Decision Support System For Management Research Articles

Decision support system for ship energy efficiency management based on an optimization model

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool.

Research on Intelligent Decision Support System for Student Management and Mental Health Intervention in Higher Education Institutions

Abstract The intelligent management system of colleges and universities is an important display of the informationization construction of colleges and universities. Aiming at the life trajectory and life information of students in colleges and universities, this paper combines data mining and other technologies, adopts Python language and MySQL database technology, and improves on the Flask framework to develop an intelligent decision support system for student management and mental health intervention. The study takes student achievement abnormality detection and mental health intervention as an example, uses the improved K-Means algorithm and Apriori algorithm to mine and analyze the relevant data, and discusses the specific application of the intelligent decision support system. The sample students can be divided into three different types of academic performance: excellent, ordinary, and difficult. Precision teaching can be implemented according to the different types of students. Only 3% of the students had more obvious and serious psychological problems, while 80% of the students had a relatively healthy psychological state. Moreover, the correlation rules between student information and mental health status are analyzed, which can aid in the implementation of mental health interventions. The intelligent system of student management can provide the most direct and effective evidence for academic administrators in making decisions on teaching plans, thus making student education more targeted, personalized, and predictive.

Modeling of biotechnological objects

The article discusses the system of modeling biotechnological objects and biogas production in the process of fermentation of various agricultural wastes. The complexity of automation of biotechnological processes, which consists in the lack of sufficient knowledge about the phenomena associated with the process of fermentation and the synthesis of target materials, is shown. Solutions to control problems using intelligent methods, intelligent control systems, based on fuzzy logic and on the basis of on the basis of neural network technologies, which have proven themselves in the control of complex objects with parameters that vary widely, as well as the analysis of algorithms for the operation of programmable logic controllers that perform the functions of regulators in the systems of operational control of complex processes, adaptive neuro-fuzzy architecture. Distributed in management decision support systems, it is made in the form of a six-layer neural structure, i.e. systems with neuro-fuzzy regulators, are able to largely meet the modern requirements of the designed systems, as a result of which the systems, using the technology of synthesis of intelligent control algorithms, can optimize complex circuits of automatic control systems.

A Geographic Information System Base Spatial Decision Support Systems for Poverty Analysis and Management in Adamawa State, Nigeria

Poverty is a multidimensional, multifaceted, and complex, poverty can manifest in the social, political, economic, environmental and any sector of human existence. In Nigeria pervious government and present administration have adopted many poverty alleviations programs but most of them did not yield any positive result due to lack of spatial Geodatabase. Therefore, this article uses the concept of spatial decision support system and Geographic Information System in assessing the spatial distribution of poverty in Adamawa State as a model for decision making. Spatial decision making are multi-facetted challenges that needs technical requirement because they contain economic, social, environmental, and political dimension. Solving theses complex issues requires integrating and using information systems such as Geographic Information System and Spatial Decision Support Systems. The pragmatic approach was used as the methodology for collecting spatial information and for making inquiries into the complex phenomena. The geographic disparities theory was used as a lens to provide answers to the research questions. The Poverty situation in Adamawa State was measured using the Fosre, Greer and Thorbecke (FGT) index to determine the incidence of poverty in the state. A GIS base spatial decision support system model was design for decision making on how to alleviate poverty in disadvantage areas of the state.

A COMPARATIVE ANALYSIS WITH THE BLACK BOX MODEL & DSS SYSTEM

This article explores Decision Support Systems (DSS) in emergency management and scrutinizes the mathematical and technological perspectives of DSS. It focuses on how these DSS handle uncertainty, manage resources, and make choices during emergencies. Some features that distinguish them as strong are data analysis as well as coping with ambiguity, while other characteristics include things like real-time monitoring and visualization of data. At the same time, there are certain obstacles to their implementation, such as inadequate data generation or refusal by users. Therefore, instances from real life based on various kinds of disaster-stricken zones are illustrated in this research work. Also, we will find out the interconnection term in Emergency DSS in Uncertainty. It explores the Black Box model and designs the main processes in Emergency DSS in Uncertainty. This work analyzes quantitative adaptation, modeling and optimization, iterative improvement, mathematical formulation, system analysis utilities, and system modeling in dynamic cases.

