Decision Support System For Planning Research Articles

Temporal Lobe Epilepsy Focus Detection Based on the Correlation Between Brain MR Images and EEG Recordings with a Decision Tree

Background/Objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. Results: The results obtained from the proposed algorithm provide 100% overlap with the physician’s finding on the EEG data. Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

A Web-based decision support system for sustainable urban planning and management

Urban and territorial planning processes are characterized by a high level of complexity that makes tools management dif?cult. This research presents a Web-GIS prototype for supporting urban planning and management decision-making processes towards social, economic and environmental sustainability. It is designed according to open-source technology and aims to de?ne new planning top-down and bottom-up approaches. The design criteria of this innovative platform respond to the essential principles of transferability, modularity, scalability, and data interoperability. This new tool is applied to a case study to demonstrate how it can support planners and local authorities in urban planning and management processes.

Integrating dynamic modeling and decision support systems for sustainable urban planning in a changing climate

ABSTRACT The study introduces the Ant Lion Recurrent Climate Estimation (ALRCE) model for precise climate forecasting, emphasizing critical parameters like rainfall, humidity, temperature, wind speed, and dew point. Its primary goal is to mitigate natural hazard risks stemming from sudden climate shifts. Data from Surat city, Gujarat, including these features, is meticulously processed to train the ALRCE model. It employs an ant lion fitness function for analysis, particularly predicting rainfall and drought rates. Implemented in MATLAB, it demonstrates superior precision, accuracy, and risk reduction, validated for future climatic predictions from 2025 to 2050. Comparative analysis with existing techniques shows ALRCE outperforms, achieving 96.5% accuracy, 98% precision, 0.18838 RMSE, and 0.63 correlation coefficient. The study enhances accuracy by 2% post-optimization, highlighting ALRCE’s potential to significantly improve climate prediction and mitigate risks associated with abrupt climate changes.

The Logistics of Volkswagen Development Center Applies Operations Research to Optimize Transshipments

Volkswagen Technical Development (TE) is responsible for all prototype development and prototype production for the Volkswagen brand and has its own logistics department (TE-Logistics). In the logistics of prototype parts in the automotive industry, new versions of prototype parts (henceforth referred to as updating parts) are repeatedly assembled in finished prototype vehicles. These updating parts are stored in warehouses and provided to an assembly site to ensure a timely assembly of the associated prototype vehicles. As the internal warehouse on the company site is not large enough for the high variety of parts, an additional external warehouse in the logistics network is needed. However, since prototype parts are unique, the allocation of the parts in suitable warehouses is particularly important. Currently, the various warehouses and the short-term demands repeatedly lead to reactive transshipments between the warehouses. To this end, we developed an approach for proactive transshipments based on a machine learning forecast and a mixed-integer linear programming model for planning proactive transshipments of parts between the warehouses to minimize transport costs. The model is based on a probability estimation of future demands to anticipate the expected optimal warehouse. After the model had revealed high improvement potential through a case study with real-world data in terms of costs and availability time compared to the current reactive process, we derived decision rules and developed a rule-based heuristic algorithm that leads to the optimal solution for the industrial use case. We implemented the heuristic with a spreadsheet-based decision support system (DSS) for daily transshipment planning. After successful test implementation, TE-Logistics estimated the annual cost savings for transport to be approximately 10%.

Implementation of Decision Support System for Ecological Environment Planning of Urban Green Space

Implementation of Decision Support System for Ecological Environment Planning of Urban Green Space

Agent-based decision-support model for bus route redesign in networks of small cities and towns: case study of Agder, Norway

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns.

