Decision Support System Model Research Articles

Fuzzy - Based Multi - Criteria Decision Support System for Maintenance Management of Wastewater Treatment Plants

Abstract Wastewater treatment plants (WWTPs) contain a large number of components, and these components in turn require a large number of maintenance activities and high costs. In this paper, a Fuzzy-based Multi-Criteria Decision Support System (FBMCDS) model was designed based on the failure mode and effect analysis FM-EA and applied to the Rustumiya Project (RP) in Iraq. Information of the RP’s components, failure modes, applied maintenance activities and costs were collected from the documented data, site visit and face-to-face interviews as well as opinions of 44 experts. Through applying the fuzzy logic to analysis the failure modes and effects, the risk priority index (FMRPI) and total intensity (FMTI) of each failure mode ware computed. Thus, maintenance priorities and weights of the RP’s failure modes were specified. In addition, the best maintenance plan was specified based on the FMRPI-FMTI diagram that shows the importance and the type of maintenance required for each failure mode. Failure of the submersible pump has the first rank in terms of priority, while the last priority was occupied by the building failure mode, where the mechanical failure modes are of the highest importance, followed by the electrical failure modes. Finally, the construction failure modes are ranked last due to the small probability of failure. The designed model is considered an efficient tool due to the similarity of the results with the reality of the situation and the ease of reading and displaying the results. In addition, this model can be applied to other projects such as water treatment plants and irrigation projects.

Impact of pre-exposure and post-exposure prophylaxes prevention programme on HIV burden and services in a low-resource setting: a simulation modelling approach.

Introductionsub-Saharan African countries contribute substantially to the global HIV disease burden. Despite this burden, and the promises that prevention could deliver, the implementation and uptake of HIV prevention programmes are still low. The study used the decision support system model to explore the potential impacts of prevention implementation on HIV burden (incidence) and service delivery.Methodsan operational research technique known as discrete event simulation model was used to capture an individual patient´s pathways through the HIV care process from diagnosis to treatment and monitoring. The regular monitoring, over a 5-year period, including all the activities and resources utilized at each stage of the pathway were analysed, and the impact of increasing prevention measures for an HIV treatment service in a treatment centre in Nigeria was tested using the simulation model.Resultsforty-three patients currently access the Pre-Exposure Prophylaxis (PrEP) and Post Exposure Prophylaxis (PEP) annually, with a 20% and 80% split in the number of patients offered PrEP and PEP, respectively. Scenarios-based on increasing the number of people offered PrEP and PEP from 43 to 250 with a 50/50 split were tested. The outputs revealed improved preventive care by averting new HIV cases, reduction in service demand and utilization, but an increase in the required human resource as well as financial burden. In the next 5 years, the cumulative averted HIV cases are expected to increase from 2 and 5 people (baseline) to 24 and 20 people for PrEP and PEP, respectively. The potentially averted 2 cases per infected persons based on the basic reproductive number of HIV.Conclusionthe effective implementation of PrEP/PEP programme offers an additional safety measure to prevent HIV transmission in at-risk individuals and possibility of ending HIV epidemic.

Visual Decision Support System Model for Managing Unemployment by Hybrid Education

Hybrid eeducation is an approach that helps in conducting face-to-face and online ddistance learning especially for formal education. However, during COVID-19 pandemic, formal education institutions including schools and universities as well as informal education tthrough the media for communities and industries are practiced by the lawmakers to manage workplace distancing. Food supply chain are experiencing unexpected disruptions due to interrelated activities that will determine supply chain performance and efficiency. This article explores the challenges faced by food industry and related sectors due to emergence impact of COVID-19 in Malaysia. Food supply chain management (FSCM) is a complex task involving interconnected activities and numerous stakeholders for a specific food product from raw materials to final product before it reaches the end customers. An overview of COVID-19 scenarios, followed by the impact of pandemic towards the activities in food supply chain, and the management issues that need to be addressed are presented. Several challenges identified are including the worldwide implication of the pandemic scenarios to food supply chain management and food supply chain activities, and indirect impact of supply chain disruption to labour force; unemployment and retrenchment. Finally, a visual decision support system (VDSS) model is developed to illustrate the main impacts of the pandemic scenario on food supply chain as well as addressing the labour management and opportunities. It has potential to provide general guidelines to educators, policy-makers, and decision-makers to deal with uncertainties after the pandemic.

