Multiattribute decision-making is an important part of decision-making theory and modern scientific decision-making. It is widely used in engineering design, economic management, and so on. It is an important part of modern decision science to sort decision objects when considering multiple attributes. Due to time pressure and lack of understanding of decision-making problems, it is difficult for decision makers to accurately express judgment information. Decision-makers’ judgment information is more suitable to be expressed by intuitionistic fuzzy sets rather than deterministic numbers or linguistic variables. In the multiattribute decision-making problem, the size of attribute weight reflects the relative importance of each attribute. The research on attribute weight determination method is one of the core problems of multiattribute decision-making. Whether it is the subjective weighting method, the objective weighting method, or the combined weighting method, the research mainly focuses on deterministic multiattribute decision-making, mostly transforming fuzzy information into deterministic information for decision-making, which will lose a lot of information. Due to the differences of objective information data, a combined weighting method in different cases was proposed in this study. The original weight information and the prior information of standardized evaluation can be fully utilized in this model. The results indicate that when decision makers have preferences for different weighting methods, the combined weighting method can be determined according to the preference information of decision makers.

This paper presents a comprehensive quantitative approach for managing safety in coal mines with a multicriteria decision-making model by using fuzzy mathematics. We develop a set of factors that affect the management of safety in coal mines, including ventilation, the mine roof, and mine temperature. The factors also include mine dust, mining gas, and mining water. Other factors are equipment reliability, explosive risk of equipment, maintenance of equipment, staff operations and staff training, staff attendance, index to evaluate and manage workload, and project quality, monitoring systems, and safety information. The approach is then applied to evaluate a case study on the safety management of the Fushan Coal Industry Co., Ltd. Coal Mine in Henan Province of China. The matrix that evaluates each factor is obtained through a membership function and scored by experts, and the weight of each attribute is obtained through an analytic hierarchy process. The evaluation results show that the Fushan Coal Industry Co., Ltd. Coal Mine overall has a good safety track record. However, the mine needs to further enhance how they manage safety, optimize the mine ventilation system, and further strengthen safety management practices to ensure the safety of production processes in the mine.

To examine a potential correlation between food waste and lunar phases, we have devised a randomized controlled trial. The experiment spanned from 31 March to 10 July 2022, during which we employed the direct weighing method to collect 1903 valid data points on food waste. Utilizing propensity score matching, we meticulously controlled for various factors, including dining dates, the number of diners, dining times, spending levels, and store activities. The study revealed a close relationship between lunar phases and food waste. During the new moon phase, there was an increase in both orders and waste generated by consumers. Specifically, individuals, on average, squandered an additional 6.27% of animal protein (0.79 g), 24.5% of plant protein (1.26 g), 60.95% of starchy foods (3.86 g), and 61.09% of vegetables (5.12 g), resulting in an aggregate food waste of 32.14% (10.79 g). Conversely, during the full moon phase, consumers decreased their orders and subsequently decreased food waste. On average, individuals wasted 44.65% less animal protein (5.76 g), 43.36% less plant protein (2.5 g), 85.39% less seafood (0.73 g), and 8.43% less vegetables (0.93 g), resulting in a 20.52% (7.81 g) reduction in food waste. Furthermore, we validated our conclusions through various validation methods, including model replacement, to ensure robustness and reliability.