Multicriteria analysis, Fuzzy set, Agri-food, Green supplier selection (GSS)

Fuzzy set, Archimedean-Dombi operator, Fuzzy similarity measure, Classification, Parkinson’s disease data set

The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation.

Risk monitoring and risk prediction are of great significance to improve the safety of urban rail operation. Existing studies often analyze the topological characteristics of accident networks from the perspective of network theory, in order to point out the role of specific influencing factors in urban rail accidents. This article proposes a risk analysis method of urban rail operation accidents, which takes risk factors, risk points and risk events as nodes to form a network, and combines the interaction between risk points to evaluate the safety of the whole system. The existing system safety analysis methods all build models based on the accidents that have occurred. Based on the analysis of the existing urban rail transit infrastructure and operating environment, this article puts forward the risk factors and risk points that may cause risk events, and combines the mechanical connection, electrical connection and signal connection among risk points to deeply explore the interaction between risks so as to find the key risk points that cause accidents and evaluate the safety of the whole system. The results show that the proposed risk analysis method can provide effective theoretical support for risk monitoring.

Road capacity utilization is causally connected with an appropriate level of efficiency and an optimal level of traffic safety. Therefore, in this paper, it is considered the issue of maximum utilization of road capacity through the maximization of the input parameter AADT (Annual Average Daily Traffic), and the minimization of output parameters related to the categories of traffic accidents. It was defined six main road sections, which were evaluated based on seven techno-operational criteria using an integrated Multi-criteria decision-making (MCDM) model. The data refer to buses as a vehicle category. The Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA) method was chosen to determine the weights of criteria, while the road sections were ranked using the Evaluation based on distance from average solution (EDAS). In addition, in one of the stages of applying the model when it comes to AADT, the Bonferroni operator (BFO) is used. The results show that the highest level of safety refers to a main road section with the following characteristics: average AADT, minimal deviation from the speed limit, an ascent of 7% and the lowest number of traffic accidents by all categories. In the paper, it was performed a multi-phase sensitivity analysis in order to identify possible differences in results when determining new circumstances.

Market changes last years have led to an additional understanding of people importance as the main resources of companies. Truck drivers are one of the occupations with the greatest shortage. More attention is being paid to ways of retaining employees. One of the most important measures is bonus or reward. There is a lack of models in the literature and it is exactly the main motive of this research. Proposed models create a basis for future theoretical research, but also for practical applications. The main assumption is that models must provide a fair way to earn bonuses in a "healthy environment". Two models are proposed. The first model for distribution company with a heterogeneous fleet of vehicles with less capacity. The second model refers to homogenous heavy truck fleet. In the first case, several criteria are used: distance (kilometers) driven, number of tours/rides, number of unloading stops and number of pallets. The second model is based on fuel consumption, distance driven, vehicle maintenance, driver experience (years in the company) and overall dispatcher score. The results show the convenience of applying the proposed models. Certain differences were also identified in the observed models. It can be concluded that there is no universal model for performance appraisal and bonus calculation. Ideas for overcoming and improving models are also proposed. Described models in original or adapted form can be applied to evaluate the performance of drivers in a wide variety of transport systems.