A Bonferroni mean considering Shapley fuzzy measure under hesitant bipolar-valued neutrosophic set environment for an investment decision

Navigating ambiguity: A novel neutrosophic cubic shapley normalized weighted Bonferroni Mean aggregation operator with application in the investment environment

The Neutrosophic Cubic Shapley Normalized Bonferroni (NC-SNWBM) method represents a cutting-edge approach to decision making theory, combining three distinct mathematical frameworks the neutrosophic cubic sets (NCS), Shapley values, and the Bonferroni aggregation operator. This innovative method addresses the challenges posed by uncertainty, vagueness, and imprecision in decision-making (DM) processes, offering a comprehensive and versatile tool for handling complex and dynamic scenarios. Neutrosophic cubic sets offers a strong platform to handle ambiguous and vague data due to three components Membership Grade (MG), Non-Membership Grade (NMG) and Indeterminancy Grade (IG) in data. By adding Shapley Fuzzy Measures (SFM), which come from cooperative game theory, distribute values among cooperative agents equally and to account for each agent's contributions to all potential coalitions. The Bonferroni aggregation operator-a statistical aggregative tool that regulates the likelihood of many types in error in statistical tests and the interdependence of the input arguments by allowing different values to parameters involved. These values are further improved by normalization in the framework of the NC-SNWBM approach in order to consider the various degrees of impact that agents exert in various circumstances. This operator is smoothly combined with normalized Shapley values and neutrosophic cubic sets in the NC-SNWBM approach to enable the aggregation of data with different levels of imprecision and uncertainty from various sources using NCS. The MG, NMG and IG connected to NCS are important elements of the NC-SNWBM approach. To evaluate each element's contribution to the overall value distribution SFM are used, and the Bonferroni aggregation operator maintains a careful balance between conservatism and significance. Together, these components provide a thorough framework that successfully tackles the problems caused by ambiguity, imprecision, and uncertainty in scenarios involving decision-making. The NC-SNWBM operator is applied to a numerical problem as an application in investment environment and sensitive and comparative analysis are conducted. The recommendation based on sensitive and comparative analysis proposed.

Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best-worst approach: an emerging economy perspective

The food waste hierarchy offers that prevention of the waste is the best solution to deal with this complex issue. Herein, to take a preventive action against food waste, this paper attempts to prioritize the key causes and drivers of retail food waste employing Dombi-Bonferroni operators-based fuzzy best-worst (DB-FBWM) framework. Drawing upon stakeholder theory and contingency theory, causes of retail food waste are categorized into six aspects and their 23 drivers. The results revealed that the "customer-driven" cause is found as the foremost aspect of retail food waste, whereas the "undesirable in-store customer behaviors" is the most vital driver. Identifying the causes and drivers of retail food waste through a reliable model can advance theory and allow building partnership among food supply chain stakeholders for sustainable waste management. The sensitivity and comparison analysis are further performed to confirm the solidity of the approach conducted.