Decision Support System In Order Research Articles

Mood recognition in bipolar patients through the PSYCHE platform: Preliminary evaluations and perspectives

Bipolar disorders are characterized by a series of both depressive and manic or hypomanic episodes. Although common and expensive to treat, the clinical assessment of bipolar disorder is still ill-defined. In the current literature several correlations between mood disorders and dysfunctions involving the autonomic nervous system (ANS) can be found. The objective of this work is to develop a novel mood recognition system based on a pervasive, wearable and personalized monitoring system using ANS-related biosignals. The monitoring platform used in this study is the core sensing system of the personalized monitoring systems for care in mental health (PSYCHE) European project. It is comprised of a comfortable sensorized t-shirt that can acquire the inter-beat interval time series, the heart rate, and the respiratory dynamics for long-term monitoring during the day and overnight. In this study, three bipolar patients were followed for a period of 90 days during which up to six monitoring sessions and psychophysical evaluations were performed for each patient. Specific signal processing techniques and artificial intelligence algorithms were applied to analyze more than 120 h of data. Experimental results are expressed in terms of confusion matrices and an exhaustive descriptive statistics of the most relevant features is reported as well. A classification accuracy of about 97% is achieved for the intra-subject analysis. Such an accuracy was found in distinguishing relatively good affective balance state (euthymia) from severe clinical states (severe depression and mixed state) and is lower in distinguishing euthymia from the milder states (accuracy up to 88%). The PSYCHE platform could provide a viable decision support system in order to improve mood assessment in patient care. Evidences about the correlation between mood disorders and ANS dysfunctions were found and the obtained results are promising for an effective biosignal-based mood recognition.

“You can't make this a science!” — Analyzing decision support systems in political contexts

This paper reports on problems and conflicts encountered when using decision support systems (DSS) in political contexts. Based on a literature study and two case studies we describe problems encountered in relation not only to the DSS itself, but also to the political decision process. The case studies have been carried out in two cities in Sweden that at different times but in similar situations have used DSS in order to reach a decision in complicated and contested matters. In both cases we have previously found that the method and IT tool used for decision analysis were appreciated by most participants, but the inherent rationality of the DSS was in conflict with how participants usually make decisions as well as with the political process. The assumption was that a strict and open method would make grounds for clear decisions, but the results of the decision process were none of the cases implemented. In one case the result of the decision analysis was that no clear decision was made. In the other case the lowest ranked alternative was implemented. Furthermore, in neither city the method was ever used again. We therefore ask: What are the challenges and limitations to using DSS in political contexts? Our study shows that challenges relate to selecting and using criteria; eliciting weights for criteria (high level of subjectivity); understanding all the amount of facts available in the system; time constraints; and lack of impact on the final decision. This study contributes to both research and practice by increasing the understanding of what challenges are experienced in DSS use, since the findings can be used as a framework of challenges that should be addressed, in design of systems as well as method for use. The study also contributes to understanding the role of politicians in decision-making and the consequences for the use of DSS. Further, the literature study showed that there are overall very few studies on the actual use of DSS in a political context, and we therefore conclude by encouraging more studies reporting actual use.

Operation of waste-to-energy-plant optimisations by using design exploration

Nowadays, a part of Municipal Solid Waste (MSW) of higher calorific value is usually used as a fuel in Waste-To-Energy-Plant (WTEP) which uses bed combustion process in a grate furnace. The properties and structure of MSW are very variable, so the complete combustion and emissions on the reasonable level are often very hard to achieve. The Computational Fluid Dynamics (CFD) with different CFD codes is often used to analyse the situation in the WTEP. In this paper, the CFD simulation within ANSYS 13.0 software package and in WORKBENCH 2.0 environment is used and supplementary optimisation’s methods are employed by using design explorer approach to achieve the target - optimised values of the input and output parameters which have been defined in order to indicate the complete combustion in the furnace. Furthermore, input and output parameters interaction can be found and presented and measured. On this basis, the optimal operating conditions and optimal combustion chamber dimensions are established through decision support systems in order to assure the complete combustion process with minimal emissions on the environment. Moreover, these requirements should be taken into account to enhance the operation conditions in the existing furnace or in research, development or project phase. In this way, the costs and time in these activities can be reduced. This approach has benefits because it can be used in other furnaces which use other fuels such as biomass, waste-oils or coal.

