Search Results (957)

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

Background: Evaluating centralized purchasing performance is a complex multi-criteria decision-making problem involving uncertainty, linguistic assessments, and subjective judgments from internal clients. Existing approaches provide limited support for handling these characteristics simultaneously. Methods: This study proposes a Mamdani fuzzy inference model integrating four criteria: Service Quality, Responsiveness, Compliance, and Collaboration. The fuzzy rule base was developed using expert knowledge and organizational evaluation practices. The model was applied to a real industrial case study based on an annual evaluation conducted collaboratively by four internal evaluators. Results: The model transformed qualitative assessments into an interpretable performance score while capturing interactions among evaluation criteria and handling uncertainty in the evaluation process. Conclusions: The proposed approach provides a structured decision-support framework for evaluating centralized purchasing performance. It enables the integration of linguistic assessments and expert knowledge, offering a flexible and coherent evaluation tool for industrial environments. Full article

Higher construction costs (CCs) linked to carbon reduction methods have hindered the adoption of low-carbon approaches in the built environment. The simultaneous minimisation of upfront embodied carbon (EC) and CCs has not received much attention in building design optimisation (BDO) research; most studies prioritise operational energy, operational carbon, and operational cost reduction. This paper develops an integrated conceptual framework for low-carbon, cost-effective BDO, particularly targeting upfront EC and CCs, to fill this research gap and meet industry demands. A systematic literature review was conducted following PRISMA guidelines, synthesising 41 peer-reviewed articles published between 2015 and 2026. Thematic and content analyses were employed to extract and categorise key methodological components, including optimisation problem characterisation, objective-driven design variable selection, constraint modelling, algorithm selection, and evaluation and validation approaches. Subsequently, the developed conceptual framework was validated through semi-structured expert interviews with participants comprising BDO researchers and building designers in the construction field. A cross-mapping of optimisation objectives, optimised parameters, and design variables was developed to clarify their interrelationships, alongside structured criteria for optimisation algorithm selection. Based on these insights, a conceptual framework named “ICCO-BD (Integrated Upfront Carbon and Construction Cost Optimisation for Building Design) framework” is proposed and validated, integrating problem formulation, parametric modelling, multi-objective optimisation, and systematic Pareto-based evaluation into a coherent end-to-end workflow, enabling improved time efficiency through reduced redesign iterations, enhanced solution quality via better pareto front exploration, and more robust decision-making through clearer trade-off interpretation. While expert feedback indicated strong conceptual relevance and practical applicability, the framework remains conceptual in nature and requires further empirical verification through real-world case studies and optimisation applications before broader industry implementation. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

This paper proposes a mathematical co-design framework for synchronous Boost DC-DC converters and their PI voltage controllers. In contrast to the conventional sequential design approach, where the power stage is sized first and the controller is tuned afterward, the proposed method treats the converter and the controller as a single coupled design problem. A nonlinear averaged model of the synchronous boost converter operating in continuous conduction mode is considered, explicitly incorporating the inductor series resistance, the capacitor equivalent series resistance, and the on-state resistances of the active switches. In addition, a simplified but physically interpretable loss model is included in order to capture inductor copper loss, capacitor ESR loss, semiconductor conduction loss, and switching loss. Based on this formulation, the joint design of the power stage and the PI controller is cast as a constrained multi-objective optimization problem whose decision variables include the inductance, capacitance, switching frequency, and controller gains. The optimization criteria account for output-voltage ripple, settling time, total losses, and current stress, while practical constraints related to duty cycle, current limits, ripple bounds, and closed-loop feasibility are enforced. The proposed framework makes it possible to compute Pareto-efficient designs and to reveal trade-offs that remain hidden under classical decoupled design procedures. Numerical case studies are structured to compare the proposed co-design strategy with a conventional sequential-design baseline. An optional technology-aware extension is also considered, allowing the influence of different semiconductor classes, such as Si, SiC, and GaN, to be assessed through technology-dependent loss and switching-frequency assumptions. The results indicate that the proposed framework provides a mathematically grounded and practically useful basis for integrated converter–controller synthesis in nonideal power electronic systems. Full article

Hybrid Evolutionary–Fuzzy Systems (HEFS) have emerged as a powerful computational paradigm for addressing complex engineering optimization and intelligent decision-making problems under uncertainty. This study presents a systematic review, conducted following the PRISMA 2020 methodology, to analyze advancements in the integration of evolutionary algorithms, swarm intelligence, fuzzy logic, and Multi-Criteria Decision-Making (MCDM) techniques over the period 2020–2026. The analysis focuses on identifying key algorithmic mechanisms, hybridization strategies, performance metrics, and application domains. The results indicate that HEFSs significantly enhance optimization performance by balancing exploration and exploitation, improving robustness, and enabling adaptive and interpretable decision-making in uncertain and multi-objective environments. In particular, fuzzy systems contribute to effective uncertainty modeling and interpretability, while evolutionary and metaheuristic algorithms provide strong global search capabilities. Despite these advantages, important challenges remain, including high computational complexity, scalability limitations, and the trade-off between accuracy and interpretability. The review also identifies emerging research directions involving Explainable Artificial Intelligence (XAI), deep learning integration, digital twins, and big-data-enabled optimization. However, the reviewed evidence suggests that these technologies should currently be interpreted as promising but still evolving extensions, whose maturity and large-scale validation remain heterogeneous across application domains. Full article

The present article discusses the application of genetic algorithms (GA) for solving multi-criteria optimization (MCO) problems in underground mining. It has been demonstrated that GAs are highly effective in identifying Pareto-optimal solutions in scenarios involving multiple conflicting criteria, specifically the simultaneous minimization of equipment failure rate, energy consumption, and repair costs. The article presents the main approaches to solving MCO problems, a brief overview of the most popular algorithms, such as NSGA-II and SPEA2, and their improved versions. The proposed algorithm, implemented in Python 3.11 using the DEAP library, incorporates adaptive crossover, enhanced diversity preservation, and problem-specific initialization. Quantitative analysis shows that the proposed algorithm achieves a Hypervolume Indicator of 0.796, representing a 7.2% improvement over standard SPEA2, with an 18.3% reduction in Inverted Generational Distance (IGD), indicating superior convergence to the true Pareto front. The algorithm identifies optimal trade-offs between conflicting objectives—for example, a 15% reduction in energy consumption correlates with a 10% increase in failure rate—providing decision-makers with quantified insights for operational planning. The novel idea is the use of an adaptive crossover strategy, a composite diversity maintenance technique, and application-specific initialization—all of which have not been used before for optimizing underground mining machinery. A visual analysis of the results, employing a graphical representation of the Pareto front, confirmed that the proposed approach enables experts to make informed decisions based on production priorities. Full article

Selecting the right energy storage system (ESS) for grid integration is a high-stakes decision involving conflicting technical, economic, environmental, and risk criteria under deep uncertainty. The existing fuzzy multi-criteria decision-making (MCDM) methods either fail to capture neutral or abstaining expert judgments or treat evaluation criteria as independent, which is an unrealistic assumption in complex engineering decisions. To address both limitations simultaneously, this study develops four new aggregation operators by extending the Bonferroni mean (BM) into the q-rung picture fuzzy sets (q-RPFSs) framework: the q-RPFBM-based, q-RPFWBM-based, q-RPFGBM-based, and q-RPFWGBM-based operators. Unlike the existing q-RPFS operator families (Dombi, Frank, Fermatean, Yager, Maclaurin), which aggregate criteria independently, BM-based operators explicitly model pairwise interactions among criteria with a structurally distinct aggregation logic that is especially critical when criteria such as cost, risk, reliability, and environmental impact are mutually correlated. The theoretical validity of the operators is confirmed through proofs of idempotency, monotonicity, and boundedness. Applied to a comprehensive ESS selection problem for Türkiye (covering nine alternatives across nineteen sub-criteria and five main criteria, including an explicit risk dimension), the framework consistently identifies pumped hydro storage as the optimal choice. Sensitivity analyses under varying q, s, and t parameters, as well as perturbed criterion weights, confirm the robustness of this ranking. The proposed framework offers energy planners and decision-makers a principled and transparent tool for evaluating ESS under high uncertainty and criterion interdependence. Full article

