Effective Decision Support Systems Research Articles

Carbon accounting framework for decarbonisation of European city neighbourhoods

Strategies for climate change mitigation in European cities have become more urgent and require actions to proactively involve administrators, citizens and other stakeholders and let them cooperate to accomplish widely approved plans for decarbonisation. Nevertheless, considering the short term of political mandates and the instability of social-economic-legal variables in our changing world, urban planning practices will require more effective and rapid decision support systems to easily access and process information. The paper presents an optimised carbon accounting methodology to assess greenhouse gas (GHG) emissions in specific urban environments and inform urban policies and design. In particular, this procedure, substantially inspired by the IPCC standard methodology for GHG emissions inventory of Nations, constitutes the framework of a “mediate model” with a dual role: to both assess the Carbon Footprint of urban neighbourhoods and to estimate the effects, in terms of Carbon Footprint mitigation, of action plans addressed to carbon neutrality. For demonstration, the carbon accounting framework has been performed based on average European values. The procedure started by profiling the typical household as functional unit, whose carbon footprint has been estimated 6.93 t CO2-eq/yr, referring to energy use for housing and mobility, domestic waste treatment and water use. The impact of the average European neighbourhood has been obtained by scaling up to 10,000 households (23,000 inhabitants) as benchmark for future applications. An additional outcome concerns the innovative spatial visualisation of results in terms of equivalent forestland (e.g. the emission of one average European household corresponds to the quantity of CO2 yearly absorbed by 0.51 ha of forest), that allows for understanding intensity and size of impacts in order to consistently support awareness raising initiatives targeting citizens and stakeholders and communication-dissemination activities.

Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings

Precision agriculture (PA) has become increasingly important to farmers particularly in resource-poor and risk-prone settings in the developing world. However, due to cost and technical constraints, deploying PA infrastructure as decision support systems (DSSs) in smallholder farming settings is often hindered. This paper draws on freely available satellite data (Sentinel-2A) and software (SNAP Toolbox), within the framework of open source remote sensing (OSRS) to demonstrate the potential of monitoring crop health and development towards building an effective DSS to inform farm management and resource allocation decision making using the Tono Irrigation Scheme—a resource-poor rural irrigation system in Ghana, as a case study. We find that vegetation index algorithms in SNAP Toolbox can accurately identify biophysical and growth conditions of crops including chlorophyll content, nitrogen status, pest and disease infestation, and water requirements. Despite the potential inherent in this novel cost-effective OSRS-based monitoring system, basic training of scheme managers and extension officers is required to enable them interpret output from OSRS analysis. Given the potential to reduce costs, improve allocation of scarce resources and increase yields, it is worth implementing OSRS as a DSS for smallholder farmers in other resource-poor settings.

The impact of alerting designs on air traffic controller’s eye movement patterns and situation awareness

This research investigated controller’ situation awareness by comparing COOPANS’s acoustic alerts with newly designed semantic alerts. The results demonstrate that ATCOs’ visual scan patterns had significant differences between acoustic and semantic designs. ATCOs established different eye movement patterns on fixations number, fixation duration and saccade velocity. Effective decision support systems require human-centered design with effective stimuli to direct ATCO’s attention to critical events. It is necessary to provide ATCOs with specific alerting information to reflect the nature of the critical situation in order to minimise the side effects of startle and inattentional deafness. Consequently, the design of a semantic alert can significantly reduce ATCOs’ response time, therefore providing valuable extra time in a time-limited situation to formulate and execute resolution strategies in critical air safety events. The findings of this research indicate that the context-specified design of semantic alerts could improve ATCO’s situational awareness and significantly reduce response time in the event of Short Term Conflict Alert (STCA) activation which alerts to two aircraft having less than the required lateral or vertical separation.Practitioner Summary: Eye movements are closely linked with visual attention and can be analysed to explore shifting attention whilst performing monitoring tasks. This research has found that context-specific designed semantic alerts facilitated improved ATCO cognitive processing by integrating visual and auditory resources. Semantic designs have been demonstrated to be superior to acoustic design by directing the operator’s attention more quickly to critical situations.Abbreviations: APW: area proximity warning; ASRS: aviation safety reporting system; ATC: air traffic control; ATCO: air traffic controller; ATM: air traffic management; COOPANS: cooperation between air navigation service providers; HCI: human-computer interaction; IAA: irish aviation authority; MSAW: minimum safe altitude warning; MTCD: medium-term conflict detection; SA: situation awareness; STCA: short term conflict alert; TP: trajectory prediction