Effects of clinical decision support systems in chronic disease management

Effects of clinical decision support systems in chronic disease management

A Conversationally Enabled Decision Support System for Supply Chain Management: A Conceptual Framework

This paper introduces a conceptual framework for integrating Conversational AI (CAI), specifically conversational agents (CAs), with Decision Support Systems (DSS) to enhance Supply Chain Management (SCM) decision-making processes. In today’s complex supply chain environment, characterized by diverse processes and entities operating across different geographic locations, the effective use of AI in DSS is crucial. The proposed framework envisions a Conversationally Enabled Supply Chain (CESC) where decision-makers interact with the DSS using natural language through a CA, facilitating tasks such as data analysis, scenario analysis, and simulation. The choice of a conceptual framework as a research tool provides a systematic approach to collect and organize elements, offering a clear reference structure and a common language. This framework aims to enhance understanding, guide research and analysis, and integrate knowledge from diverse sources, contributing to a holistic understanding of the proposed CA-empowered DSS for SCM. The paper emphasizes the significance of CESC and sets the stage for future research and development in the domain, providing a foundation for ongoing work.

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis

Abstract With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.

On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World

On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World

Artificial Intelligence-Driven Decision Support Systems for Sustainable Energy Management in Smart Cities

Artificial Intelligence-Driven Decision Support Systems for Sustainable Energy Management in Smart Cities

Development of a Big Data Analysis and Management Decision Support System for Student Mental Health in Higher Education

Abstract In recent years, the number of psychological problems occurring in college students has been increasing day by day, and timely, convenient, and accurate psychological warning is an important way to prevent college students from psychological crises. Starting from the basic needs of the system and aiming at realizing the embedding of data mining technology in the psychological management system, this paper elaborates on the design and implementation of the data mining technology module suitable for the psychological management system. The clustering algorithm-K mean algorithm of data mining is used to distinguish groups of students with different categories of psychological problems, which provides data support for the next decision-making. Through simulation experiments between the system designed in this paper and four other mental health management systems, the clustering performance and system evaluation performance of this paper’s system are better than those of the other systems. The result of mining the mental health of college students through this system shows that 82.2% of the student’s assessment results show normal, and 8.35% of the students need to focus on, and this result is not much different from that obtained from the statistical survey of a personality questionnaire, which indicates that the use of clustering analysis in the system can be an effective prediction of the mental health of college students.

МОДЕЛИ И ФУНКЦИИ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ В ЭНЕРГОСИСТЕМАХ

The aim of the work is to study models and intelligent systems for supporting management decisions in energy systems. The article is devoted to solving the problem of adequate description of systems for supporting management decision-making in energy systems, as well as analysing models and functions of intelligent system models for supporting management decision-making in energy systems. As part of the research, methods of generalization and systematization of models, functions of an intelligent management decision support system, as well as systemic, evolutionary, categorical and modelling methods are used to form a conceptual model of the Transnistrian Moldavian Republic power systems which is necessary to further develop a mathematical model of intelligent system for supporting management decisions in energy systems. The novelty of the research lies in the fact that a conceptual model of a decision information system for the power supply control in the power systems of the Pridnestrovian Moldavian Republic is developed. The results of the study include analysing the subject area and developing a conceptual database model and the information support structure in the energy systems of the Transnistrian Moldavian Republic. Findings state that to develop an information system project, a system analysis of the energy industry, in particular information processes in the power supply is performed. The main aims and objectives of the project are determined, which become the basis for constructing an objective tree.

Managing the staffing process of the Samara region oil industry

The problem of constructing dynamic models in the form of differential equations or transfer functions linking the performance indicators of the oil industry of the Samara region with the performance indicators of a regional university with a large institute of oil and gas technologies, considered as the controlling factors of staffing the oil industry, is solved. The input factors of the model are the normative performance indicators of the Samara State Technical University (SamSTU). The effectiveness of the regional oil cluster of the Samara region is assessed by indicators established by the Federal State Statistics Service (Rosstat), in particular, the amount of oil received for processing. For the algorithm of personnel management of the oil industry of the region, the problem of structural and parametric identification of the personnel training process at the university based on statistical characteristics as experimental data is solved. Using the library of typical linear models of management objects by N.S. Raibman, mathematical models of the process of staffing the industry in the form of transfer functions are constructed. Verification of mathematical models is carried out by comparison with official statistical data. On the basis of the obtained transfer functions, the synthesis of algorithms for automatic management of the personnel management process of the regulatory industry in the management decision support system is carried out.

SIMONTO-Pea: Phenological Models to Predict Crop Growth Stages in BBCH of Grain and Green Peas (Pisum sativum) for Temporal Pest Management

Many pests damage pea crops, which potentially leads to reduced quality and yield losses. Since pests occur at different phenological growth stages of pea crops, the prediction of growth stages, for example as BBCH stages, is beneficial. In this study, three models have been developed to simulate growth stages of grain and green pea crops, for the latter with early and late sowing dates. All data, such as BBCH stages and air temperature, were collected in Germany in a three-year study under practical farming conditions at 415 sample sites. For the development of each model, a Gompertz regression model based on the observed data was performed. The model validation suggests that each model precisely and reliably predicts pea crop growth stages for spring-sown peas. Amongst others, the RMSEIndex for grain peas was 3.4; for green peas, early and late sowing dates, respectively, they were 3.4 and 4.5. SIMONTO-Pea (SIMulation of ONTOgenesis) is the first model that predicts detailed pea crop growth stages based on the BBCH scale. This innovation is especially beneficial for users such as advisors and farmers dealing with spring-sown pea crops as a decision support system in monitoring and pest management according to pea crop growth stages.

Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.

A multi-agent digital twin–enabled decision support system for sustainable and resilient supplier management

Industry 4.0 is reshaping the manufacturing landscape, bringing about revolutionary changes in both product manufacturing and collaborative interactions among value chain constituents. Within the manufacturing value chain process, sustainable and resilient supplier management is recognized as a strategic endeavor that drives value creation, such as enhanced product quality, risk mitigation, and improved organizational reputation. Sustainable and resilient supplier management processes necessitate the integration of multiple criteria, efficient supplier coordination, and adaptability to evolving challenges. Nonetheless, conventional supplier management practices primarily rely on expert judgment, focus on the present situation, and lack a robust decision validation mechanism, leading to a limited capability to constitute proactive managerial decision-making under uncertainty. This paper introduces a digitally enabled decision support system for facilitating an intelligent and customized supplier management process in dynamic and complex environments. The system leverages stratified fuzzy decision-making techniques to account for the potential impacts of future events. Additionally, it integrates multi-agent digital twin technology to provide a reliable validation mechanism for assessing the effectiveness of supplier development strategies. The effectiveness of the proposed system was demonstrated through its successful implementation within a multinational solar water pump manufacturing company, highlighting its promising potential for practical applications.

Intelligent Decision Support System for Building Project Management Based on Artificial Intelligence

With the deepening of research on artificial intelligence (AI) technology in the field of computer science and the improvement of its social influence, the effective combination of AI technology and engineering project management practice has become a hot topic of discussion in the field of engineering project management. In the application research of AI in engineering project management, most of them use data-driven models to solve project management problems, improve data insight and improve the speed and quality of project management decision-making. Decision support systems have been developed on the basis of management information systems, but they have rarely been successfully applied in project management. The decision support system based on data warehouse is a new level of development in decision support systems, providing an effective way for projects to achieve real-time decision-making, thereby achieving synchronous, integrated, and optimized dynamic management of project progress, cost and function, and improving the timeliness and scientificity of decision-making. Therefore, this article proposes the theoretical framework and architecture of a construction project decision support system based on data warehousing. Then, this article proposes a deep belief network (DBN) evaluation method to evaluate the performance of the system, and the results show that the proposed method has superior performance.

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Maintaining interconnected infrastructures such as transportation, communication, power grids, and pipeline networks is paramount in emerging economies. One of the critical interruptions is the targeted attacks on the operating cyber-physical systems to disconnect operations, inspection, or monitoring of the system. Therefore, adopting a cybersecurity system (or platform) that provides holistic protection is vital for protecting the integrity of critical infrastructure networks. As such, this research aspires to provide a decision support system for cybersecurity managers or practitioners (in the Indian power sector) to select the best and appropriate platform for protection against cyber-attacks. A three-phase method is adopted. First, a literature search followed by an expert panel discussion identified alternatives (cybersecurity platforms) and selection criteria. Next, a questionnaire was developed. Thirdly, a hybrid Best-Worst Improved and COmprehensive distance-Based RAnking (BWM-I and COBRA) method was proposed and applied to evaluate the cybersecurity platform alternatives. Four alternatives (Cloud-Based Platforms, Web-Based Platforms, Application-Based Platforms, and AI-Based Platforms), six primary criteria, and fifteen unique sub-criteria were identified. Responses were collected from 80 power utility managers on a pan-India basis, ranking "End-to-End Coverage" criteria and the AI-Based platform as best. This approach identified the best cybersecurity platform that, if adopted, can be extended to other critical infrastructures, with an appropriate adjustment in the selection criteria. The study can be helpful to practitioners in evaluating cybersecurity platforms. Furthermore, it addresses a research gap in its application in a developing country like India.

ИННОВАЦИОННЫЕ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ: АНАЛИЗ СУЩЕСТВУЮЩИХ СИСТЕМ И ИХ ПРИМЕНЕНИЕ

In this scientific article, the analysis, development and application of innovative management decision support systems was carried out. Various systems were reviewed such as SAP Business Objects, IBM Cognos, Oracle Business Intelligence and ITSM 365. Each system was analyzed against a number of criteria such as data analytics capabilities, usability, performance and price. As a result, the advantages and disadvantages of each system were identified, which helped to determine their effectiveness and suitability for use in enterprises. It was found that the development and implementation of management decision support systems can face difficulties, such as the high cost of development, the need to take into account the specifics of each individual organization and business processes, as well as the complexity of implementing and training staff.