A decision support approach to achieve competitive advantage for a hypermarket chain

This paper describes the decision support approach used in the development process of the S Group's Prisma hypermarket chain in Finland. The management was looking for a new and sustainable operating model for the rapidly growing chain, and contacted the author to consult in the process. Fierce competition forced the search for new business ideas, tools and methods that would provide a clear competitive advantage. To find new perspectives, we decided to use statistical approaches and various decision support system options, such as multi-criteria modelling. A database was available for research and analysis, including data on purchasing behavior and key performance indicators (KPI). The approach had to take into account the role and impact of customers. It was highly important to include customer behavior in the analysis using shopping basket data. Shopping basket data was central in the current paper. From these, an observation matrix was created combining shopping basket data, product data and customer background information. Using multivariate methods, customer groupings and profiles were created with the data from the observation matrix. Using the customer profile and KPI data, a multi-criteria decision support system was produced to support strategic planning. The decision support system (DSS) model was created together with a market chain operational expert and an external methodological expert. We used the VIG software package developed by Korhonen (Belg J Oper Res Stat Comput Sci 27(3):15, 1987) to solve the problem because it is easy to use and requires no prior knowledge of computers or multi-objective linear programming models. Pareto Race plays a central role in the VIG system. The chain expert easily learned how to use and work with the model. The results were immediately visible and could be used to examine alternatives and assess their appropriateness. It was decided to present five different scenarios to the hypermarket chain management. The main objective of the development process was to develop a strategy that would provide the Prisma hypermarket chain with a long-term competitive advantage. Various models were developed and used to support the strategy work by analysing and exploring the data collected, prioritising and selecting decision options. Two currently retired managers (Mönkkönen, S Group, the chain manager, Prisma chain, Interview 02.06.2021, 2021), who were involved in the development process, rated the strategy process as very successful and the modelling carried out during the process significantly supported decision-making. The immediate help of DSS modelling for decision making comes from being able to provide decision makers with reasonable, better solution options to support their decision making. The final impact of decisions could be evaluated after a longer period of time, which in the case of the Prisma development project results means several comparable financial years. Finland suffered exceptionally badly from the financial crisis and the global economic downturn in 2008–2009. The Prisma chain has survived the periods and crises described above without any loss-making years, and the whole chain has grown from 16 units in 1992 to 68 units in 2020.

Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety

When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.

Selection of optimal forecasting models in the navigation safety decision support system

One of the most important components of the navigation safety decision support system (NS DSS) is the forecasting block. The results of his work have an impact on the type of control actions formed by the system. The effectiveness of this block depends on the forecasting methods used. The accuracy of the result of the forecasting block depends largely on the choice of the forecasting method.To develop reliable forecasts, it is necessary to determine forecasting methods, in relation to the specifics of the functioning of the NS DSS. Several ways of solving the problem of choosing the appropriate forecasting method in relation to the NS DSS are proposed. Two methods are demonstrated: Artificial neural networks (ANN) using precedents and Intelligent process analysis (IPA). Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS. Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS, which allows us to assert the relevance of this problem and further research.

Multivariate Analysis of the Health Status of the Elderly Based on the Decision Support System Model and Countermeasures

Objectives: At present, China’s population over 60 years old has been increasing year by year. As the problem of population aging is becoming more and more prominent, followed by a variety of health problems, which is studied. Methods: In this paper, Bayesian technology and decision tree technology are applied to the health assessment system of cardiovascular and cerebrovascular chronic diseases. Results: A health management system for chronic diseases is developed to enable health management experts to assess health according to physical examination data. Conclusion: The management of chronic cardiovascular and cerebrovascular diseases in physical examinees can reduce the incidence of chronic diseases and improve the quality of life of physical examinees.