The Economic Impact of MIS Integration in Operations and Customer Relationship Management

This paper examines the strategic integration of Management Information Systems (MIS) within Operations Management (OM) and Customer Relationship Management (CRM) frameworks, emphasizing its pivotal role in bolstering economic efficiency. Through a synthesis of economic theories and MIS capabilities, the study delineates how this integration fosters improved operational processes, strategic decision-making, and customer engagement, thereby driving organizational economic benefits. Spanning process optimization, decision support systems, quality management, and customer relationship strategies, the analysis showcases the multifaceted economic impacts of MIS integration, including cost reduction, revenue enhancement, and customer satisfaction. By leveraging cutting-edge MIS technologies, organizations can harness data analytics to optimize processes, leverage decision support systems for strategic planning, implement quality management systems to uphold service standards, and personalize customer engagement to enhance satisfaction and loyalty. This study underscores the transformative potential of MIS in redefining operational efficiency and customer relationship strategies, providing valuable insights for businesses seeking to leverage technology for economic gain in the competitive market landscape.

A decision support system for localized planning of reclaimed water around wastewater treatment plants.

Exploding population, industrialization, and an increase in water pollution has led to acute shrinkage in freshwater availability. Numerous countries have started exploring municipal wastewater as a new potential source of water to bring a paradigm shift from linearity to obtaining circularity in human water cycle management. This study aims to develop a decision support system for integrated water and wastewater management (DSS_IWWM), targeted towards reuse-focused selection of appropriate wastewater treatment technology, and localized planning around STPs in terms of reclaimed water demand identification, estimation, allocation, and sustainable pricing. The developed DSS_IWWM comprises of a repository of fourteen reuse purposes, reuse quality criteria, and 25 wastewater treatment technologies (WWTTs) in 360 combinations. It is sensitive to local resource scenarios and applies a socioeconomic and technology-focused methodology for addressing the interests of the community and investing agencies and viably. To validate the application of the DSS_IWWM, it is first tested with data from three cities in the state of Uttar Pradesh (India)-Lucknow, Prayagraj, and Agra-and then extended to nine more Indian cities with varying influent quality characteristics, resource inputs, existing STP technologies, and same target quality and decision criteria prioritization, to present a comparison of appropriate WWTTs and associated average prices obtained in different scenarios. It is concluded that influent quality, existing technology, and target quality criteria play significant role in selection of appropriate WWTTs. The traditional technologies such as UASB and ASP are required to be augmented and supplemented with high-performing WWTTs, such as BIOFOR-F with (C + F + RSF) and SBT + WP to obtain desired effluent quality. High-performing advanced oxidation process (AOP)-based systems such as A2O, SBR, and BIOFOR-F require WWTTs with relatively lower average costs (such as SBT and OP). The developed DSS_IWWM may prove to be very useful and beneficial for policymakers, government officials, engineers, and scientific community as it will facilitate rational decision-making for efficient investment planning in reuse focused wastewater treatment towards achieving circular economy in sustainable water resource management.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning

BackgroundDento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations.MethodsPatients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.ResultsThe binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups.ConclusionsMachine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Research on the construction path of large-scale industrial agglomeration circle in Foshan based on computer big data fusion

This paper innovatively proposes the goal of research on the decision support system of the large-scale industrial agglomeration circle in Foshan. Based on the computer big data combined with the process of urban planning and management in Foshan, this paper proposes the construction content of the current planning decision support system for the new industrial agglomeration circle in Foshan. This paper proposes the relevant models for the analysis and construction of the development and evolution model and the construction of the auxiliary decision-making management system. The method of large-scale industrial agglomeration area in Foshan proposed in this paper provides services for the government to make rational and scientific decision-making. The computer big data industry circle planning model proposed in this paper has certain practical significance.

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity.

