Suitable Decision Support System Research Articles

Application of Electre Method Decision Support System for Laptop Selection

In the contemporary era, laptops have transitioned from being perceived as luxury items to becoming an essential tool for work, education, and various learning activities. The market is saturated with a plethora of laptop manufacturers, providing consumers with a wide array of options to cater to their specific needs and budget constraints. However, a common challenge faced by consumers is the mismatch between their requirements and the specifications of available laptops, often due to a lack of understanding. Addressing this issue requires the implementation of a suitable Decision Support System (DSS). The proposed solution involves employing the Electre method, renowned for its multicriteria approach. This method systematically eliminates unsuitable alternatives, facilitating the identification of laptops that align with the desired criteria. The process involves several key steps, such as defining criteria and alternatives, assigning weights, normalizing the decision matrix, determining concordance and discordance indices, and ultimately establishing an aggregate dominance matrix. By leveraging the Electre method, this Decision Support System can effectively recommend laptops that not only meet the consumer's specified criteria but also align with their budget constraints. This systematic approach aims to enhance the overall decision-making process, ensuring optimal satisfaction for laptop buyers in terms of both performance and financial considerations.

Estimation of fault probability in medium voltage feeders through calibration techniques in classification models

Machine Learning is currently a well-suited approach widely adopted for solving data-driven problems in predictive maintenance. Data-driven approaches can be used as the main building block in risk-based assessment and analysis tools for Transmission and Distribution System Operators in modern Smart Grids. For this purpose, a suitable Decision Support System should be able of providing not only early warnings, such as the detection of faults in real time, but even an accurate probability estimate of outages and failures. In other words, the performance of classification systems, at least in these cases, needs to be assessed even in terms of reliable outputting posterior probabilities, a really important feature that, in general, classifiers very often do not offer. In this paper are compared several state-of-the-art calibration techniques along with a set of simple new proposed techniques, with the aim of calibrating fuzzy scoring values of a custom-made evolutionary-cluster-based hybrid classifier trained on a set of a real-world dataset of faults collected within the power grid that feeds the city of Rome, Italy. Comparison results show that in real-world cases calibration techniques need to be assessed carefully depending on the scores distribution and the proposed techniques are a valid alternative to the ones existing in the technical literature in terms of calibration performance, computational efficiency and flexibility.

Understanding carsharing: A review of managerial practices towards relevant research insights

The carsharing market has never been as competitive as it is now, and during the last years, we have been witnessing a boom in the number of carsharing organizations that appear, often accompanied by an also booming number of companies that disappear. Designing a viable carsharing system is challenging and often depends on local conditions as well as on a myriad of operational decisions that need to be supported by suitable decision-support systems. Therefore, carsharing is being increasingly studied in the Operations Management (OM) literature. Nevertheless, often due to the limited transparency of this highly competitive sector and the recency of this business, there is still a “gap of understanding” of the scientific community concerning the business practices and contexts, often resulting in over-simplifications and relevant problems being overlooked. In this paper, we aim to close this “gap of understanding” by describing, conceptualizing, and analyzing the reality of 34 business-to-consumer carsharing organizations. With the data collected, we propose a detailed description of the current business practices, such as the ones concerning pricing. From this, we highlight relevant “research insights” and structure all collected data organized by different OM topics, enabling knowledge to be further developed in this field.

Perbandingan Metode SAW dan Topsis pada Sistem Pendukung Keputusan Seleksi Penerima Beasiswa

SMK TI LABBAIKA provides scholarships every semester to students who meet the requirements. The selection process still uses the manual method based on the Headmaster's knowledge. The suitable decision support systems and methods are needed to minimize errors in decision making and get fair decision results. In this study, two decision support system methods were chosen, namely Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The SAW and TOPSIS methods were chosen because they have simple calculations. A comparison of the two methods is carried out to determine the best method for selecting scholarships. The criteria used in this study are student status, number of family members, parent's occupation, parent's income, and semester grades. The 20 alternative data is used for testing. Based on the results of testing the accuracy for the SAW method is 45% while for TOPSIS is 60%.

