Саобраћајни факултет [Научни радови] / Faculty of Transport and Traffic Engineering [Scientific papers]
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- СтавкаProperties of Convex Fuzzy-Number-Valued Functions on Harmonic Convex Set in the Second Sense and Related Inequalities via Up and Down Fuzzy Relation(MDPI, 2023) Khan, Muhammad Bilal; Stević, Željko; Maash, Abdulwadoud A.; Noor, Muhammad Aslam; Soliman, Mohamed S.In this paper, we provide different variants of the Hermite–Hadamard (H - H) inequality using the concept of a new class of convex mappings, which is referred to as up and down harmonically s-convex fuzzy-number-valued functions (UDH s-convex FNVM) in the second sense based on the up and down fuzzy inclusion relation. The findings are confirmed with certain numerical calculations that take a few appropriate examples into account. The results deal with various integrals of the 2rs r+s type and are innovative in the setting of up and down harmonically s-convex fuzzy-number-valued functions. Moreover, we acquire classical and new exceptional cases that can be seen as applications of our main outcomes. In our opinion, this will make a significant contribution to encouraging more research.
- СтавкаTrapezoidal Interval Type-2 Fuzzy PIPRECIA-MARCOS Model for Management Efficiency of Traffic Flow on Observed Road Sections(MDPI, 2023) Xu, Wei; Kumar Das, Dillip; Stević, Željko; Subotić, Marko; Alrasheedi, Adel F.; Sun, ShiruRoad infrastructure management is an extremely important task of traffic engineering. For the purpose of efficient management, it is necessary to determine the efficiency of the traffic flow through PAE 85%, AADT and other exploitation parameters on the one hand, and the number of different types of traffic accidents on the other. In this paper, a novel TrIT2F (trapezoidal interval type-2 fuzzy) PIPRECIA (pivot pairwise relative criteria importance assessment)-TrIT2F MARCOS (measurement of alternatives and ranking according to compromise solution) was developed in order to, in a defined set of 14 road segments, identify the most efficient one for data related to light goods vehicles. Through this the aims and contributions of the study can be manifested. The evaluation was carried out on the basis of seven criteria with weights obtained using the TrIT2F PIPRECIA, while the final results were presented through the TrIT2F MARCOS method. To average part of the input data, the Dombi and Bonferroni operators have been applied. The final results of the applied TrIT2F PIPRECIA-TrIT2F MARCOS model show the following ranking of road segments, according to which Vrhovi–Šešlije M-I-103 with a gradient of 1.00 represents the best solution: A5 > A8 > A2 > A1 > A4 > A3 > A6 > A12 > A13 = A14 > A11 > A7 > A9 > A10. In addition, the validation of the obtained results was conducted by changing the values of the four most important criteria and changing the size of the decision matrix. Tests have shown great stability of the developed TrIT2F PIPRECIA-TrIT2F MARCOS model.
- СтавкаA Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production(MDPI, 2023) Chen, Xuemei; Zhou, Bin; Štilić, Anđelka; Stević, Željko; Puška, AdisGreen supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy–rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy–rough numbers (FRN). The integrated FRN SWARA–FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy–rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model.
- СтавкаPredicting Road Traffic Accidents—Artificial Neural Network Approach(MDPI, 2023) Gatarić, Dragan; Ruškić, Nenad; Aleksić, Branko; Ðurić, Tihomir; Pezo, Lato; Lončar, Biljana; Pezo, MiladaRoad traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning.
- СтавкаIntegrated intelligent decision support model for ranking regional transport infrastructure programmes based on performance assessment(Elsevier, 2023) Bayane Bouraima, Mouhamed; Qiu, Yanjun; Stevć, Zeljko; Marinković, Dragan; Deveci, MuhammetIn recent years, major regional transport infrastructure programmes have been adopted throughout the African continent. However, it has been noticed that while some programmes seem to be more effective, others have shown opposite outcomes. On this point, understanding the difference in the execution outcomes of these programmes is important for stakeholders. In this study, an improved fuzzy Step-Wise Weight Assessment Ratio Analysis based on the fuzzy Bonferroni aggregator is integrated with fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution to assess the most critical factors affecting the performance of the African regional infrastructure programmes and identify the best regional transportation infrastructure programme based on its performance. Four regional transportation infrastructure programmes are considered. To rank these programmes, five criteria are defined. The results show that “leadership” and “availability of finance” are the most critical factors while the fourth alternative “East African road network project” is the best alternative among the four possible programmes. The robustness of the proposed methodology is validated through a three-phase sensitivity analysis. The results confirmed the stability and applicability of the proposed methodology.