Automatic Target Recognition Within Anomalous Regions of Interest in Hyperspectral Images

This paper proposes an automatic target recognition (ATR) methodology for airborne hyperspectral imagery in which regions of interest (ROIs) containing anomalous objects are inspected for recognition of specific targets. ROI-by-ROI processing is carried out in a fully automated fashion and does not need operator intervention, thus being suitable for in-flight applications. The ROI-based ATR methodology is developed within a multiple hypotheses testing framework, and its key strengths are in the use of a bank of flexible and robust nonparametric detectors combined with an automated method for null hypothesis discrimination and an effective decision support system. Experimental results over multiple real hyperspectral images show the effectiveness of the proposed methodology for automatic recognition of several different targets embedded in various kinds of background.

Development of decision support system for employee selection using Adaptive Neuro Fuzzy Inference System

The number of children day care is increasing from year to year. Children day care is categorized as service industry that help parents in caring and educate children. This type of service industry plays a substitute for the family at certain hours, usually during work hours. The common problems in this industry is related to the employee performance. Most of employees have a less understanding about the whole job. Some employees only perform a routine task, i.e. feeding, cleaning and putting the child to sleep. The role in educating children is not performed as well as possible. Therefore, the employee selection is an important process to solve a children day care problem. An effective decision support system is required to optimize the employee selection process. Adaptive neuro fuzzy inference system (ANFIS) is used to develop the decision support system for employee selection process. The data used to build the system is the historical data of employee selection process in children day care. The data shows the characteristic of job applicant that qualified and not qualified. From that data, the system can perform a learning process and give the right decision. The system is able to provide the right decision with an error of 0,00016249. It means that the decision support system that developed using ANFIS can give the right recommendation for employee selection process.

Evaluating Impact of an Emerging Big Health Data Platform: A Logic Model and Q-Methodology Approach

Despite advances in technology and medical science, modern health-based projects are open to systemic failure due to many factors. These include I.T. developer’s lack of awareness with regard to end-user needs, poor communication amongst all parties concerned and inappropriate or inadequate tests of the emerging system. Other issues may be external (e.g. political and legal) such as sharing of patient data and issues surrounding consent. The goal of this paper is to take a major health-based European model in current development and explore how it addresses the needs of four institutions in four different countries, and how it will meet their respective needs. The evaluation was designed within a Logic Model, and uses the Framework approach, and Q-Methodology to assess both impact and evaluation. Data will be collected through longitudinal semi-structured interviews and Q-scoring with principal stakeholders and developers at each stage of the project. This approach, recurring interviews with the same key players in the project, will help ensure that there is mutual understanding between I.T. developers and end-users of the system. The final system is meant to provide effective health-based decision support systems for policy makers.

Enhancing Clinical Decision Support Systems with Public Knowledge Bases

AbstractWith vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy medical records of the patient. In this paper, we propose automatic diagnosis prediction methods for enhancing the retrieval in a clinical decision support system, where the prediction is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods use Markov Random Field (MRF) model to identify diagnosis candidates in the knowledge bases, and their performance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015, and 2016. The results show that our methods can automatically predict diagnosis with about 75% accuracy, and such predictions can significantly improve the related biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-the-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors.

Making sense of cloud-sensor data streams via Fuzzy Cognitive Maps and Temporal Fuzzy Concept Analysis

Understanding situations occurring within the physical world by analyzing streams of sensor data is a complex task for both human and software agents. In the area of situation awareness, the observer is typically overwhelmed by information overload and by intrinsic difficulties of making sense of spatially distributed and temporal-ordered sensor observations. Thus, it is desirable to design effective decision-support systems and develop efficient methods to handle sensor data streams. The proposed work is for the comprehension of the situations evolving along the timeline and the projection of recognized situations in the near future. The system analyzes semantic sensor streams, it extracts temporal pattern describing events flow and provides useful insights with respect to the operators’ goals. We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps. The cloud-based architecture integrates a distributed algorithm to perform Fuzzy Formal Concept Analysis enabling to deal with deluge of sensor data stream acquired through a sensor-cloud architecture. We discuss the results in terms of prediction accuracy by simulating sensor data stream to early recognize daily life activities inside an apartment.

