RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management
No full textDigital transformation can help to make better use of existing transportation networks that are congested. One solution to the road congestion problem is real-time traffic management, which focuses on enhancing traffic flow conditions. The advantages of real-time traffic management systems have developed significantly as a result of connected autonomous vehicle (CAV) innovations. CAVs can act as enforcers for managing the traffic. This study aims to propose a novel rough numbers-based extended Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method for prioritizing real-time traffic management systems. Furthermore, a new approach for defining rough numbers is proposed, based on an improved methodology for defining rough numbers' lower and upper limits. This allows consideration of mutual relations between a set of objects and flexible representation of rough boundary interval depending on the dynamic environmental conditions. In this study, three main alternatives are defined for real-time traffic management systems: real-time traffic management, real-time traffic management integrated with CAVs, and real-time traffic management by using CAVs. For these alternatives, 5 main criteria and 18 sub-criteria are defined and then prioritized using the fuzzy multi-criteria decision-making (MCDM) approach. The proposed method's performance is validated through scenario analysis. The findings demonstrate that the proposed method is effective and applicable to real-world conditions. According to the study's findings, real-time traffic management with CAVs is the most advantageous alternative, while real-time traffic management integrated with CAVs is the least advantageou
A modified EDAS model for comparison of mobile wallet service providers in India
No full textThe present paper has two-fold purposes. First, the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users' views in the Indian context. To this end, we propose a new grey correlation-based Picture Fuzzy-Evaluation based on Distance from Average Solution (GCPF-EDAS) framework for the comparative analysis. We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-a-vis service quality dimensions for criteria selection. For comparative ranking, we conduct our analysis under uncertain environments using picture fuzzy numbers. We find that user-friendliness, a wide variety of use, and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users. It is seen that PhonePe (A3), Google Pay (A2), Amazon Pay (A4) and PayTM (A1) hold top positions. For validation of the result, we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution. We observe a significant consistency. We then carry out rank reversal test for GCPF-EDAS model. We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon. To examine the stability in the result for further validation, we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights. We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions
A novel fuel supply system modelling approach for electric vehicles under Pythagorean probabilistic hesitant fuzzy sets
No full textVarious companies have developed electric vehicle (EV)-based multiple fuel supply system modeling approaches (FSSMAs). Nonetheless, no superior approach concurrently satisfies all essential criteria, including 'sustainability' and 'fuel consideration' criteria. Furthermore, benchmarking the FSSMA alternatives to determine the most sustainable ones does not come without issues. The five main most common concerns are the use of various evaluation criteria, effecting the weights of the criteria with sublayers, criteria pri-oritization, trade-offs among the criteria, and data variations. Thus, this study proposes a novel FSSMA for EV benchmarking based on two methods-the Pythagorean probabilistic hesitant fuzzy sets and fuzzy weighted zero inconsistency (PPH-FWZIC) and the measure-ment of alternatives and ranking according to the compromise solution (MARCOS)-which are integrated as a single method. The PPM-FWZIC method was developed to solve the cri-teria prioritization issue, while the MARCOS method was developed to solve the various evaluation criteria, trade-offs among the criteria, and data variation issues to benchmark the FSSMA for EV alternatives. The integrated multicriteria decision-making (MCDM) method allows the system to perform a backward scoring process (BSP) and derive a scor-ing decision matrix from the formulated decision matrices that are performed based on the feed-forward data presentation (FFDP) procedure to solve the multiple criteria layers that affect the proper assessment of the impact of a certain criterion and its subcriteria in the weighting purpose issues. Subsequently, the FSSMAs for EVs are benchmarked, and the most sustainable approach is selected. The results were tested via sensitivity analysis and the Spearman correlation coefficient. The present study is also compared with a bench-mark study based on a benchmarking checklist
DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)
No full textForecasting climate and the development of the environment have been essential in recent days since there has been a drastic change in nature. Weather forecasting plays a significant role in decision-making in traffic management, tourism planning, crop cultivation in agriculture, and warning the people nearby the seaside about the climate situation. It is used to reduce accidents and congestion, mainly based on climate conditions such as rainfall, air condition, and other environmental factors. Accurate weather prediction models are required by meteorological scientists. The previous studies have shown complexity in terms of model building, and computation, and based on theory-driven and rely on time and space. This drawback can be easily solved using the machine learning technique with the time series data. This paper proposes the state-of-art deep learning model Long Short-Term Memory (LSTM) and the Transductive Long Short-Term Memory (T-LSTM) model. The model is evaluated using the evaluation metrics root mean squared error, loss, and mean absolute error. The experiments are carried out on HHWD and Jena Climate datasets. The dataset comprises 14 weather forecasting features including humidity, temperature, etc. The T-LSTM method performs better than other methodologies, producing 98.2% accuracy in forecasting the weather. This proposed hybrid T-LSTM method provides a robust solution for the hydrological variables