Evaluating and improving the sustainability of ecosystem services in river basins under climate change

This paper presents a new framework for evaluating the sustainability of basin-wide ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services. In this framework, the Soil and Water Assessment Tool (SWAT) and MODSIM11A generalized river basin decision support system and network flow model developed at the Colorado State University. models and experts' opinions are used to evaluate the ESs.To show the applicability of the proposed framework, it is applied to the Zarrinehrud river basin under three different climate change (CC) scenarios (i.e., RCP 4.5, 6.0, and 8.5) for two different time horizons (i.e., 2020–2049 and 2020–2098). This basin is the main water supplier of the largest hypersaline lake in the Middle East, Lake Urmia. In the next step, 128 water resources management (WRM) scenarios are taken into account considering the projects defined by Urmia Lake Restoration National Committee (ULRNC). All ecosystem services are evaluated considering all WRM and CC scenarios. Finally, a group COPRAS-based decision-making approach is used to determine the best WRM scenario under climate change. The results show that WRM scenario 128 is the best scenario for improving ecosystem services in the study area. This scenario includes some projects such as allocating water to the lake from new resources, rehabilitating irrigation and draining networks, and improving cropping patterns.

An automated and optimized geo-computation approach for spatial fire risk modelling using geo-web service orchestration

Forest fire detection through space-based observations using thermal sensors is in operation since more than one decades. These active fire alerts are generated by using products from MODIS and SNPP–VIIRS satellites and are limited up to identification of active fire locations based on recorded temperature in satellite sensors. However, relation of fire location with forest type map, terrain profile and weather data are required for planning and monitoring of vulnerable areas. An attempt is made to develop online Spatial Decision Support System (SDSS) and spatial model for forest fire risk mapping. The developed SDSS is based on Geospatial Web Service Orchestration (GWSO) workflow which enables forest fire risk alerts and advisories as Web Map Service, Geo SMS and a Geoportal application. User can use this SDSS with minimum technical skills and limited computation resources to generate daily forest fire risk map for fire monitoring and management.

Towards decision support systems for energy management in the smart industry and Internet of Things

Decision Support Systems (DSS) have widely provided on intelligent human–machine interactions like the Internet of Things (IoT). As the dimensions and complexity of the IoT communications between intelligent devices, industrial equipment, sensors, and mobile applications are continuously increasing, supporting Service Level Agreements (SLA) for a large amount of cloud data centers and a huge number of user requests is going to be more challenging. This challenge would be more complicated when the energy consumption of the industrial IoT ecosystems increases exponentially as well. Therefore, DSS models are required for automated decision-making in critical IoT environments like intelligent industrial systems and smart cities. This paper provides a comprehensive review of the DSS strategies for energy-efficient IoT applications in intelligent urban computing. The main objective of this review is (1) presenting a technical taxonomy to categorize existing energy-efficient DSS models for industrial and smart environments in IoT ecosystems; (2) recognizing significant research trends in the field of energy-aware DSS strategies for intelligent urban computing in IoT and (3) presenting a technical and statistical analysis of reviewed studies and evaluation factors. Finally, innovations as forthcoming issues and new challenges of energy-based DSS strategies for intelligent urban computing in IoT are presented.