Extending the CSM-CERES-Beet Model to Simulate Impact of Observed Leaf Disease Damage on Sugar Beet Yield

A CSM-CERES-Beet pest damage routine was modified to simulate the impact of Cercospora leaf spot disease effects on sugar beet yield. Foliar disease effects on sugar beet growth and yield were incorporated as daily damage to leaf area and photosynthesis, which was linked to daily crop growth and biomass accumulation. An experiment was conducted in Southwest Germany (2016–2018) with different levels of disease infection. Data collected included time-series leaf area index, top weight, storage root weight and Cercospora leaf spot disease progress. The model was calibrated using statistical and visual fit for one treatment and evaluated for eight treatments over three years. Model performance of the calibration treatment for all three variables resulted in R2 values higher than 0.82 and d-statistics higher than 0.94. Evaluation treatments for all three observation groups resulted in high R2 and d-statistics with few exceptions mainly caused by weather extremes. Root mean square error values for calibration and evaluation treatments were satisfactory. Model statistics indicate that the approach can be used as a suitable decision support system to simulate the impact of observed Cercospora leaf spot damage on accumulated above-ground biomass and storage root yield on a plot/site-specific scale.

A Rule Based Clinical Decision Support System for Healthcare Industry

Objectives: The main aim of this work is to enhance the quality of medical care by reducing the turnaround time of reminder and maintenance. This work intends to propose suitable Decision Support System (DSS) for medical industry. Methods: DSS can interpret information and create a supporting system for decision making. In medical industry, DSS is highly important since the disease, symptoms of the disease and relevant treatment methods are not predictable. This study proposes a DSS for helping medical experts in a ubiquitous medical system which can use the DSS. Findings: Here we proposed a web-based editor to create a rule which can be understood and can be modified by the clinicians. The proposed DSS can support the healthcare experts by rule-set method and expert system. To improve the quality of care in a medical industry, a clinical reminder to get alert automatically from the clinical documents is of much use. From the evidence of the document, a Novel Rule set is created. The new rule can be inferred and it can be implemented after the approval of clinicians. It is based on the previous rules and histories have to infer new rules to apply. Keywords: Clinical Information System, Decision Making, Decision Support System, Knowledge Extraction

Decision-Support System-based Service Delivery in the Product-Service System Context: Literature Review and Gap Analysis

Manufacturing companies are revolutionizing the way they deal with customers, trying to satisfy their emerging needs with new, Product-Service System-centered (PSS), offerings. The shift towards this offering and its implementation are not easy since the proposal of badly structured offerings leads to unsatisfactory results for all the stakeholders. To obtain the maximum value in use, the way products and services are sold and delivered must be configured properly. In view of this, proper conceptualization, design and engineering methodologies must be adopted, along with suitable Decision Support Systems (DSS) to support them. DSS could help decision-makers in improving the service delivery process, proposing its scheduling, or deciding which kind service and how to provide it to customers. To be effective, DSS must be fed with right data and, in this sense, the implementation of Industry 4.0 technologies results crucial since the availability and exploitation of new, big, and structured datasets could improve DSS reliability. The aim of this work is to investigate how the presence of a DSS focused on service delivery process can enhance PSS offering in the context of Industry 4.0. The authors used a set of keywords related to PSS, service delivery process, DSS and Industry 4.0 to perform the research on the Scopus database. Following, they filtered results according to parameters of interest and, after reviewing the papers’ pool, they identified the final set for the review. The analysis highlighted a gap in literature due to the lack of researches around the improvements in the service delivery process achievable through the implementation of smarter DSS exploiting the Industry 4.0 technologies.

Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran)

Considering the wide spread locations of wind farms in Iran, it is important to develop a suitable decision support system (DSS) to fulfill proper management of wind farms. Extensive literature survey indicates that there are no integrated forms of DSS to manage a set of wind farms. The existing wind farms are performing independently, and there is no practical method for exchanging the online data. DSS can contribute to optimal operation of wind farms, operation and maintenance scheduling, pricing policy, etc. In this study, a geographic information system and RETSCREEN software were linked to the designed DSS to achieve a more suitable result. Also, a huge number of data are constantly collected and fitted into the DSS to predict future performance of the wind farms, support the decision for pricing policy, and develop next generation of wind farms in the studied area. The developed software, called Monitoring Wind Turbines Performance, was applied to a wind farm located in Ganjeh at the northern part of Iran to ensure the capability of the DSS model. The results show that application of this model can lead to both minimal costs imposed to the wind farm owners and considerable margin of economic gain. Setting up such a network and smart technologies can be considered as a useful tool for energy resiliency management in mega cities at the national level.

