The Logistics Performance Index (LPI) represents a tool developed by the World Bank that is used to measure the efficiency and effectiveness of a country’s logistics sector, and comprises of six components. This indicator is used to compare the logistics performance of different countries, identify challenges in global supply chains, and help policymakers improve their logistics infrastructure and service quality. Given the importance of this indicator, every country aims to achieve a higher LPI score and, consequently, a more favorable ranking. The objective of this paper is to propose a new methodology for calculating the LPI score for transport routes. To validate the proposed methodology, the study analyzes seven cases involving import and export flows from Serbia. Based on the results, the analysis identifies which transport routes achieve the highest scores and which require specific preventive and corrective actions to improve their performance.
When the word "disaster" is used, it usually refers to both human-caused situations that have a negative impact on the community and its environment as well as natural disasters like hurricanes, earthquakes, floods, and similar phenomena. Good logistics management is crucial to reducing the bad effects of these kinds of circumstances. This typically entails tasks like planning, organizing, acquiring, moving, and other associated duties. The distribution of supplies to impacted individuals in an effort to save lives is the main objective of humanitarian logistics. The location of humanitarian goods and equipment, which are kept in makeshift humanitarian logistics centers, is crucial for ensuring prompt response in such circumstances. As a result, when deciding where to locate these centers, it is crucial to take into account particular local factors. Numerous factors might impact this kind of selection, which is why finding a location for a humanitarian logistics center is considered a multi-criteria challenge. This research suggests using the ADAM (Axial-Distance-Based Aggregated Measurement Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) techniques to solve this kind of issue. An example of their application is provided by a case study that centers on where Serbia's humanitarian logistics hub is located. The creation of a framework and a special set of standards for choosing the locations of humanitarian logistics centers are the main results of this study. This can help decision-makers, authorities, individuals, non-governmental groups, and logistical service providers make well-informed decisions that have the potential to save countless lives.
This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways.
As global focus on climate change intensifies, carbon credits have become an important tool for reducing greenhouse gas emissions. Africa, with its abundant natural resources and potential for sustainable development, is well-positioned to capitalize on this growing market. This article explores how Africa can enhance its participation in the carbon credit market, transforming environmental initiatives into economic opportunities by addressing key implementation challenges. By utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method within an interval-valued spherical fuzzy (IVSF) framework, the study supports collective decision-making. It identifies three crucial factors: access to financing issue, the absence of clear policies and legal frameworks, and the lack of capacity and expertise within governments, businesses, and communities. The research provides practical recommendations for governments aiming to effectively implement the carbon credit concept.
The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications.
Confidence sets provide a robust method for addressing the uncertainty inherent in the membership degrees of elements within fuzzy sets (FSs). These sets enhance the capability of FSs to manage imprecise or uncertain data systematically. Analogous to repeated experimentation, the interpretation of confidence sets remains valid before sample observation. However, once the sample is examined, all confidence sets exclusively encompass parameter values of either 1 or 0. This study introduces novel techniques in the domain of confidence levels, specifically the Confidence Complex Polytopic Fuzzy Weighted Averaging (CCPoFWA) operator, confidence complex polytopic fuzzy ordered weighted averaging (CCPoFOWA) operator, and Confidence Complex Polytopic Fuzzy Hybrid Averaging (CCPoFHA) operator. These aggregation operators are indispensable tools in data analysis and decision-making, aiding in the understanding of complex systems across diverse fields. They facilitate the extraction of valuable insights from extensive datasets and streamline the presentation of information to enhance decision support. The efficacy and utility of the proposed methods are demonstrated through a detailed illustrative example, underscoring their potential in strategic decision-making for the placement of nuclear power plants in Pakistan.
The pressing need to reduce reliance on petroleum in the energy sector and the increasing demand for environmental protection are driving research and practical endeavors in the management of renewable supply chains. Professionals, global institutions and scholars have widely acknowledged the importance of studying the correlation, between the performance of supply chains and renewable energy sources. It's important to delve into the articles in terms of the methodologies that have been used, the principal concerns addressed, the specific renewable energy sources focused on, and the performance indicators employed to optimize supply chains for renewable energies. This paper provides an analysis that improves the understanding of research in the realm of quantitative decision making for renewable energy supply chains. The analysis commences by searching for articles published. Subsequently, they are narrowed down to those that are most relevant. The article also addresses knowledge gaps in the literature. The findings provide a reference for researchers who are considering conducting studies in this area.
This study was undertaken to elucidate the influence of self-management on the productivity levels of personnel within the Water and Wastewater Department, District 2, Tehran, utilizing a descriptive survey method that engaged 119 respondents. The assessment was founded on the administration of meticulously validated questionnaires, with subsequent statistical analysis conducted using Statistical Package for the Social Sciences (SPSS). The analysis included the Kolmogorov-Smirnov test to confirm the normal distribution of the variables, namely, self-management strategies and productivity levels, and the Pearson-Spearman tests to evaluate correlations. The findings, underscored by Cronbach's Alpha values of 0.879 for self-management strategies and 0.906 for productivity levels, confirmed the hypothesis of a significant positive impact of self-management on workforce productivity. Notably, the natural reward strategy was identified as having the least effect on ameliorating workplace conditions. This investigation contributes to the body of knowledge by highlighting the critical role of self-management practices in enhancing the efficiency of public sector operations. The insights garnered from this study pave the way for the implementation of strategic self-management practices aimed at boosting productivity within public sector entities.
In the realm of understanding consumer purchasing behaviors and refining decision-making across diverse sectors, Market Basket Analysis (MBA) emerges as a pivotal technique. Traditional algorithms, such as Apriori and Frequent Pattern Growth (FP-Growth), face challenges with computational efficiency, particularly under low minimal support settings, which precipitates an excess of weak association rules. This study introduces an innovative approach, termed Customer-Centric (CC)-MBA, which enhances the identification of robust association rules through the integration of consumer segmentation. By employing Recency, Frequency, and Monetary (RFM) analysis coupled with K-means clustering, customers are categorized based on their purchasing patterns, focusing on segments of substantial value. This targeted approach yields association rules that are not only more relevant but also more actionable compared to those derived from conventional MBA methodologies. The superiority of CC-MBA is demonstrated through its ability to discern more significant association rules, as evidenced by enhanced metrics of support and confidence. Additionally, the effectiveness of CC-MBA is further evaluated using lift and conviction metrics, which respectively measure the observed co-occurrence ratio to that expected by chance and the strength of association rules beyond random occurrences. The application of CC-MBA not only streamlines the analytical process by reducing computational demands but also provides more nuanced insights by prioritizing high-value customer segments. The practical implications of these findings are manifold; businesses can leverage this refined understanding to improve product positioning, devise targeted promotions, and tailor marketing strategies, thereby augmenting consumer satisfaction and facilitating revenue growth.
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.
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.
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.
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