This paper proposes a multi-criteria decision making (MCDM) model, namely, Weighted Product Evaluation Based on Distance from Average Solution (WPEDAS), to evaluate the financial performance of companies. The proposed WPEDAS model focuses on the weighted product approach, which is different from the existing Evaluation Based on Distance from Average Solution (EDAS) model that adopts a weighted sum approach based on distance from average solution. Besides that, we further enhance the model performance by developing a hybrid MCDM model. The proposed hybrid Weighted Aggregated Sum Product Evaluation Based on Distance from Average Solution (WASPEDAS) model is developed based on the weighted sum EDAS and the proposed WPEDAS. The proposed hybrid WASPEDAS model offers higher flexibility and robustness of customizing decision strategies based on the decision makers in solving MCDM problems. The proposed hybrid model is demonstrated using the financial ratios of companies in the Consumer Discretionary sector in the NASDAQ Exchange. The entropy weight method is integrated into the proposed models to determine the weights of decision criteria. Based on the results of sensitivity analyses, the proposed hybrid WASPEDAS model proves its reliability and robustness in performance evaluation. This implies that the proposed hybrid WASPEDAS model offers greater stability in ranking the companies, thus helping investors and fund managers in analyzing the companies during investment decision making. In addition, this study also provides guidance to the companies’ management teams in their strategic and tactical decision making to reduce volatility in driving the companies towards excellence. Full article

The increasing use of wireless personal networks in educational institutions has created significant challenges in ensuring network security and the reliable testing of communication infrastructure. The selection of appropriate software tools for network security testing is a complex decision-making problem due to multiple software quality criteria and operational requirements. This paper proposes a multi-criteria model for evaluating approaches to wireless personal network security testing in educational institutions through the analysis of representative software tools. The evaluation framework is based on the ISO/IEC 25010 software quality criteria: reliability, functional suitability, interoperability, performance efficiency and scalability, compatibility and maintainability. Five widely used tools (Nmap, OpenVAS, Nessus, Wireshark and Wazuh) were analyzed using a structured multi-criteria approach. Criteria weights were determined using the PIPRECIA-S method, while the ranking was verified using the TOPSIS method. The results show that Wazuh achieved the highest overall score (0.3051), followed by Wireshark (0.2315) and Nessus (0.1954), while OpenVAS (0.1443) and Nmap (0.1225) achieved lower ranks. The stability and reliability of the model were confirmed by sensitivity analysis, Pareto analysis, Spearman’s rank correlation and scenario analysis. The model provides a reliable decision-support framework for selecting network security testing approaches in educational and similar organizational environments. Full article

One area of application for distributed ledger technologies is the Internet of Things. These technologies can provide an effective solution to many problems in this field. The consensus layer is a crucial architectural component of distributed ledger systems. Modern IoT networks place increased demands on the consensus mechanisms used in blockchain systems. There are many consensus protocols with different properties and purposes, including those for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis methods are widely used in such problems, as they allow for the consideration of multiple conflicting criteria and provide a balanced approach to evaluating alternatives. Given the variability of network parameters and requirements of consensus mechanisms, multi-criteria decision-making methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based Internet of Things networks. The system is an implementation of a previously developed conceptual model for a consensus protocol selection framework. A case study is also provided to demonstrate the application of the system. Full article