Neural Network and Fuzzy Logic Based Smart DSS Model for Irrigation Notification and Control in Precision Agriculture

The efficiency and uniformity of irrigation can be maintained from the complex and diverse information based systems by considering weather, soil, water, and crop data. The model is created by suitable decision support system (DSS) algorithm. The DSS model acquires real-time soil and environmental data using our wireless sensor network mechanism. A radial basis function type of neural network is performed to predict hourly soil moisture content (MC) requirement as well as required soil evapotranspiration using Blaney–Criddle method. In this work, a fuzzy logic based weather dependent irrigation control mechanism is also developed and it is integrated with the DSS to generate adequate SMS notifications by interfacing a GSM modem. The soil MC prediction algorithm is implemented by collecting field data from a test land in Bhubaneswar located in the eastern region of India. The comparative analysis is also performed by calculating prediction RMSE, RSE, MSE, RPD and algorithm running time. The proposed smart DSS model is used to compensate the amount of water loss through evapotranspiration by considering weather, soil, water, and crop data.

Evaluation of Organizational Models for Response Centres for Telecare Services of the Future

Introduction: This study was carried out as a sub-activity for the project Model for Telecare Alarm Services. The project is participated by 8 Norwegian municipalities, the academic partner University of Agder, the research partner SINTEF IKT and the business partner Imatis. Municipalities are facing new challenges in the organization of the future Telecare alarm services in the response centres. Currently, municipal health- and social care services are responsible for 24/7 duties in emergency primary health care, including telephone calls (number 116117). In addition, similar 24/7 duties for Telecare alarm services and Telemedicine services on daytime are carried out. The scope of the project is to gain knowledge in how existing telecare alarm services are tackled and pinpoint important challenges when designing new services for the future. The project aims to identify similarities and synergies for combining existing municipal services as inter-municipal collaborative services. Context: Welfare technology is expected to have a great potential to improve safety and security for people living at home while increasing the quality of nursing and care, and contributing to achieve cost-effective services. Today, new digital sensor technologies are in pilot evaluations in several municipalities, while the design of Response Centre services models for the future still is under consideration. In order to scale-up new digital services, it is important to evaluate how a combined alert service can be organized, and how such services can interact with the emergency team of home-based services, while including family relatives involved in the process. Methods: During Autumn 2015 and Spring 2016 health care professionals, representatives from patients and operators at existing alarm centres participated in several workshops focusing on challenges in existing services based on experiences from piloting new digital solutions. Different organizational and future electronic collaboration models between the operator and the emergency home nurse service were evaluated in different lab-test scenarios. Conclusions and discussion: There is a lack of empirical knowledge from existing operational services, and information tracking actions for actual alarm conditions to necessary actions taken at the patient’s home is not available. How the Telecare alarm services are organized differs between the 429 Norwegian municipalities, and there is a clear need of re-organizing to sustainable services for the future change in digital welfare sensor technologies. The project identified several important aspects of requirements for a qualified Response centre service: 1) Certified medical expertise as the response centre operators, 2) Relational skills (personal suitability), 3) Access to updated information about the user's situation, services and past events (registry information, etc. derived from existing Electronic Health Records stored within all the actual municipalities organized within the Response Centre administration), 4) Standardized procedures for urgency assessment and follow-up, 5) Guidelines for evaluation of user needs for sensors and alarm functions, 6) National guidelines for standardized procedures and quality standards including monitoring of process quality indicators.There is a huge potential in designing integrated Response Centre services by combining the different 24/7 duties of today, in robust and uniform services with high skills and certified quality. The project is to be continued in 2016-17 in order to develop methods for effect evaluations, study of possible technical infrastructure solutions, evaluation of legal conditions and guidelines, initiating development of suitable decision support systems tailored to those new services, and giving guidelines on how close relatives and voluntary services could be incorporated in the acute response team.