An accurate identification and localization of vertebrae in X-ray images can assist doctors in measuring Cobb angles for treating patients with adolescent idiopathic scoliosis. It is useful for clinical decision support systems for diagnosis, surgery planning, and spinal health analysis. Currently, publicly available annotated datasets on spinal vertebrae are small, making deep-learning-based detection methods that are highly data-dependent less accurate. In this article, we propose an algorithm based on convolutional neural networks that can be trained to detect vertebrae from a small set of images. This method can display critical information on a patient's spine, display vertebrae and their labels on the thoracic and lumbar, calculate the Cobb angle, and evaluate the severity of spinal deformities. The proposed achieved an average accuracy of 0.958 and 0.962 for classifying spinal deformities (i.e., C-shaped, S-shaped type 1, and S-shaped type 2) and severity of Cobb angle (i.e., normal, mild, moderate, and severe), respectively. The Cobb angle measurement had a median difference of less than 5° from the ground-truth with SMAPE of 5.27% and an error on landmark detection of 19.73. In addition, Lenke classification is used to analyze spinal deformities as types A, B, and C, which have an average accuracy of 0.924. Physicians can use the proposed system in clinical practice by providing X-ray images via the user interface.

Mapping cultivation potential index of rainfed wheat from climatic perspective using Shannon entropy catastrophe scheme

Mapping the Cultivation Potential Index (CPI) for a specific crop is a topical research activity in agro-climate studies, which can lead to environmental and water resources protection and agriculture efficiency increase. This study formulates a framework to delineate the CPI of rainfed wheat from the climatic perspective of precipitation and temperature. Four precipitation layers were prepared using the frequency analysis of annual, autumn, spring, and June precipitation time series; also, three temperature layers comprise autumn, spring, and June temperatures obtained by remote sensing techniques. The Shannon-Entropy Catastrophe Scheme (ECS) was also formulated to combine seven layers of precipitation and temperature besides the land use layer while decreasing the inherent subjectivity. The formulated methodology was implemented in the Maragheh city, northwest of Iran. Some contrasts exist between the spatial distributions of precipitation in different time scales, which necessitates the formulation of ECS as a multi-criteria decision-making technique. ECS identifies the eastern half of the study area as the medium to relatively high CPI, which can be employed as a decision support system for planning and managing agriculture.

Decision support system for cost planning and implementation of production tasks of LW “Bogdanka” S.A. – the concept of integrating IT solutions towards the idea of an intelligent mine

The article presents the concept and specific activities undertaken by LW “Bogdanka” S.A. to build an IT system that integrates the current dispatching solutions of the mine with IT systems supporting the management of basic underground infrastructure facilities. This concept forms the substantive foundation of a solution that supports the decision-making system for conducting production activities using IT solutions and production monitoring, implemented under the slogan “Intelligent Mine”. The Intelligent Mine, as understood by LW “Bogdanka” S.A., involves a series of innovative technical solutions aimed at increasing mining effectiveness while ensuring the safety of underground workers and minimizing the environmental impact. The article addresses several key issues, including the practical experience of LW “Bogdanka” S.A. in innovative deposit management, scheduling access, preparatory and exploitation works, production resources management, and monitoring of production processes. Additionally, the article explores how these integrated IT systems contribute to improving operational efficiency, enhancing safety protocols, and promoting sustainable mining practices. By providing a comprehensive overview of these initiatives, the article highlights the significant advancements and practical implementations that LW “Bogdanka” S.A. has achieved in its journey toward becoming an Intelligent Mine.

AI-Driven Decision Support System for Green and Sustainable Urban Planning in Smart Cities

Abstract This study focuses on innovative practices in sustainable urban planning, demonstrating significant advancements in key areas such as solar panel efficiency, rainwater harvesting capacity, community garden space, and bike-sharing station accessibility through in-depth experimentation and analysis. The research results show that the energy conversion rate of solar panels reached 25%, surpassing the market standard of 24%, which is crucial for enhancing self-sufficiency in energy in urban areas. The rainwater harvesting system performed well, achieving a capacity of 600 liters per square meter, slightly below the market rate of 650 liters, but still demonstrating significant potential in dense urban environments. Additionally, our project provided 3 square meters of community garden space per resident, exceeding the market average, effectively promoting urban greening and improving residents’ quality of life. In terms of transportation, our experimental model featured 1.5 bike-sharing stations per 1000 residents, better than the market data of 1.2 stations, contributing to the development of sustainable urban transportation. These outcomes not only showcase the potential of sustainable urban planning but also provide practical references and guidance for future urban development.

