suitable to develop an early warning strategy by mapping the aquatic weeds. This paper aims at assessing the capability of satellite-based remotely sensed imagery to provide information suitable for monitoring and managing the Lake Victoria resources. The spectral data collected during a field campaign, carried out for that purpose, were used to map the floating aquatic vegetation. By analyzing the "in situ" measurements and time series of satellite data we retrieved the distribution of aquatic weeds from 2004 to 2007 and the seasonal aquatic vegetation growth.

Cyber-physical systems, and the intelligent decision support systems that they enable, carry the promise of greater efficacy in environmental management. The complexity of the physical infrastructure subject to decisions by environmental management systems is often an impediment to this efficacy. Other challenges include fundamental differences in the operation of cyber and physical components, and significant interdependency among the cyber components that facilitate decision support and the physical components they control. The agentbased computing paradigm is proposed for surmounting these challenges, due to the flexibility of software agents as autonomous and intelligent decision-making components. In this paper, we utilize an agent-based system to illustrate the application of game theory to decision support for environmental management, in the context of intelligent water distribution. Simulation results are presented to validate the model proposed, and to investigate the effect of various model parameters on water allocation.

Users at different levels of domain experience have very different needs. For example, a system designed to assist domain novices may frustrate experts and vice-versa. This is one of several challenges specific to building decision support systems for experience-centered domains. Examples of experience-centered domains include medical diagnosis [37], chess [15], [16], as well as most professional design or planning tasks [24]

Under circumstances where data quality may vary (due t o inaccuracies or lack of timeliness, for example), knowledge about the potential performance of alternate predictive models can help a decision maker t o design a business value-maximizing information system. This paper examines a realworld example from the field of finance t o illustrate a comparison of alternative modeling tools. T w o modeling alternatives are used in this example: regression analysis and neural network analysis. There are t w o main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy, but the opposite was true when w e considered the business value of the forecast. (2) Neural netbased forecasts tended t o be more robust than linear regression forecasts as data accuracy degraded. Managerial implications for financial risk management of MBS portfolios are drawn from the results.

The selection and scheduling of a portfolio of projects is a task frequently found among the strategical activities performed by management staff in several industries. When choosing a project to be selected and scheduled into a portfolio, managers have to deal with conflicting criteria, resource constraints, distinct scenarios, and changes during the planning and execution phases. In this work, we describe the functionalities of a decision support system (DSS) prototype designed to help managers make better decisions, when constructing a portfolio of projects. The DSS software was developed to solve portfolio selection problems originating at a large corporation in the power generation industry. It implements a heuristic algorithm, which finds solutions at least 10% better than a manually produced solution for a real-world input instance.

Implementing information management system is still a challenging task for any organization. A lot of surveys are conducted to find out the root causes of implementation problems and propose new methods that can improve and simplify this process. In this paper we suggest using an ontology-based decision support system to avoid some implementation challenges.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 7 ( 2 0 1 2 ) 4-15 a neuroscientist-centred tool where the protocols being followed in TMS studies are made explicit, leaving to the users flexibility in exploring and sharing the results, and providing assistance in managing the complexity of the final diagnosis. This type of tool can make the results of medical experiments more easily exploitable, thus accelerating scientific progress.

Trading decisions in financial markets can be supported by the use of online algorithms. We evaluate the empirical performance of a threat-based online algorithm and compare it to a reservation price algorithm, an average price algorithm and to buy-and-hold. The algorithms are analyzed from a worst case and an empirical case point of view. The effectiveness of the algorithms is analyzed with historical DAX-30 prices for the years 1998 to 2007. The performance of the threat-based algorithm found in the simulation runs dominates all other investigated algorithms. We also compare its performance to results from worst case analysis and conduct a t-test.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 133-143 Case based fuzzy cognitive maps Bayesian network Decision support system Knowledge representation Semantic web Practice guideline a b s t r a c t

Developing decision support systems for environmental applications is an intricate, challenging task. The increasing complexity of environmental decision problems, the growing number of subjects involved and keen competition between conflicting interests make decisions and decision support difficult. Decision support systems have been developed since the 1970s to help tackle semi-structured and unstructured decision problems. Despite their popularity, the success of DSS development is uncertain and many computerised decision-support tools have failed when dealing with complex and unstructured problems. This article describes the development methodology and progress of mDSS, a decision support system for water resource management that has been developed under the European research project MULINO. The mDSS tool is designed to integrate environmental (especially hydrological) models with multiple-criteria evaluation procedures. A number of prototypes have been developed and the final version is expected at the end of the 3-year project. The system's development is driven by the experience acquired in several case studies selected in five European countries. Although the main aim of the MULINO project and the DSS is to help with increasingly complex decisions of general water management, the concepts of sustainable river basin management introduced by the water framework directive are addressed as well.

Soil erosion research is a popular topic in soil science that appears in conjunction with several other topics like soil and/or water conservation, soil physics, - chemistry and - biology and others. A request for the term "soil erosion" on CAB abstract CD, dated from January 1992 to January 2001, resulted in 15.531 articles. According to the publication year on

In this paper we consider the use of rules-and knowledge-based systems for the modelling of a broad class of decision problems. NEGOPLAN is a rule-based decision support tool, originally designed for negotiation support. We have used it to model processes with two participants, where the structure of the problem at any moment can be derived from the previous structure and decisions. We present NEGOPLAN+, an extenston that allows us to model the decision processes of an autonomous agent in a randomly changing environment. Our case study is the explorator) mission of a robot collecting material on the surface of an unknown planet. We show how the static and dynamic aspects of the decision process can be modelled in NEGOPLAN +. and we discuss the wider implications of mechanisms available in our system.

Ontology-based approaches may be tried in e-health scenario to solve some of the interoperability problems that are non-trivial, for e.g. 'Anemometer' and 'Sphygmomanometer' are two different terms but represent the blood pressure monitor in Europe and India, respectively. The semantic interoperability issue arises when similar things are meant differently to the remotely sitting agents. Given such scenario, this paper aims to describe a framework that combines A) dialogue game with B) a decision support system (DSS) leading to resolution of such types of ontological mismatches. Under 'Dialogue game' argumentation mechanisms are used for resolving certain types of ontological mismatches. While, the DSS decides on the initiation, termination, structuring and sequencing the dialogues, as these may not be properly adjudged by the 'Dialogue game'. The framework is therefore context driven in nature, where concepts can be substituted with the similar kind of concepts (may not be accurate by searching and choosing equivalent concepts. Such framework therefore offers potential for dynamic modification of participating Ontologies and provides explanation in context of diet management in diabetes with time in a more matured way.

Technology will help to address the challenges for sustainable forestry in the 21st century. Some of the challenges will include the shift of production from native forest to plantations in areas of comparative advantage, more efficient processing delinking end-use products from raw wood characteristics, increased demand, better information technologies to support decision makers, and more options for conserving biodiversity. Definitions of sustainability will vary in time and space as society's expectations and aspirations change, so there can be no 'silver bullet' to ensure sustainability. However, progress may be facilitated with a systematic approach to forest management embracing the usual planning cycle: formulation of objectives, preparation of a strategy, planning, implementing, monitoring, and reappraisal. This requires a good understanding of each particular situation. Managers need good resource assessment and decision support systems; they must foster stak...

Wetlands perform functions that support the generation of ecologically, socially and economically important values. European legislation has increasingly recognised the importance of preserving wetland ecosystems. The Water Framework Directive (WFD) embodies many of the existing directives that have implications for wetlands. The EU funded EVALUWET project (European Valuation and Assessment tooL sUpporting Wetland Ecosystem legislaTion) aims to develop and implement an operational wetland evaluation decision support system to support European policy objectives. A multidisciplinary approach is adopted combining expertise from natural and social scientists.

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge.

Building community resilience requires balancing demand, time, and cost in making decisions regarding hazard exposure and risk management under dynamic conditions. This dilemma represents a long-standing problem for emergency managers. As the number, frequency, and severity of hazardous events increase, losses escalate and the ensuing increase in time and cost of recovery compels a re-examination of disaster management decision processes. Defining the development of disaster resilience as a "national imperative," the National Research Council (2012, p.