Vladimir Simic

This research presents a new method for real-time logistics monitoring and quality control in supply chain management using spherical fuzzy Z-number Aczel–Alsina (AA) aggregation. A method based on quantum cognitive theory is employed to improve the accuracy and efficiency of logistics tracking. The proposed method provides a robust framework for decision-making when dealing with dynamic and unpredictable real-time logistics data. The results demonstrate improvements in supply chain transparency and logistics tracking accuracy, which are important for timely and informed logistics management decisions. This research employs a multi-attribute group decision-making (MAGDM) approach to address this complex problem. The main aim of this paper is to propose and explore spherical fuzzy Z-number sets (SFZNSs). Compared with existing fuzzy set structures, SFZNSs provide a more flexible framework for representing higher levels of uncertainty. We introduce an innovative distance measure for spherical fuzzy Z-numbers and derive unknown weight information using spherical fuzzy entropy calculations. Additionally, aggregation operators based on the AA t-norm and t-conorm are introduced to address MAGDM problems within the spherical fuzzy Z-number environment. The proposed method is particularly useful when the weights of the criteria and decision makers are unknown. By integrating AA operations within the spherical fuzzy Z-number framework, the proposed methodology provides a flexible and robust approach to information aggregation, enabling complex and uncertain information to be effectively represented and processed in decision-making problems.

Catalytic converters in sulfur recovery units (SRUs) play a vital role in ensuring efficient sulfur conversion and minimizing hazardous emissions, yet their specific failure modes remain largely understudied. Traditional failure mode and effects analysis-based risk assessment is constrained by equal criterion weighting and by its inability to concurrently address vagueness and the reliability of expert judgments. To address this methodological gap, this study introduces a novel framework that integrates Z-number Stepwise Weight Assessment Ratio Analysis (Z-SWARA) to determine fuzzy reliability-based weights for severity, occurrence, and detection criteria and Z-number Weighted Aggregated Sum Product Assessment (Z-WASPAS) to rank failure modes. This represents the application of this specific hybrid Z-number approach to SRU catalytic converters, explicitly capturing both uncertainty and expert confidence. Expert linguistic evaluations were converted into Z-numbers to construct a realistic weighted decision matrix evaluating 23 identified SRU risk modes. Based on the Z-SWARA results, severity emerged as the most critical criterion (weight = 0.420), followed by occurrence (0.325) and detection (0.256). Furthermore, the Z-WASPAS ranking revealed that the most critical risks are hot spot formation in the catalyst bed (K = 0.83), coke formation on the catalyst (K = 0.78), and malfunction of the inlet control valve (K = 0.77). The results demonstrate that the proposed hybrid approach significantly improves the discriminative power and accuracy of risk prioritization, supporting proactive maintenance and effective risk management in complex process facilities.

The online version contains supplementary material available at 10.1038/s41598-026-47477-8.

Sustainable tourism performance of cruise ports is a critical determinant of the long-term competitiveness of both ports and destination regions. This study aims to develop and apply a novel picture fuzzy sets (PFSs)-based decision-support system to systematically evaluate cruise ports’ sustainable tourism performance by integrating qualitative expert judgments with quantitative operational data. The proposed framework adopts a multi-attribute group decision-making structure, in which PFSs are employed to explicitly capture uncertainty, hesitation, and neutrality in expert evaluations. Criteria weights are determined using an extended PFS-based weights by envelope and slope (WENSLO) method, while cruise port rankings are obtained through a PFS-integrated multi-attributive border approximation area comparison (MABAC) approach. The applicability of the proposed PFS–WENSLO–MABAC hybrid model is demonstrated through a case study of major cruise ports in Türkiye. The results indicate that the number of cruise passengers (0.5601) and the number of cruise ships (0.4218) are the most influential determinants of sustainable tourism performance, with Kuşadası Cruise Port (0.9029) achieving the highest overall ranking. Sensitivity and robustness analyses confirm the stability of the ranking outcomes under varying weighting scenarios. The findings provide actionable insights for port authorities and policymakers by identifying priority performance dimensions and offering a reliable analytical tool to support strategic planning and sustainability-oriented decision-making in cruise tourism.

Choosing the right options for sustainable traditional medicine development is a major challenge for healthcare policymakers and practitioners, especially with uncertainties introduced by political shifts and policy changes. A new approach is developed in this study to address this complex issue. Applying the alternative ranking using two-step logarithmic normalization (ARLON) based on picture fuzzy sets (PFS), this framework is the first of its kind in the literature. It helps identify the most appropriate strategies from options for sustainable traditional medicine development. The study combines the picture fuzzy ARLON approach with the FullEX technique to weigh criteria representing challenges to sustainable traditional medicine development. Sensitivity analysis scenarios and comparative analyses are conducted to test the approach’s robustness. Results show that "promotion of the validation of the safety, efficacy, and quality of medicinal plants and their products" and "concerted campaigns emphasizing the advantageous aspects of traditional medicine" are the most appropriate alternatives for sustainable traditional medicine development.

The Supplier Selection Problem (SSP) plays a significant role in Supply Chain Management (SCM) in today’s competitive world. With respect to this, the literature reveals that incorporating the viability concept in the SSP for the Oil and Gas (O&G) industry has not been adequately addressed in prior studies. Hence, the current study focuses on the SSP for the energy sector by considering the viability pillars. To do so, a data-driven decision-making model is developed that calculates the weights of indicators executing the Fuzzy Best-Worst Method (FBWM) and then evaluates the performance of the supplier by integrating Data Envelopment Analysis (DEA), Support Vector Machine (SVM), and Random Forest (RF) techniques. Overall, the main contribution of this research is to develop an effective data-driven model to examine the viable SSP for the O&G industry. According to the results obtained, among the potential indicators, cost, quality, responsiveness, manufacturing flexibility, robustness, restorative capacity, pollution control, Waste Management (WM), technical capability, and smart factory are selected as the most significant indicators in their corresponding aspects. Moreover, the comparison results against the classic methods demonstrate the robustness, applicability, and validity of the developed data-driven decision framework. Finally, theoretical and managerial implications are presented.

Green port operations require efficient and sustainable terminal management, supported by robust decision-making tools for selecting a terminal operating system (TOS). This research examines the complex process of selecting TOS in the context of green ports, aiming to identify the key criteria that influence the preference for TOS in facilitating port services. The study develops an intuitionistic fuzzy (IF) set-based hybrid decision-analytic model for TOS selection to enhance the overall performance of green ports. The IF–logarithmic decomposition of criteria importance (LODECI)–Aczel–Alsina Weighted Assessment (ALWAS) model was introduced. The IF–LODECI method was formulated for criterion weighting. It incorporated the maximum decomposition approach for robust weight stabilization. The IF–ALWAS method, based on the ALWAS method, was proposed to evaluate alternatives. The new hybrid decision-analytic model integrated Aczel–Alsina t-norm and t-conorm operations, with the IF–Aczel–Alsina weighted averaging operator as the final step. The application of the model was exemplified through a case study conducted at green ports in Türkiye, focusing on environmentally conscious criteria for TOS selection, involving six experts, 10 criteria, and five alternatives. The results revealed that “berth management and scheduling” was identified as the most significant criterion, while “Navis TOS” demonstrated the highest overall performance among the alternatives. Rigorous sensitivity analyses were conducted to validate the robustness of the proposed hybrid model and algorithm. This study presents a comprehensive decision-making framework for selecting TOS in green ports, bridging the gap between theoretical advancements and practical applications.