Designing for construction procurement: an integrated Decision Support System for Building Information Modelling

PurposeMany applications of Building Information modelling (BIM) are already integrated into project management processes. However, the construction industry is suffering from poor decision-making, especially during procurement where fundamental decisions are made. To make the best decisions at earlier project stages, such as design, large amount of information needs to be processed and classified. Therefore, this study seeks to create a Decision Support System (DSS) for construction procurement through the application of existing informatics infrastructure and BIM applications.Design/methodology/approachLiterature review expert interviews and case studies with complex procurement considerations were used to identify and validate attributes and criterions for procurement decision-making. Accordingly, Multi-Attribute Utility Theory (MAUT) methodology was used and mathematical models were driven as the foundation for a DSS.FindingsFive major criterions of time, cost, relationship quality, sustainability and quality of work performed was identified for complex construction procurement decision-making. Accordingly, a DSS structure and mathematical model was proposed. Based on this a model architecture was developed for the integration of the DSS into Autodesk Revit as a BIM platform, and assist in pre-contract decision-making.Practical implicationsThe results can be used in pre-contract selection processes via currently used BIM applications. The model architecture can integrate DSS outputs to nD models, cloud systems and potentially virtual reality facilities to facilitate better construction operations and smarter more automated processes.Originality/valueThis study formulates and captures complex and unstructured information on construction procurement into a practical DSS model. The study provides a link to integrate solutions with already available platforms and technologies. The study also introduces the concept of designing for procurement; which can be expanded to other challenging decisions during construction.

Modeling and Implementation of a New Negotiation Decision Support System for Conflict Resolution Under Uncertainty

Modeling and Implementation of a New Negotiation Decision Support System for Conflict Resolution Under Uncertainty

Applying the peak‐end rule to decision‐making regarding similar products: A case‐based decision approach

AbstractThis study proposes a marketing decision support system (DSS) for firms using case‐based and peak‐end approaches to model consumers. The proposed DSS is composed of model and estimation methods. We employ a similarity function used in case‐based decision theory to examine the degree of similarity between the past and current products offered to a consumer. The contributions of this study are as follows: First, by extending the peak‐end approach, the proposed model could be utilized to analyse not only the same product but also multiple similar products. The DSS could be applied to a broad range of decision problems. Second, by extending the case‐based decision model, our DSS considerably reduces the number of computational operations needed. Third, the model demonstrated the best fit among the compared models and possessed high prediction accuracy when analysing the viewing data for Japanese television dramas. The DSS could increase the future purchase probability of a product. Our research bridges two significant concepts: case‐based decision models in DSSs and peak‐end rules in behavioural economics.

Topical 3D modelling and simulation of air dispersion hazards as a new paradigm to support emergency preparedness and response

Releases into the atmosphere resulting from accidents or malicious activities may be extremely diverse in nature and have harmful consequences for human health and the environment. Successfully managing such releases is a matter of the utmost importance for security services and governmental authorities. For some decades, Atmospheric Transport and Dispersion (AT&D) models embedded in Decision-Support Systems (DSS) have been operated in order to simulate the space and time distribution of hazardous materials and to assess their health impact on the population and on first responders in cases of emergency. Unfortunately, the AT&D models in DSS generally oversimplify the local or regional environment and are compromised by insufficient space and time resolution close to the source of the release. Thus, we have developed a DSS based on 3D multi-scale weather forecast and Lagrangian AT&D models in the frame of a project called “EMERGENCIES”. As an emblematic example, we have performed dispersion simulations of fictitious noxious releases from the inside or outside of public buildings both at micro-scale in a very large domain encompassing the city of Paris and its suburbs, and at meso-scale beyond this domain. Furthermore, results have been produced in a time lapse consistent with emergency management thanks to large, yet affordable computational resources. Providing emergency response actors with such operational, accurate and reliable results may be extremely advantageous for both preparedness and response to emergencies. Ultimately, state-of-the-science AT&D models offer a sound validated new paradigm to support decision-making by security services and their authorities facing contingent Chemical, Biological, Radioactive, Nuclear or Explosive (CBRN-E) releases into the air.