The quality of service: An overall performance assessment for water utilities

Over the last decades, the delivery of water supply services has changed among regions and actors, in order to meet the changing needs. Those services can be balanced based on the relation between the obligations they must fulfill, the quality offered, and the associated price/value for money. Furthermore, due to the water services inherent characteristics, they are prone to poor quality of service (in general terms). Thus, global performance assessments are of paramount relevance under those constraints. In Portugal, the water regulator has developed a system of performance indicators (partial measures of performance) and, therefore, it is not possible to achieve the desired holistic performance evaluation (global measure). To enable such assessment, we propose an application based on the ELECTRE TRI-nC method to define quality of service categories and aggregate performance indicators. To obtain a coherent family of criteria (i.e., exhaustive, cohesive, and non-redundant) we apply an iso-preference logic. The results obtained are presented through a geographical information system allowing for a clearer visualization of the overall performance of water utilities. The method proposed can be considered a suitable decision support system and useful regulatory tool able to provide policy relevant outputs.

A Survey on Container Processing in Railway Yards

In spite of extraordinary support programs initiated by the European Union and other national authorities, the percentage of overall freight traffic moved by train is in steady decline. This development has occurred because the macroeconomic benefits of rail traffic, such as the relief of overloaded road networks and reduced environmental impacts, are counterbalanced by severe disadvantages from the perspective of the shipper, e.g., low average delivery speed and general lack of reliability. Attracting a higher share of freight traffic on rail requires freight handling in railway yards that is more efficient, which includes technical innovations as well as the development of suitable decision support systems. This paper reviews container processing in railway yards from an operations research perspective and analyzes basic decision problems for the two most important yard types: conventional rail–road and modern rail–rail transshipment yards. Furthermore, we review the relevant literature and identify open research challenges.

Balancing consistency and expert judgment in AHP

The various mechanisms that represent the know-how of decision-makers are exposed to a common weakness, namely, a lack of consistency. To overcome this weakness within AHP (analytic hierarchy process), we propose a framework that enables balancing consistency and expert judgment. We specifically focus on a linearization process for streamlining the trade-off between expert reliability and synthetic consistency. An algorithm is developed that can be readily integrated in a suitable DSS (decision support system). This algorithm follows an iterative feedback process that achieves an acceptable level of consistency while complying to some degree with expert preferences. Finally, an application of the framework to a water management decision-making problem is presented.

Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

Abstract The ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380–1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490780 nm (r2 = 0.69), 510780nm (r2 = 0.74), 5161300nm (r2 = 0.62) and 5401300 nm (r2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490780nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation

Fire is the main cause of forest destruction in Mediterranean basin countries. Long-term prediction is intended for long-term planning which may serve to characterize and cluster regions as subject to high or low fire risk. This will enable the development of a rational and sensible forest fire prevention and protection policy. The problem with the existing approaches of long-term forest fire risk clustering is that they use crisp sets applying specific cluster boundaries. On the other hand, fuzzy algebra can provide reliable and flexible means of modeling that can be applied by a suitable decision support system. Given a specific area of interest, the evaluation of the long-term forest fire risk can be performed by the use of a triangular and a trapezoidal membership function. The decision support system that has been developed applies an inference mechanism that is based on various aspects of fuzzy sets and fuzzy machine learning techniques. The system has been applied in Greece, but it can be used on a global basis. Results show that the system successfully estimates the forest fire risky areas.

Knowledge representation in expert systems for process control

The application of so-called “higher automation functions” in complex systems is usually connected with considerable difficulties. In most cases these automation tasks can satisfactorily be solved only by the help of a suitable decision support system. Considering the knowledge base in process automation a special framework for knowledge representation in XPS for process control and an original inference strategy will be discussed. Features of quality and demands on knowledge representation components in real-time expert systems are formulated. The advantages of hybrid representation schemes utilizing algorithmic (deep) knowledge mainly in the procedural part of the knowledge base are pointed out. The presented solution was introduced developing the real-time expert system shell PROCON.