Order acceptance/rejection policies in determining the sequence in mixed model assembly lines

This study presents a decision support system for order acceptance/rejection in a Make-to-Order production environment with the mixed model assembly line approach. Indeed, variety of orders is the parameter under investigation herein. When the company does not have sufficient capacity or the required resources to accept the entirety of all new arrival orders, the following steps are proposed: At first, the customers are prioritized based on their corresponding profit values (at present and in the future) whereas other orders are rejected by the top manager along with some undesirable ones. Then, prices and delivery dates of the non-rejected orders are determined via a mathematical programming model. Quality levels are also reported to the customers based upon which they are allowed to select their desired blend of main components. Afterwards, in the case of customer’s lack of satisfaction with the offered price and due date, further negotiation is proposed. Finally, if the negotiation leads to an agreement, the order is accepted and added to the production schedule of the shop floor. The schedule of accepted orders is performed based on minimizing the work overload. The present study presumes that the processing times of the tasks are affected by both learning effect and deterioration of the jobs, simultaneously. In the last section of the study afoot, numerical experiments as well as the corresponding methodology are presented in order to solve the models. Moreover, a simulated annealing algorithm is utilized to solve the problem in a large scale with little computational time. Numerical experiments confirm that the simulated annealing algorithm tends to yield rather fine results with little discrepancy compared to those of the exact algorithm in small and medium size.

An Analysis of Processes, Risks, and Best Practices for Use in Developing Systems Engineering Process Simulators

Systems engineering simulation models provide a valuable means to understand and learn important concepts related to systems engineering. Simulators can be used for education and as decision support systems in order to evaluate the dynamic consequences of various courses of action. This paper examines existing research mined from literature related to systems and software engineering processes, risks, and best practices. It seeks to guide the definition of priorities for systems engineering process simulator development. The paper identifies an initial set of research opportunities for the development of systems engineering process simulators.

Technical solutions for analysis of grape juice, must, and wine: the role of infrared spectroscopy and chemometrics

Information about constituents of grape juice, must, and wine can be used for management and decision support systems in order to improve, monitor, and adapt grape and wine production to new challenges. Numerous sensors that gather this information are either currently available or in development. Nevertheless there is still a need to adapt these sensors to special requirements, for example robustness, calibration and maintenance, operating costs, duration, sensitivity, and specificity to a particular application. The sensors commonly used by the wine industry are those that are based on mid-infrared (MIR), near-infrared (NIR), visible (VIS) and ultraviolet (UV) spectroscopy. This article reviews some recent technical solutions for analysis of juice, must and wine based on the combination of infrared spectroscopy and chemometrics.

Decisions on multiple sclerosis immunotherapy: New treatment complexities urge patient engagement

For patients with multiple sclerosis (MS) involvement in treatment decisions becomes ever more imperative. Recently new therapeutic options have become available for the treatment of MS and more will be licensed in the near future. Although more efficacious and easier to administer, the new drugs pose increased risks of severe side effects. Also, new diagnostic criteria lead to more and earlier MS diagnoses. Facing increasingly complex decisions, patients need up-to-date evidence-based information and decision support systems in order to make informed decision together with physicians based on their autonomy preferences. This article summarizes recently terminated and ongoing trials on MS patient education and decision aids conducted by the authors' study groups. Programs on relapse management, immunotherapy, and for patients with suspected and early MS have been developed and evaluated in randomized controlled clinical trials. It could be shown that the programs successfully increase knowledge and allow patients to make informed decisions based on their preferences. For the near future, we aim to develop a modular program for all relevant decisions in MS to increase patients' self-management and empower patients to develop their individual approach with the disease. Faced by a disease with many uncertainties, this should enhance patients' sense of control. Still, it remains a challenge to adequately assess decision quality. Therefore, a study in six European and one Australian centers will start soon aiming to establish adequate tools to assess decision-making quality.

A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems

In this paper, we present a novel decision support system for order acceptance/rejection in a hybrid Make-to-Stock/Make-to-Order production environment. The proposed decision support system is comprised of five steps. At the first step, the customers are prioritized based on a fuzzy TOPSIS method. Rough-cut capacity and rough-cut inventory are calculated in the second step and in case of unavailability in capacity and materials, some undesirable orders are rejected. Also, proper decisions are made about non-rejected orders. At the next step, prices and delivery dates of the non-rejected orders are determined by running a mixed-integer mathematical programming model. At the fourth step, a set of guidelines are proposed to help the organization negotiate over price and due date with the customers. In the next step, if the customer accepts the offered price and delivery date, the order is accepted and later considered in the production schedule of the shop floor, otherwise the order is rejected. Finally, numerical experiments are conducted to show the tractability of the applied mathematical programming model.