A probabilistic approach to monitoring the condition of complex technical equipment

A probabilistic cascade-hierarchical approach is proposed for monitoring the condition of complex technical equipment in production, taking into account the types of controlled catches (software, hardware and mechanical) and types of control: technical means and the appropriate level of qualification of personnel based on polymodeling. In contrast to the deterministic control process, when sequential tests are carried out until a certain outcome is obtained, the proposed approach made it possible to form non-stationary rules for estimating the probabilities of process outcomes dynamically, relative to changing test conditions and the environment. The use of the KullbackLeibler measure to assess the divergence of the probability distribution functions of defects in complex technical equipment, having high sensitivity, allowed us to form a scheme of a two-circuit decision support system for dynamic test planning of complex technical equipment, which ultimately increased the degree of validity of the decisions made.

Decision Support System for Strategic Planning in Educational Organization: A Survey

Strategic planning models and the provision of information for decision making in complex strategic circumstances are research topics of great interest. This research addresses the issue of supporting strategic planning decision-making in educational foundations by building a Decision Support System (DSS) to be used by decision-makers in carrying out their strategic planning process, the DSS is integrated in the entire organisation's information system database. This research provides an overview of DSS, the college planning process, the development of DSS through the use of artificial intelligence, and a framework for planning activities at different levels of the organisation to develop strategic plans. Based on the strategic planning process model, a DSS framework is proposed, along with decision support approaches for various DSS modules. DSS provides the best support (at individual, group, and organisational levels) for all stages of strategic planning decision-making. By applying DSS, it is possible to create more perfect circumstances to achieve sustainable future-orientated institutional goals.

A decision support system for autonomous ship trajectory planning

This study presents an approach to enhance human–machine collaboration in autonomous ship trajectory planning. A decision support system is developed, considering crucial environmental factors such as ocean currents, wind, and tidal information, alongside the integration of narrow channel geometry, squat effect, and Under-keel-clearance (UKC). The Dynamic Consequence Analysis (DCA) risk assessment method is utilized to establish dynamic safety domains, incorporating ship maneuvering characteristics and potential failure scenarios. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is then employed to generate alternative trajectories, optimizing five objective functions: minimizing safety domain violation, consecutive speed changes, path length, deviation from the initial plan, and deviation from the initial estimated time of arrival. Furthermore, two Multi-criteria Decision Making (MCDM) methods, Multi-Objective Optimization by Ratio Analysis (MOORA) with user preferences, and the Entropy Weight Method (EWM) with automatic weight allocation, are employed to rank alternative solutions from the Pareto front. Finally, a clustering method is employed on the Pareto front solutions. The outcomes that serve as representatives for these clusters are then merged with the highest-rated alternative solutions from MCDM methods. This combined set forms the basis for the final decision-making process carried out by the operator. While dynamic obstacles are not considered in this study, evaluation across three scenarios demonstrates the effectiveness of the proposed decision support system for trajectory planning.

The Suitable Location for a Hybrid Renewable Energy Wind-Solar Power Plant: A Review by Bibliometric

This bibliometric analysis aims to provide an overview of the research landscape related to the suitable location for a hybrid renewable energy wind-solar power plant by using AHP and GIS. The study used the Scopus database to identify relevant articles and analyzed them based on research trends, themes, key authors, journals, and countries. The results showed a growing interest in the topic, particularly in recent years, with an increasing number of publications. The most used research methods were AHP and GIS, and the most frequently addressed themes were site selection, renewable energy potential assessment, and optimization of hybrid systems. Key authors and institutions from Asia and China dominated the research landscape. The study suggests the need for more comprehensive and standardized approaches to site selection and renewable energy potential assessment, as well as the importance of considering local contexts and stakeholder engagement in decision-making processes. Future research directions could explore integrating other renewable energy sources and developing decision support systems for hybrid renewable energy planning.

Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region’s number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDA-ML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Paraná, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach.