Effective business system management necessitates strategic planning, efficient resource monitoring, and consistent team coordination. In practice, decision-making (DM) processes are frequently challenged by uncertainty, imprecision, and the need to aggregate diverse information sources. To address these complexities, a confidence-based algebraic aggregation framework incorporating the $p, q, r$-Fraction Fuzzy model has been proposed to enhance decision accuracy under uncertain environments. Within this framework, four novel aggregation operators are introduced: the Confidence $p, q, r$-Fraction Fuzzy Weighted Averaging Aggregation ($Cpqr$-FFWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Averaging Aggregation ($Cpqr$-FFOWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Weighted Geometric Aggregation ($Cpqr$-FFWGA) operator, and the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Geometric Aggregation ($Cpqr$-FFOWGA) operator. These operators are designed to capture the inherent vagueness and subjectivity in business-related decision inputs, thereby facilitating robust assessments. The theoretical properties of the proposed operators—such as idempotency, boundedness, and monotonicity—are rigorously analyzed to ensure mathematical soundness and operational reliability. To illustrate the practical applicability of the model, a detailed case study is provided, demonstrating its effectiveness in maintaining resource sufficiency, preventing financial disruptions, and ensuring organizational coherence. The use of these aggregation mechanisms allows for systematic integration of expert confidence levels with varying degrees of fuzzy information, resulting in optimized decisions that are both data-informed and uncertainty-resilient. The methodological contributions are positioned to support real-world business contexts where dynamic inputs, incomplete data, and human judgment intersect. Consequently, the proposed approach offers a substantial advancement in intelligent decision-support systems, providing a scalable and interpretable tool for business performance enhancement.
To address the evolving preferences of residents in smart community development and the uncertainty inherent in expert-driven technology adoption decisions, an integrated Quality Function Deployment (QFD) framework has been proposed. This framework combines Interval Type-2 Fuzzy Sets (IT2 FSs), a modified Kano Model, Regret Theory, and the Grey-Entropy Technique for Order Preference by Similarity to Ideal Solution (GETOPSIS). IT2 FSs were employed to accommodate the semantic ambiguity of user demands, enabling more precise interpretation of linguistic input. A refined Kano classification was used to categorise 15 demand indicators, from which 5 Customer Requirements (CRs) and 10 Design Requirements (DRs) were derived. Regret Theory was incorporated to model behavioural biases commonly observed in expert evaluations, particularly the tendency to avoid perceived short-term losses. Additionally, a dynamic weight adjustment mechanism was introduced based on corporate life cycle theory, revealing strategic divergences between early-stage enterprises, which prioritise basic security infrastructure, and mature firms, which emphasise sustainable, energy-efficient technologies. The GETOPSIS method was further enhanced to improve the robustness of technology prioritisation under uncertainty. The principal contributions of this study are threefold: (1) the provision of a QFD framework capable of modelling high-order uncertainty through linguistic variables, (2) the integration of behavioural decision theory to better reflect real-world expert judgement, and (3) the development of an improved GETOPSIS approach for more reliable multi-criteria decision-making. The proposed framework provides theoretical and methodological foundations for advancing adaptive technology adoption strategies in smart communities and may serve as a decision-support tool for policymakers and developers in rapidly evolving urban environments.
The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions. Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability. To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting. Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data. In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle. To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability. Empirical validation has been conducted using real-time data from the Borsa İstanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models. Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency. The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.
To enhance cost-efficiency and streamline logistics operations in industrial manufacturing, centralised warehouse systems have increasingly been adopted as a strategic alternative to decentralised storage structures. In this study, the storage framework of a lubricating oil production facility has been examined to assess the operational implications of decentralised warehousing currently in use. It has been identified that the existing system incurs excessive operational costs, prolongs handling times, and demands a disproportionately high labour force, thereby constraining the overall efficiency of the supply chain. In response to projected increases in production output, the feasibility of constructing a centralised, gravity-fed warehouse equipped with automated and robotic technologies for the handling of palletised goods has been investigated. This proposed facility would be strategically integrated with the product packaging unit to form a unified logistical hub within the manufacturing site. A comprehensive analysis was conducted to determine the optimal location for the central warehouse, with key criteria including material flow, space availability, connectivity to production lines, and scalability. The results indicate that the implementation of a centralised automated storage and retrieval system (AS/RS) would significantly improve warehouse throughput, reduce operational expenditures, and align closely with long-term production expansion plans. Additionally, the integration of advanced storage technologies is expected to enhance inventory visibility, minimise human error, and support real-time production coordination. It is concluded that the establishment of a central warehouse facility, functioning as a core node in the internal logistics network, is essential for achieving sustainable operational efficiency and future-proofing the lubricating oil manufacturing process.
In response to escalating urban traffic congestion, environmental degradation, and mobility inefficiencies, intelligent transportation systems (ITS) and sustainable mobility strategies have been increasingly recognised as vital components of smart city development. In this study, the city of Trabzon, Türkiye, was examined as a representative urban environment facing such challenges. Six major intersections exhibiting persistent traffic congestion were selected for conversion from conventional fixed-time signal control to adaptive, traffic-actuated signalisation systems. Detailed performance evaluations were conducted, incorporating microsimulation modelling and real-time traffic flow analysis. The implementation of adaptive signalisation was found to significantly reduce vehicular delay, queue lengths, and intersection-level emissions, while enhancing operational efficiency and traffic safety. A complementary analysis assessed the economic and environmental impacts of this intervention, revealing considerable annual savings in fuel consumption and marked reductions in carbon dioxide (CO$_2$) emissions, thereby underscoring the long-term sustainability and cost-effectiveness of the proposed system. In parallel, the integration of electric vehicles (EVs) and micromobility solutions—including electric buses, minibuses, passenger cars, bicycles, and scooters—was proposed to further promote sustainable urban mobility. Strategic placement of EV charging infrastructure was suggested, with spatial planning informed by expected demand distribution and intermodal connectivity. Economic modelling demonstrated a reduction in operational fuel expenditure, while environmental projections indicated a substantial decrease in transport-related greenhouse gas emissions. Furthermore, micromobility modes were proposed as critical for addressing first- and last-mile connectivity gaps, mitigating short-distance vehicular traffic, and alleviating urban parking demand. Policy recommendations emphasised the necessity of strong municipal leadership in facilitating infrastructure deployment, public adoption, and behavioural shifts towards low-emission transport alternatives. The findings position Trabzon as a viable model for medium-sized urban centres seeking to implement scalable and replicable smart mobility frameworks. By integrating adaptive traffic control with zero-emission mobility, this study provides actionable insights into the design of efficient, economically viable, and environmentally sustainable urban transportation ecosystems.
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