КОНЦЕПТУАЛЬНІ МОДЕЛІ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕНЬ ПРИ КЕРУВАННІ МНОЖИНОЮ ПРОЄКТІВ МУНІЦИПАЛЬНОЇ СФЕРИ

Purpose. As a rule modern organizational systems in any industry contain many elements. These elements are connected by a complex scheme of relationships. In the municipal sphere, various programs and projects are implemented simultaneously. To increase the efficiency of work with projects, it is necessary to solve operations management problems. It is necessary to coordinate the allocation of resources, assess the quality of implementation of individual stages of each project, take into account the joint use of contractors, assess risks and eliminate problem situations. The purpose of the research is to improve the conceptual models of decision support systems to create information technology for managing the implementation of many programs and projects in the municipal sphere. Methodology. For the implementation of operational management of many projects, it is advisable to have the appropriate information technology and tools. The development of such technologies requires a certain methodological approach. The difficulties of structural synthesis of executive systems are associated with the uncertainty of the design tasks conditions. It is impossible to fully specify the algorithms of functioning and interaction of executive structures. Results. An improved ontological metamodel of the problem area and an improved model of the decision support system for managing multiple projects have been developed. Originality. The ontological metamodel of the problem area differs in that it has several strata, each of which identifies the relevant problems and aspects of the organization. The model of the decision support system has been improved by adding a formal static description of the implementation process of many projects and their relationship with many attributes and aspects. Practical value. An ontological metamodel allows to establish hierarchical causal relationships between different problems. This is especially true when implementing many projects involving many departments. The model of the decision support system allows making operational decisions taking into account many factors and their interrelations on various aspects of activity. All this contributes to the main task – the development of information technology to support decision-making in the organizational processes of municipal bodies and enterprises. References 12, figures 1.

Mathematical model of an intelligent decision support system for road quality management

The experience of operation of decision support systems is analyzed, it is shown that the existing principles of creating these systems do not take into account the failure and change in the performance of individual service element, which depend on the current load on the system. The definition of an adaptive decision support system as an intelligent system that works in an automated mode and is able to function in a changing environment, its internal structure, knowledge base, regulatory framework for management, support tools, system users is given. The tasks of construction a mathematical model of an adaptive decision support system and determining its objective function are set. A distributed decision support system, which receives information containing data from various input sources was studied. For construction a mathematical model of the system under investigation, the procedures for selecting mathematical models and methods of queuing theory were used. To assess the quality and effectiveness of an adaptive decision support system, simulation modeling is proposed.

Real Estate Segmentation: A Model of Real estate Decision Support System

Due to human limitations of computational thinking, the quality of rational decision-making is constrained, and as a result, people encounter bounded rationality. A decision support system is widely demanding in tackling this problem, especially in real estate management. This study focuses on 3 main purposes. Firstly, comparing K-means, X-means and K-medoid algorithm performance in clustering sold house characteristics to be further used for pricing houses. Second, characterizing each cluster for developing a suitable marketing strategy by utilizing machine learning technology. Lastly, providing a managerial implication as a decision support system for assisting stakeholders in making a decision. Eventually, K-means and X-means algorithm show very similar performance. X-means can automatically determine the number of clusters while k-means utilize the elbow method to find the optimum number of clusters. Three clusters were identified as cluster 0, cluster 1, and cluster 2. Cluster 0 was occupied by 85.77% of low house prices. There are two practical implications of this study. Firstly, the results of clustering analysis which reflected in a model of decision support system. Second, an intuitive and comprehensive methodological framework is presented for helping stakeholders designing a decision support system.

