Прегледај по Аутор "Moslem, Sarbast"

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    A novel group decision-making approach based on partitioned Hamy mean operators in q-rung orthopair fuzzy context
    (Springer, 2024) Rawat, Sukhwinder Singh; Komal; Liu, Peide; Stevic, Zeljko; Senapati, Tapan; Moslem, Sarbast
    In multi-attribute group decision-making (MAGDM), the attributes can be placed into independent groups based on their properties through partitioning. First, the partitioned dual Hamy mean (PDHM) operator is introduced, along with its essential properties. This operator integrates these separate groups while preserving the relationships between the attributes within each group. Furthermore, the partitioned Hamy mean (PHM) and the PDHM operators are also constructed in the generalized orthopair fuzzy environment, namely the q-rung orthopair fuzzy PHM (q-ROFPHM), the q-rung orthopair fuzzy PDHM (q-ROFPDHM), and theirweighted forms. Their essential properties are verified to ensure the validity of the proposed aggregation operators (AOs). Subsequently, a newMAGDMapproach is developed, employing the proposed AOs. TheMAGDMproblem of selecting the best person is examined. Moreover, the research includes a sensitivity analysis in three directions and a comparative analysis of the proposed MAGDM approach with five different approaches. The findings indicate that applying attribute partitioning in the proposed approach mitigates the adverse impact of irrelevant attributes, leading to more feasible and reliable outcomes. Additionally, a practical case study focuses on selecting a suitable industry for investment among the five available options. This case study demonstrates the approach’s effectiveness by considering five distinct qualities and results that make the Internet industry the best place to invest. Furthermore, a comparative analysis with four similar papers is also performed, indicating that the developed method’s results are more reliable and consistent.
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    A novel interval rough model for optimizing road network performance and safety
    (ELSEVIER, 2024) Na, Zhou; Stević, Željko; Subotić, Marko; Kumar Das, Dillip; Kou, Gang; Moslem, Sarbast
    This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model’s ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model’s effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications.
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    Sustainable development solutions of public transportation:An integrated IMF SWARA and Fuzzy Bonferroni operator
    (Elsevier, 2023) Moslem, Sarbast; Stević, Željko; Tanackov, Ilija; Pilla, Francesco
    Developing the quality of public transport has an efficient impact to attract more users to switch modes from private vehicles to public transport, which has a tremendous capability in reducing the traffic congestion, noise and CO2 emissions the urban areas. For this reason, policy makers and scholars aim to enhance the supply quality of public transport system, where involving citizens along with experts and decision makers in the decision process will reflect the existed and future demand to provide more sustainable solutions which provide efficient solutions to achieve positive impacts on the local environment. This study intends to determine the preference of decision-makers and operators for the importance of the supply quality elements of urban bus transport services in Mersin city, Turkey. For this aim, decision makers and experts are involved in the evaluation process to provide feedback on public transport quality with the view of increasing the satisfaction and thus usage, with positive impacts on increased public transport modal share and dropped CO2 emission transport. In total we have formed list of three main criteria: transport quality, service quality and traceability which contains in total 21 sub-criteria. For determining all criteria weights we have formed in total 112 models using IMF SWARA (Stepwise Weight Assessment Ratio Analysis) method and for final determining criteria weights Fuzzy Bonferroni operator (FBO) has been used. Applying such model we have ensured stability in final values of criteria and have obtained optimal results based on preferences decision makers. The adopted results show that the most significant attribute in the system is the “Traceability” with highest weight score (0.398), followed by the “Transport quality” with weight score (0.334), however, the service quality rank as the last significant attribute with weight score (0.264). The originality of our work is conducting IMF SWARA method for improving the service quality of the public transport system.