Using intelligent agents for Transportation Regulation Support System design

This paper presents an agent-based approach used to design a Transportation Regulation Support System (TRSS), that reports the network activity in real-time and thus assists the bus network regulators. The objective is to combine the functionalities of the existing information system with the functionalities of a decision support system in order to propose a generic model of a traffic regulation support system. Unlike the other approaches that only deal with a specific task, the original feature of our generic model is that it proposes a global approach to the regulation function under normal conditions (network monitoring, dynamic timetable management) and under disrupted conditions (disturbance assessment and action planning of feasible solutions). Following the introduction, the second section presents the notions of the domain and highlights the main regulation problems. The third section details and motivates our choice of the components of the generic model. Based on our generic model, in the fourth section, we present a TRSS prototype called SATIR (Système Automatique de Traitement des Incidents en Réseau – Automatic System for Network Incident Processing) that we have developed. SATIR has been tested on the Brussels transportation network (STIB). The results are presented in the fifth section. Lastly, we show how using the multi-agent paradigm opens perspectives regarding the development of new functionalities to improve the management of a bus network.

Analysing agricultural users’ patterns of behaviour: The case of OPTIRas™, a decision support system for starch crop selection

Redesigning IT systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of this paper is to provide insights into patterns of behaviour of agricultural users, during the usage of a decision support system called OPTIRas™. This system aids agricultural users in their cultivar selection activities. We analyse logs resulting from OPTIRas™, and we get insights into user’s navigational patterns. We claim that the results of our analysis can be used to support the redesign of decision support systems in order to address specific agricultural users’ characteristics.

An analytical approach to quantitative effect estimation of operation advisory system based on human cognitive process using the Bayesian belief network

The design of instrumentation and control (I&C) systems for nuclear power plants (NPPs) is rapidly moving towards fully digital I&C systems and is trending towards the introduction of modern computer techniques into the design of advanced main control rooms (MCRs) of NPPs. In the design of advanced MCRs, human–machine interfaces have improved and various types of decision support systems have been developed. It is important to design highly reliable decision support systems in order to adapt them in actual NPPs. In addition, to evaluate decision support systems in order to validate their efficiency is as important as to design highly reliable decision support systems. In this paper, an operation advisory system based on the human cognitive process is evaluated in order to estimate its effect. The Bayesian belief network model is used in the evaluation of the target system, and a model is constructed based on human reliability analysis event trees. In the evaluation results, a target system based on the operator's cognitive process showed better performance compared to independent decision support systems.

Visualizing Operators' Cognitive Strategies in Multivariate Optimization

The proposed “Tracking Resource Allocation Cognitive Strategies” tool (TRACS) allows for post-hoc visualization of the cognitive steps exhibited by a human operator while interacting with a multivariate resource allocation decision-support interface. This tool was applied to both mission planning for multi-criteria resource allocation for military strikes, and also multi-variable geospatial path planning problems for astronaut moon traversals. Both domains involve a human operator interacting with an automated decision-support system in order to find a solution to a complex planning problem involving multivariate and constrained optimization for a cost function. With the help of TRACS, clear patterns of behavior were identified that could be correlated to performance in both applications.

Dialectic decision support systems: System design and empirical evaluation

Many complex and unstructured decisions are hindered by a lack of clear understanding of various underlying assumptions and perspectives involved in the decision process. At present, the traditional decision support systems (DSS) pay little attention to the elicitation of underlying assumptions and perspectives in dealing with complex issues. We argue that the Socratic dialectic inquiry is an effective method for dealing with unstructured problems that are complex and require the involvement of different perspectives in DSS. In this paper, we propose a design for Dialectic Decision Support Systems (DDSS), in which dialectical processes are integrated with traditional DSS in order to provide support for individual decision makers. We then formulate a conceptual model for identifying factors that contribute to the efficacy of DDSS in comparison to traditional DSS. The empirical test of the model supports the superior efficacy of the DDSS and identifies factors that contribute to it. The contributions of this research are in generating support for stimulating critical thinking, dealing with complex decision issues and identifying creative solutions.

