Vladimir Simic
7005545253
Publications - 14
Tackling energy poverty with renewable energy Projects: Fuzzy decision support system based on virtual and real experts
Publication Name: Renewable Energy
Publication Date: 2026-01-01
Volume: 256
Issue: Unknown
Page Range: Unknown
Description:
Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.
Open Access: Yes
Driving sustainable hydroelectric investments: Leveraging two-step logarithmic normalization for sustainable investment prioritization
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 2110-2122
Description:
Hydroelectric energy investments involve substantial techno-economic risks that can increase costs and undermine economic sustainability if not properly managed. However, the literature lacks comprehensive studies addressing these risks. This study proposes a novel decision-making model to identify and prioritize strategies for effective risk management in hydroelectric projects. The model integrates z-scoring for expert selection, the Criteria Importance Assessment (CIMAS) method for weighting criteria, and the Alternative Ranking using Two-Step Logarithmic Normalization (ARLON) method for ranking EU-15 countries according to their strategies. Pythagorean fuzzy numbers are incorporated to better handle uncertainty and improve evaluation accuracy. Results indicate that challenges in adopting new technologies and grid integration issues are the most influential risk factors. The findings provide actionable insights for policymakers and investors to enhance the sustainability and efficiency of hydroelectric energy investments. Policymakers should implement targeted incentives and regulatory frameworks to accelerate technology adoption and address grid integration challenges in hydroelectric projects. Strategic planning should prioritize infrastructure modernization, cross-border energy cooperation, and capacity-building programs to enhance sector resilience and investment security.
Open Access: Yes
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-09-01
Volume: 11
Issue: 9
Page Range: Unknown
Description:
Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.
Open Access: Yes
An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (T−SHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel T−SHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.
Open Access: Yes
Prioritization of AI-based material handling approaches for smart logistics in sustainable warehouses: A q-rung orthopair fuzzy CoCoSo methodology with consensus reaching
Publication Name: Environment Development and Sustainability
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study aims to address the artificial intelligence-based material handling approach selection problem under circular economy to contribute the smart and sustainable business management in logistics systems. The "consensus-reaching process" for experts is not emphasized in the current decision-making procedures with q-rung orthopair fuzzy data. Experts working on group decision-making challenges may hold views that are very dissimilar from one another as a result of their knowledge and experiences. In order for experts to increase the amount of consensus, a consensus-building process is needed. Besides, the ranking results provided by "combined compromise for ideal solution" do not change dramatically in line with the changing weight distributions of characteristics. So, q-rung orthopair fuzzy-based combined compromise for ideal solution methodology with consensus reaching is introduced for solving the addressed emerging problem of logistics companies. This robust and logical decision-making method can comprehensively analyze the advantages, disadvantages, and potential barriers to the acceptance of artificial intelligence-based material handling approaches. The real-life study is offered for a logistics company that plans to invest in robotic solutions based on artificial intelligence. The findings show that autonomous mobile robots represent the best artificial intelligence-based material handling approach. Recommendations for adopting alternative solutions are provided to assist in the efficient completion of smart logistics activities.
Open Access: Yes
Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.
Open Access: Yes
Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products
Publication Name: Methodsx
Publication Date: 2024-12-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
In this research is presented a new method for determining the weights of criteria called simple weight calculation (SIWEC) method. The steps of this method are presented in the practical example of determining the importance of criteria for the needs of sales of agricultural products in the Semberija region. During the presentation of this method two methods are elaborated the simple SIWEC method which includes numerical ratings and the fuzzy SIWEC method which includes ratings in the form of linguistic value. In the selected example is presented how to use this method in order to determine the importance of criteria and in both cases the criterion of sales reliability is given the greatest weight. The contribution SIWEC method is reflected in its simplicity, which facilitates decision-making. • The method presented in this research apart from others is that it uses the evaluation of the criteria by decision makers, so the criteria should not be ranked and compared, but simply evaluated. • Unlike similar methods, the presented method uses the adjusted steps of the method for ranking the alternatives, and decision makers are given a different importance in the decision-making.
Open Access: Yes
Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.
Open Access: Yes
Cloud spot instance price forecasting multi-headed models tuned using modified PSO
Publication Name: Journal of King Saud University Science
Publication Date: 2024-12-01
Volume: 36
Issue: 11
Page Range: Unknown
Description:
The increasing dependence and demands on cloud infrastructure have brought to light challenges associated with cloud instance pricing. The often unpredictable nature of demand as well as changing costs of supplying a reliable instance can leave companies struggling to appropriately budget to support a healthy cash flow while maintaining operating costs. This work explores the potential of multi-headed recurrent architectures to forecast cloud instance prices based on historical and instance data. Two architectures are explored, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. A modified optimizer is introduced and tested on a publicly available Amazon elastic compute cloud dataset. The GRU model, enhanced by the proposed modified approach, had the most impressive outcomes with an MAE score of 0.000801. Results have undergone meticulous statistical validation with the best-performing models further analyzed using explainable artificial intelligence techniques to provide further insight into model reasoning and information on feature importance.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
Computational Assessment of Energy Supply Sustainability Using Picture Fuzzy Choquet Integral Decision Support System
Publication Name: Computers Materials and Continua
Publication Date: 2025-01-01
Volume: 85
Issue: 1
Page Range: 1311-1337
Description:
For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM method allow users to express their opinions. To overcome the impact of uncertainty and vagueness of expert opinion, we explore the notion of picture fuzzy theory, which is a more efficient and dominant mathematical model. Recently, the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process. However, Choquet integral operators are more flexible and are used to express correlation among different attributes. This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators. To prove the intensity and validity of invented approaches, some dominant properties and special cases are also discussed. An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria. Additionally, we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria. A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.
Open Access: Yes
Optimizing industrial robot selection using novel trigonometric Pythagorean fuzzy normal aggregation operators
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-10-01
Volume: 11
Issue: 10
Page Range: Unknown
Description:
The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.
Open Access: Yes
Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.
Open Access: Yes