Frame Work to Classify Data in Interactive System to Enhance Decision Making

Decision-making is a process of choosing among alternative courses of action for the purpose of attaining a goal or goals. The ultimate objective of data analytics is to ease the decision making process but this has lot of challenges and proper planning is the only way to overcome. The idea behind this research work is to propose a novel framework for data analytics to make effective decisions in an organization by aiding the various stages of the decision making mechanism. According to the design science methodology, the research has been formulated and used in the frame work design process. A novel framework was proposed that combines different aspects of data analytics, needed architectures and tools are incorporated in the various stages of decision making process. Based on the Simons the decision-making process, a new framework was designed with 4 phases namely Data, analytical, model deployment and visualization. The decisive objective of the proposed framework is to ease the process of decision making and also to take effective decision. In the process of future planning by the organization, it needs simple accurate estimation techniques for predictions to make effective decisions. Predictions always deal with the future events based on past incidents or records. Different kinds of predictions have been done regularly in many fields for the benefit of an individual, a group of people, an organization or a country. Support Vector Machines can be used to create a powerful prediction model because of its capability in classification and regression. The purpose of this research work is to develop a decision support system model was developed using novel algorithm. The newly developed framework has been proposed for the purpose of data analytics and for prediction. In this work, the machine learning algorithms Support Vector Machines (SVM), Random Forest, Decision Tree, Naive Bayes and the newly proposed algorithm has been analyzed and results are compared. The outcome of this research work proves that the proposed framework model provides better result than other model. The objective of designing and developing the proposed framework is to ease the process of decision making in the scenario of interactive system. A student performance assessment is used to evaluate the proposed framework using real data. To test the correctness of proposed framework, an experiment was done with the student data to predict student performance using newly proposed machine learning framework. The result justifies the proposed framework for the decision making process, gives added value.

MACHINE LEARNING BASED CLINICAL DECISION SUPPORT SYSTEM TO PREDICT FETAL HYPOXIA IN WOMEN DURING ANTENATAL CHECK-UP.

BACKGROUND:Most under-five deaths occur within the first month after birth and intrapartum complications are a major contributor to the cause of death. These defects can be easily identified during the ante-natal check-up by use of a non-stress test. Due to the lack of availability of resources and medical experts in remote areas clinical decision support systems powered by machine learning models can provide information to the healthcare provider to make timely and better-informed decisions based on which course of treatment can be planned. AIM:The study aims to develop an accurate and sensitive clinical decision support system model that can identify pathological fetuses based on the fetal heart rate recordings taken during the non-stress test. METHOD: Foetal Heart rate recordings along with 10 other variables were collected from 1800 pregnant women in their third trimester. The data was put through a feature selection algorithm to identify important variables in the set. The data set was randomly divided into 2 independent random samples in the ratio of 70% for training and 30% for testing. After testing various machine learning algorithms based on specificity, sensitivity to accurately classify the fetus into normal, suspected, or pathological Random Forest algorithm was chosen. RESULT:The fetal status determined by Obstetrician 77.85% observations from the normal category, 19.88% from the suspected category, and 8.28% from the pathological category. The Boruta algorithm revealed that all 11 independent variables in the data set were important to predict the outcome in the test set. In the training set the model had an accuracy of 99.04% and in the testing set accuracy was 94.7% (p-value=< 2.2e-16) with the precision of 97.56% to detect the pathological category. CONCLUSION:With the ability of the model to accurately predict the pathological category the CDS can be used by healthcare providers in remote areas to identify high-risk pregnant women and take the decision on the medical care to be provided.

RETRACTED ARTICLE: Research on product decision support system model based on cloud service and embedded system

With the continuous progress of society and the continuous development of science and technology, people's lives have also undergone great changes. In people's daily life, the rapid development of information technology has brought people many new life experiences. Computer network technology takes up an increasing proportion of social development. People are gradually getting used to the convenient life brought by computer network technology, and its development has also changed the way and channels of information dissemination. The development of embedded systems provides a broader space for the construction of computer networks. In the process of development, people also combine cloud service platforms with embedded systems to provide help for product decision-making, but embedded systems themselves have many shortcomings However, the system does not have strong hardware support in the process of storage resources and operation, which will affect the normal operation of the cloud service platform, so the problems existing in the embedded system must be solved. In order to improve the effectiveness and scientificity of product decision-making, this article builds an effective technical framework for the system construction of product decision-making based on the analysis of cloud service decision-making platform construction. This study also evaluated the performance of the product decision-making system by establishing a mathematical model, proving that the construction and use of the system can provide effective help for decision-makers’ decision-making activities.