Consistency of the decision-maker in pair-wise comparisons

Most authors assume that the natural behaviour of the decision-maker is being inconsistent. This paper investigates the main sources of inconsistency and analyses methods for reducing or eliminating inconsistency. Decision support systems can contain interactive modules for that purpose. In a system with consistency control, there are three stages. First, consistency should be checked: a consistency measure is needed. Secondly, approval or rejection has to be decided: a threshold value of inconsistency measure is needed. Finally, if inconsistency is 'high', corrections have to be made: an inconsistency reducing method is needed. This paper reviews the difficulties in all stages. An entirely different approach is to elaborate a decision support system in order to force the decision-maker to give consistent values in each step of answering pair-wise comparison questions. An interactive questioning procedure resulting in consistent (sub) matrices has been demonstrated.

상수도관망의 이상징후 판정을 위한 위험요소 평가 - PROMETHEE와 ANP 기법 중심으로

본 연구에서는 안전하고 원활한 용수공급을 목적으로 하는 상수도관망시스템의 오염예방 및 위험관리를 위한 통합의사결정시스템의 기본구조를 제시하고 유럽과 미국에서 널리 사용되고 있는 다기준 의사결정기법인 PROMETHEE와 ANP를 적용해 상수도관망의 이상징후 판정을 위한 위험요소들의 우선순위를 평가하였다. 문제 구성을 위하여 pH 잔류염소농도, 유량, 수압, 전기전도도, 탁도, 블록누수량, 수온을 자료항목으로 선정하였고 관부식, 관파열, 관내수질오염을 평가기준으로 하여 PROMETHEE와 ANP의 결과를 비교 분석하였다. 본 연구에서 제시한 상수도관망의 위험요소 평가결과는 위기상황 대처방안시스템 구축시 사고대응 제어알고리즘 설계의 기초자료가 될 수 있을 것이다. In this study, we proposed a layout of the integrated decision support system in order to prevent the contamination and to manage risk in water supply networks for safe and smooth water supply. We evaluated the priority of risk factors to detect anomaly in water supply networks using PROMETHEE and ANP techniques, which are applied to various Multi-Criteria Decision Making area in Europe and America. To develop the model, we selected pH, residual chlorine concentration, discharge, hydraulic pressure, electrical conductivity, turbidity, block leakage and water temperature as the key data item. We also chose pipe corrosion, pipe burst and water pollution in pipe as the criteria and then we present the results of PROMETHEE and ANP analysis. The evaluation results of the priority of risk factors in water supply networks will provide basic data to establish a contingency plan for accidents so that we can establish the specific emergency response procedures.

Development of a Decision Support System to Assess Farm Animal Welfare

The progress of a project aimed at developing a decision support system (DSS) for overall welfare assessment is described. We chose the pregnant sow as a 'case' to develop the assessment method. First, we designed and tested a very simple prototype DSS in order to examine several basic ideas as to how welfare can be assessed on a scientific basis. In this prototype welfare assessment was based on the needs of animals. The second version of the DSS, which extended the approach taken in the first, included a wider range of housing systems and an improved knowledge base. It used a rigid formal procedure to construct the model, moving directly from scientific statements into if-then rules. However, this extended version resulted in counter-intuitive scores for the seven main housing systems for pregnant sows. In the third version of the DSS the complexity of the model was reduced, if-then rules being excluded. It was based on more general biological considerations (needs and sub-needs) and produced scores which accorded better with expert opinion. Weighting was managed in a simplified, but procedural way. For improved welfare assessment we suggest that the method of 'reflective equilibrium' should be used.

Incorporating Reasoning by Analogy as a DSS Tool

This paper reports a study that incorporated a reasoning by analogy (RBA) system into a business simulation game of the microcomputer industry. The RBA system was designed to support a manager’s intuitionand judgement by facilitating the use of analogies. Users search for comparable (i.e., analogous) firms througha simple pattern‐matching routine built into the system. We argue for including RBA tools within decision support systems in order to facilitate analogical reasoning by decision makers. The study results support the conclusion that implementation of RBA systems significantly improves decision making effectiveness. The design and implementation of an RBA system, including the environmental scanning required to create the databases needed to support the system,is described and implications for practitioners and designers are discussed.

Self-organizing executive information networks

Organizations acquire decision support systems in order to deliver the information needed for decision making. But at executive levels, rapid changes in organizational structure or executive decision making can quickly obsolete an existing information dissemination structure. Insights gained from prior research in executive activities, executive support systems and neural networks are used in this research to design an adaptive, self-organizing system for the dissemination of information to executives. These self-organizing executive information networks are able to learn from the changing needs of their executive constituency. A neural network based executive information system incorporates a system for responding to organizational changes in responsibilities and information flows as an integral part of the information dissemination system. Extensive research in neural network based computer systems and in studies of biological systems have shown the practicality of this approach.