This paper proposes a Multi-strategy Improved Love Evolution Algorithm, named MSILEA, to overcome the limitations of the original Love Evolution Algorithm (LEA) in complex optimization tasks. Although LEA has a distinctive stimulus–value–role interaction mechanism, its linear search-radius control, distance-dominated behavioral decision rule, and weak directional learning in the value phase make it prone to insufficient exploitation, ineffective behavioral switching, and local optimum trapping on rotated, hybrid, and composition functions. To address these issues, MSILEA introduces three complementary strategies: a nonlinear two-stage search radius regulation strategy, a quality–distance joint decision strategy, and a winner-direction differential learning strategy. These strategies respectively improve stage-dependent search control, multi-criteria behavioral selection, and directional learning ability. From the perspective of the symmetry concept, the proposed MSILEA can be regarded as an optimization framework that dynamically regulates the symmetry and asymmetry of population interactions. The encounter and role mechanisms preserve paired interaction symmetry among candidate solutions, whereas the quality–distance joint decision and winner-direction differential learning strategies introduce controlled symmetry breaking to guide the population toward higher-quality regions of the search space. MSILEA is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with nine representative classical and advanced metaheuristic algorithms. On the 30-dimensional CEC2017 suite, MSILEA achieves the best Friedman mean rank of 1.93, outperforming the original LEA with a mean rank of 4.60. On the CEC2022 suite, MSILEA also obtains the best mean ranks of 2.50 and 2.00 in the 10-dimensional and 20-dimensional cases, respectively. In the microgrid day-ahead optimal scheduling problem, MSILEA obtains the lowest mean operating cost of 1.23 × 10⁶ CNY and reduces the cost by approximately 50.80% compared with LEA. The average CPU time of MSILEA is 18.47 s, which is comparable to LEA and lower than several improved competitors. These results indicate that MSILEA can improve optimization accuracy, convergence robustness, and engineering feasibility without increasing the theoretical computational complexity. Full article

The paper presents the results of experimental studies aimed at assessing thermal conductivity, compressive strength, water absorption and capillary action of samples in the form of ordinary concrete (reference sample—B1) and lightweight concrete with the addition of a biocomponent (C100) in the range of 3–31.2% porosity with varied morphology. Gas permeability studies were conducted for porous materials with an anisotropic structure. The measurement results indicate a significant effect of the type of material on thermal conductivity for B1, which is 3.05 W·(m·K)⁻¹ and C100 equal to 0.09 W·(m·K)⁻¹. On the other hand, the highest water absorption is demonstrated by C100, which is 99%, and the lowest by B1 equal to 2%. Tests were conducted for different gas permeability conditions using oxygen (O₂), nitrogen (N₂) and carbon dioxide (CO₂). The basis for assessing gas permeability through porous beds is the gas flow resulting from the overpressure forcing this flow. The highest gas permeability coefficient at a flow resistance of 6 kPa for B1 was 2.7·10⁻⁷ m², and for C100, 2.1·10⁻⁷ m² at CO₂ flow. The following issues were identified: scientific, identifying the lack of research on gas permeability testing for anisotropic concrete structures; application, identifying reports of premature failure of concrete structures in livestock buildings due to the effects of toxic substances. The novelty in the article is the indication of the gas permeability model and the performance of a comparative analysis (multi-criteria analysis) based on diagnostic features. In the hierarchical decision-making structure, gas permeability was used as one of the evaluation criteria, which can be assessed as a stimulant or destimulant depending on the climatic zone. The permeability of gas media is one of the features that allow for assessing the suitability of materials, among others, for small-sized prefabricated wall systems—the durability of both the element itself and any reinforcing inserts depends on permeability. The aim of this article was to compare the physical and functional properties of materials, such as thermal conductivity, water absorption, capillarity and gas permeability, in relation to the material composition. The research was of an application and engineering nature, focusing on macroscale functional parameters that are important from the point of view of the practical application of the tested building composites. The scientific problem is to indicate the lack of scientific research on the study of gas permeability in anisotropic concrete structures in livestock building conditions. The engineering use of hempcrete indicates its usefulness in various structural elements of livestock buildings. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI–World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002–2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI–World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen’s d = 1.77–5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen’s d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI–World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches. Full article