Contract Design with Information Asymmetry in a Supply Chain under an Emissions Trading Mechanism

ABSTRACTWe aim to design an appropriate sourcing mechanism with information asymmetry in a supply chain with one manufacturer and multiple suppliers subject to an emissions trading scheme. The manufacturer purchases raw materials from suppliers, who hold private information regarding the green degree—that is, the unit emission rates—of their raw materials. An appropriate strategy must be adopted by the manufacturer for the contract design, including a series of payments and the order quantities; the suppliers are subsequently invited to bid for the contracts. The basic model is formulated to assist the manufacturer in designing a reasonable contract for a single supplier. The characteristics of the optimal order quantity and payoff functions of both the manufacturer and supplier are analyzed. A competitive procurement scenario with multiple suppliers is also discussed. With respect to the diversity of auctions, three different auction types are analyzed, including a green degree auction, a price auction with emissions targets, and a performance‐based auction. In addition, an efficient emissions trading policy is established to guide manufacturers regarding how to balance their emission allowances based on the optimal order quantities. Our approach provides an effective decision support system for both the manufacturer and suppliers.

Bayesian Network Modeling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Payment

This paper deals with the issue of predicting customers’ default payment. The Bayesian network credit model is applied for the prediction and classification of personal loans with regard to credit worthiness. Referring to credit experts and using K2 algorithm for learning structure, we set up the dependency conditional relations between variables that explain default payments. Then, the parametric learning is adopted to detect conditional probabilities of customers’ default payment. The parameters are estimated on the basis of real personal loan data obtained from a Tunisian commercial bank. The Bayesian network analysis has revealed that customers’ age, gender, type of credit, professional status, and monthly repayment burden and credit duration have an important predictive power for the detection of customers’ default payment. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers through the use of a Bayesian Network model.

A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem

In this paper, we propose a multi-objective integer linear programming model aiming at efficiently planning and managing hospital operating room suites. By effectively exploiting a novel hybrid genetic solution approach, the devised optimization model is able to determine, in an integrated way, (i) the operating room time assigned to each surgical specialty, (ii) the operating room time assigned to each surgical team, (iii) the surgery admission planning and (iv) the surgery scheduling. The resulting Pareto frontiers provide a set of “optimal” solutions able to support hospital managers in efficiently orchestrating the involved resources and planning surgeons and surgeries. On this basis, the proposed solution framework could represent a suitable engine for the development of advanced and effective health care management decision support systems.

Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling

This paper is an investigation into the feasibility of using artificial neural networks (ANN) in conjunction with data envelopment analysis (DEA) for performance measurement and prediction modeling of Class I railroads in the United States. For this exploratory study, DEA-ANN are combined into a two-stage modeling approach. While it is frequently used as a benchmarking tool, DEA lacks predictive capabilities. However, ANN has strong nonlinear mapping and adaptive prediction functionality. In this study, the advantages of combining these complementary methods into an integrated performance measurement and prediction model are explored. For this combined approach, a Charnes, Cooper and Rhodes (CCR) DEA model is used to evaluate the efficiency of each decision making unit (DMU) and to capture the efficiency trend of each railroad. Based upon those DEA results, the follow-on backpropagation neural network (BPNN) model predicts an efficiency score and target output for each DMU. This is a new attempt to extend the BPNN model for purposes of best performance prediction. The resulting framework is an effective benchmarking and decision support system which adds adaptive prediction capabilities to current benchmarking practices.

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Model and Heuristics

This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes.

An online platform for conducting spatial-statistical analyses of national census data across Australia

In recent years there have been significant advances in online planning and decision support systems that encompass spatial and statistical analysis and visualisation services. In this paper we provide an analysis and evaluation of one particular online spatial-statistical platform, known as the Australian Urban Research Infrastructure Network (AURIN) portal. By adopting the Statistical Data and Metadata Exchange (SMDX) format, more than 500 socio-economic data products can be dynamically searched, browsed, accessed and analysed via the AURIN portal. The significance of this approach is that urban geographers can access both the datasets and statistical analysis tools via the same online Portal and thus in theory, conduct their research more efficiently.In conducting our research we specifically focus on three practical use cases which utilise machine-to-machine access from the AURIN portal to the national census data maintained by the Australian Bureau of Statistics (ABS). The first use case involves location quotient analysis in New South Wales to analyse the financial sector. The second use case focuses on understanding economic growth across industry sectors across Queensland using shift-share analysis. The third use case aims to identify spatial autocorrelation between low income and other spatial variables in South Australia. Through this in-depth case study approach we have identified there are some strengths and weaknesses with the AURIN portal. We have demonstrated that the portal can successfully search, interrogate and visualise spatial-statistical data from across Australia. We have also demonstrated that the AURIN portal can successfully conduct simple spatial-statistical analysis all via a single online platform. However, we have also found there remains significant challenges in manipulating and visualising complex multi-dimensional datasets through the portal. Yet these challenges are not considered insurmountable and further research should endeavour to address them. In conclusion, it is important to highlight that online platforms such as the AURIN portal hold significant promise as effective planning and decision support systems which can be used to better shape our cities.

A framework for production rescheduling in sociotechnical manufacturing environments

No plan survives contact with reality. Despite the rich research base regarding handling uncertainty in production planning and control systems, there is an intellectual gap between theory and practice with regard to handling unforeseen events generated by internal and external factors, such as unforeseen machine downtimes and changes in demand. Motivated by longitudinal observations in two industrial settings and an analysis of the relevant literature, a framework for rescheduling decision-making in the face of unforeseen production events is proposed. In practical settings, the effectiveness of decisions depends on a set of situational factors. The findings of this research can be utilised further to provide guidelines for developing effective decision support principles and systems, addressing the needs of organisational decision-makers.

An Improvised Topsis Approach to Select Web Source as External Data Source for Web Warehousing

Objective: The main objective of the paper to incorporate the external web-data efficiently to web-warehouse, as the evolution of web and the requisite of data analytics necessitate it for effective decision support system. Methods/Statistical Analysis: Since the data owned of any organization is insufficient for decision support system. Nevertheless dynamic and complex nature of web pose various challenges during selection of relevant web-data. So evaluation of web resources to select as external source for web-warehouse is the crucial phase during warehousing. Various Multi Criteria Decision Making (MCDM) approaches have been used for it. All these approaches evaluate the web resources on the basis of a set of features which define the relevancy of the resource. Findings: The main focus is on one of the approaches of MCDM viz. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach and also improvised the TOPSIS approach for efficient evaluation of the web resources. In traditional TOPSIS approach Euclidean distance has been measured to compute the proximity of real web-sources from Ideal web-sources. The Euclidean distance measure only the distances between the real and ideal web-resources but not the differences between them. In order to compute the differences between real and ideal web-resources Kullback-Leibler divergence method has been incorporated in the place of Euclidean distance method. Application/Improvements: The improvised TOPSIS computes symmetric as well as asymmetric distances to compute the differences, so efficient to compute the proximity in order to evaluation of web-resources.

Risk Assessment of Infrastructure System of Systems with Precursor Analysis.

Physical infrastructure systems are commonly composed of interconnected and interdependent subsystems, which in their essence constitute system of systems (S-o-S). System owners and policy researchers need tools to foresee potential emergent forced changes and to understand their impact so that effective risk management strategies can be developed. We develop a systemic framework for precursor analysis to support the design of an effective and efficient precursor monitoring and decision support system with the ability to (i) identify and prioritize indicators of evolving risks of system failure; and (ii) evaluate uncertainties in precursor analysis to support informed and rational decision making. This integrated precursor analysis framework is comprised of three processes: precursor identification, prioritization, and evaluation. We use an example of a highway bridge S-o-S to demonstrate the theories and methodologies of the framework. Bridge maintenance processes involve many interconnected and interdependent functional subsystems and decision-making entities and bridge failure can have broad social and economic consequences. The precursor analysis framework, which constitutes an essential part of risk analysis, examines the impact of various bridge inspection and maintenance scenarios. It enables policy researchers and analysts who are seeking a risk perspective on bridge infrastructure in a policy setting to develop more risk informed policies and create guidelines to efficiently allocate limited risk management resources and mitigate severe consequences resulting from bridge failures.

Rock-Quality Study at Tunnel Site in the Kameng Hydro-Electric Project, Bichom, Arunachal Pradesh, India

An idea of the rocks and general geology in the tunnel site is an important basis for developing more effective tunnel design and construction decision support systems to avoid adverse and unforeseen geological conditions in the construction of a tunnel. The main classification systems for rock support estimates, the Q and the RMR, use the most important ground features or parameters as input. The Kameng Hydro-Electric Project (KaHEP) situated in West Kameng district of Arunachal Pradesh is going to use tunnel for channelling the water collected in the reservoir of upstream of Bichom Dam to the powerhouse located at Kimi. An attempt has been made to study different geological parameters, such as Rock Quality Designation (RQD), Rock mass Quality (Q) and Rock Mass Rating (RMR) to classify the rock mass, estimate the stand-up time and to select the support systems. The rock types of the study area comprise mainly of highly fractured quartzite with shear and sericite schist and carbonaceous schist alteration. The stand-up time of the tunnel is very less as the rock mass quality is very poor and poor respectively so it requires immediate support after excavation

Guiding Principles for Data Architecture to Support the Pathways Community HUB Model.

Introduction:The Pathways Community HUB Model provides a unique strategy to effectively supplement health care services with social services needed to overcome barriers for those most at risk of poor health outcomes. Pathways are standardized measurement tools used to define and track health and social issues from identification through to a measurable completion point. The HUB use Pathways to coordinate agencies and service providers in the community to eliminate the inefficiencies and duplication that exist among them.Pathways Community HUB Model and Formalization:Experience with the Model has brought out the need for better information technology solutions to support implementation of the Pathways themselves through decision-support tools for care coordinators and other users to track activities and outcomes, and to facilitate reporting. Here we provide a basis for discussing recommendations for such a data infrastructure by developing a conceptual model that formalizes the Pathway concept underlying current implementations.Requirements for Data Architecture to Support the Pathways Community HUB Model:The main contribution is a set of core recommendations as a framework for developing and implementing a data architecture to support implementation of the Pathways Community HUB Model. The objective is to present a tool for communities interested in adopting the Model to learn from and to adapt in their own development and implementation efforts.Problems with Quality of Data Extracted from the CHAP Database:Experience with the Community Health Access Project (CHAP) data base system (the core implementation of the Model) has identified several issues and remedies that have been developed to address these issues. Based on analysis of issues and remedies, we present several key features for a data architecture meeting the just mentioned recommendations.Implementation of Features:Presentation of features is followed by a practical guide to their implementation allowing an organization to consider either tailoring off-the-shelf generic systems to meet the requirements or offerings that are specialized for community-based care coordination.Discussion:Looking to future extensions, we discuss the utility and prospects for an ontology to include care coordination in the Unified Medical Language System (UMLS) of the National Library of Medicine and other existing medical and nursing taxonomies.Conclusions and Recommendations:Pathways structures are an important principle, not only for organizing the care coordination activities, but also for structuring the data stored in electronic form in the conduct of such care. We showed how the proposed architecture encourages design of effective decision support systems for coordinated care and suggested how interested organizations can set about acquiring such systems. Although the presentation focuses on the Pathways Community HUB Model, the principles for data architecture are stated in generic form and are applicable to any health information system for improving care coordination services and population health.

Treatment preference and patient centered prostate cancer care: Design and rationale

Prostate cancer is a slow progressing cancer that affects millions of men in the US. Due to uncertainties in outcomes and treatment complications, it is important that patients engage in informed decision making to choose the "optimal treatment". Patient centered care that encompasses informed decision-making can improve treatment choice and quality of care. Thus, assessing patient treatment preferences is critical for developing an effective decision support system. The objective of this patient-centered randomized clinical trial was to study the comparative effectiveness of a conjoint analysis intervention compared to usual care in improving subjective and objective outcomes in prostate cancer patients. We identified preferred attributes of alternative prostate cancer treatments that will aid in evaluating attributes of treatment options. In this two-phase study, in Phase 1 we used mixed methods to develop an adaptive conjoint task instrument. The conjoint task required the patients to trade-off attributes associated with treatments by assessing their relative importance. Phase 2 consisted of a randomized controlled trial of men with localized prostate cancer. We analyzed the effect of conjoint task intervention on the association between preferences, treatment and objective and subjective outcomes. Our conjoint task instrument can lead to a values-based patient-centered decision aid tool and help tailor treatment decision making to the values of prostate cancer patients. This will ultimately improve clinical decision making, clinical policy process, enhance patient centered care and improve prostate cancer outcomes.