Smart Tourism as a Strategic Response to Challenges of Tourism in the Post-COVID Era
No full textMany scholars have emphasised the importance of tourism for global economies. However, contemporary business paradigms in tourism have changed due to the COVID-19 pandemic, which made a tremendous negative impact on tourism hitherto and affected certain positive aspects, such as boosting digital transformation. Despite the considerable increase in the inquisitiveness to the influence of COVID-19 on different industries and digital transformation and a myriad of notable studies concerning this subject, the interdependence between the impact of the pandemic on digital transformation in tourism is understudied. Building on the previous studies, this paper aims to address this issue, narrow the existing theoretical gap, and provide how to use new technologies to strategically approach future tourism challenges. The paper particularly investigates smart tourism as a new and effective method to cope with challenges in tourism. The goal of the article is to contribute to understanding the impact of the COVID-19 pandemic and other crises on the acceleration of digital transformation and the role of new technologies in tourism. This study sheds new light on the strategical approach to contemporary and future tourism challenges
Personal Ethics Perception and Its Relation to the Expected Properties of a Good Leader
No full textAs in other developed countries, in Slovenia the view of ethics changes with growing up. The key changes occurred during the transition from the traditional to the modern, and then to the postmodern information society. Leadership is regarded as the study of a fundamental psychological process or social influence in relation to certain issues. Our hope for a better world lies in leadership, as every leader leaves a positive imprint on future generations as well. Through generations, differences in values and views on ethics are perceived. Therefore, in our paper we ask ourselves the research question of how students' self-assessment of ethics affects the expected qualities of a good leader. 242 undergraduate students were included in the study. Data collection took place from March 2020 to April 2022. Through the analysis of collected data based on students' self-assessments, we determine how they value their ethics and what qualities they expect from a good leader. We conclude the article by giving our opinion based on the findings of the research
Interval-valued intuitionistic fuzzy symmetric point criterion-based MULTIMOORA method for sustainable recycling partner selection in SMEs
No full textThe need and strategy of eco-economy encourage enterprises to reach sustainability by employing sustainable supply chain management. Contrary to the numerous literatures focusing on green design and sustainability practices, this paper presents sustainable recycling partner (SRP) assessment with economic, environmental and social pillars. To propose an integrated framework for SRP selection in small-and-medium enterprises (SMEs), interval-valued intuitionistic fuzzy set (IVIFS)-based model is applied to deal with the vague, uncertain and qualitative information. Inspired by these topics, we propose IVIF-improved Dombi weighted averaging and IVIF-improved Dombi weighted geometric operators to aggregate the decision-making expert’s preferences and discuss some sophisticated characteristics of developed aggregation operators. Further, we establish an integrated weighting model by combining the IVIF-symmetric point of criterion (IVIF-SPC) and IVIF-rank sum (IVIF-RS) tools. Then, the classical multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) model has been extended using the proposed divergence measure and improved Dombi operators for treating multi-criteria decision analysis problems on IVIFS setting. To explore the effectiveness and practicability of the proposed model, a case study of SRP selection in SMEs is conducted. The results of the developed model, “Namo e-waste management limited (NEWML),” should be considered as the first SRP in SMEs in India. Further, the sensitivity investigation and comparative discussion are presented to check the stability and robustness of the presented technique
Preface to the special issue on numerical simulations and optimization under uncertainty
No full textNew Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices
No full textSince corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups
Student Profiles of Change in a University Course: A Complex Dynamical Systems Perspective
No full textLearning analytics approaches to profiling students based on their study behaviour remain limited in how they integrate temporality and change. To advance this area of work, the current study examines profiles of change in student study behaviour in a blended undergraduate engineering course. The study is conceptualised through complex dynamical systems theory and its applications in psychological and cognitive science research. Students were profiled based on the changes in their behaviour as observed in clickstream data. Measure of entropy in the recurrence of student behaviour was used to indicate the change of a student state, consistent with the evidence from cognitive sciences. Student trajectories of weekly entropy values were clustered to identify distinct profiles. Three patterns were identified: stable weekly study, steep changes in weekly study, and moderate changes in weekly study. The students with steep changes in their weekly study activity had lower exam grades and showed destabilisation of weekly behaviour earlier in the course. The study investigated the relationships between these profiles of change, student performance, and other approaches to learner profiling, such as self-reported measures of self-regulated learning, and profiles based on the sequences of learning actions