This study delves into the intricate relationships among returns of diverse indices within the Tehran stock market, employing both Pearson and partial correlation coefficients as analytical tools. Utilizing monthly data from fourteen capital market indices, the investigation applies the k-means method for clustering based on four critical attributes: risk, efficiency, average industry index, and the number of companies within each industry. The findings reveal that when the total index is considered as a controlling variable, the partial correlation analysis yields distinct insights into the interconnections among market indices, thereby highlighting the significant influence of the total index on these relationships. Moreover, the clustering analysis categorizes the indices into three distinct groups: the first cluster exclusively comprises the total index; the second cluster includes indices from the automobile, pharmaceutical, metal, cement, chemical, and food sectors; whereas the remaining indices are allocated to the third cluster. This multifaceted approach not only elucidates the dynamic interplay between different stock market indices but also underscores the variability in their interrelations when viewed through the lens of a controlled variable. The study's methodological rigor and its innovative use of multiscale partial correlation analysis contribute to a deeper understanding of the factors shaping the Tehran stock market's behavior, offering valuable insights for investors, policymakers, and scholars alike.
This investigation explores the scheduling of $n$ jobs on a single machine, where each job possesses a common due date, and processing time is characterized by pentagonal fuzzy numbers (PFNs). The primary objective is to minimize the aggregate of inventory holding and penalty costs, addressing the critical impact of earliness and tardiness on profitability. It is identified that earliness leads to increased inventory carrying costs and potential degradation in product quality, whereas tardiness undermines customer goodwill and inflicts reputational damage through delayed payments. Consequently, the scheduling dilemma that seeks to minimize the combined penalties of earliness and tardiness, whilst adhering to a common due date on a single machine, emerges as a pivotal and challenging endeavor in optimizing goods delivery within production settings. Recognized as a non-deterministic polynomial-time hardness (NP-hard) problem, this task underscores the complexity and competitive nature inherent in manufacturing operations. To navigate the uncertainties embedded in this problem, a fuzzy logic approach, augmented by a heuristic algorithm, is employed. Through this methodology, the problem is addressed in a manner that encapsulates the vagueness and imprecision inherent in processing time, thereby facilitating more resilient and adaptable scheduling decisions. The efficacy of this approach is demonstrated via a computational example, underscoring its potential to enhance decision-making in the realm of job scheduling.
The Spherical fuzzy rough set (SFRS), which is based on approximations and is handled in this work, is a key idea for handling uncertainty when data is taken from real-world situations. The most adaptable operational laws based on the parameter for fuzzy frameworks are the Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN), which are crucial for data interpolation. In this paper, operators based on AATN and AATCN are developed: spherical fuzzy rough Aczel-Alsina weighted geometric (SFRAAWG), spherical fuzzy rough Aczel-Alsina ordered weighted geometric (SFRAAOWG), and spherical fuzzy rough Aczel-Alsina hybrid weighted geometric (SFRAAHWG). A few fundamental properties of the generated SFRAAWG, SFRAAOWG, and SFRAAHWG operators are defined and given examples. The multi-criteria decision-making (MADM) problem is applied to the developed SFRAAWG operator. Additionally, the sensitivity of the SFRAAG operator is examined. The developed AOs are compared to a few pre-existing AOs and their significance is evaluated.
In the realm of general insurance in India, an econometric investigation was conducted to estimate the revenue efficiency across a selection of 15 prominent, diversified general insurance entities for the fiscal years 2011-12 to 2016-17. Utilizing a semi-parametric methodology, the revenue frontier was constructed under the GAM framework, while the variance components were estimated employing the method of moments. This analysis further explored the influence of revenue efficiency on critical profitability metrics, namely return on equity (ROE) and return on assets (ROA), through the application of instrumental variable regression. The findings provide pivotal insights into the dynamics of revenue efficiency and its consequential impact on the financial performance of general insurance companies in India, offering a substantial contribution to the literature on insurance economics and the methodology of efficiency measurement. The research underscores the significance of adopting semi-parametric models for a nuanced understanding of revenue efficiency, thus paving the way for enhanced strategic decision-making in the insurance sector.
In the realm of cybersecurity, the formulation of comprehensive strategies is imperative for multinational corporations to protect against pervasive cyber threats. Recent developments in the field of intuitionistic multi-fuzzy sets (IMFSs) have heralded q-rung orthopair multi-fuzzy sets (MFSs) as a pivotal tool for encapsulating ambiguity and uncertainty within complex scenarios. The essence of this study lies in the introduction of two innovative distance measures tailored for q-rung orthopair MFSs (q-ROM$^{k}$FSs) of dimension k, enhancing the capacity to delineate distinctions between such sets effectively. Employing score functions pertinent to q-ROM$^{k}$FSs, this research extends its application to the sphere of Multi-Attribute Decision Making (MADM), presenting a methodological advancement in decision-making processes. The efficacy of the proposed measures is elucidated through a comparative analysis with existing methodologies in MADM, thereby underscoring the superiority of the introduced approach. This investigation not only contributes to the enrichment of the theoretical underpinnings of q-ROMFSs but also propels their practical application in cybersecurity strategy formulation for multinational entities. The study employs the Euclidean and Hamming distance measures as benchmarks, supplemented by the development of a score and accuracy function, to furnish a comprehensive tool for addressing cybersecurity challenges.
An innovative framework is introduced for the enhancement of efficiency within emergency departments (EDs), utilizing an integration of simulation and fuzzy Multi-Criteria Decision-Making (MCDM). A discrete event simulation (DES) model was developed, capturing the intricate dynamics characteristic of ED operations with high fidelity. This model's integration with the Analytic Hierarchy Process (AHP) and the Elimination and Choice Expressing Reality (ELECTRE) method, within a fuzzy context, facilitated a critical evaluation and optimization of the decision-making processes inherent in EDs. The incorporation of these methodologies yielded significant improvements in patient flow and service quality, highlighting the substantial potential of marrying simulation with fuzzy MCDM to achieve operational excellence in healthcare settings. The study stands as a contribution to the enhancement of ED operations, offering a versatile methodology with potential for adaptation across diverse healthcare environments. This approach underscores the imperative of employing a nuanced, integrated strategy to navigate the complexities of healthcare service delivery, ensuring an equilibrium between operational efficiency and the quality of patient care.
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