Search

The second-hand vessel market has quite different dynamics than the market of the new-building vessel, and highly complicated and conflicting criteria and many uncertainties affect the ship selection process. Therefore, it is required to use a robust mathematical model to solve these kinds of decision-making problems. For this purpose, this paper presents an extended version of WASPAS (Weighted Aggregated Sum Product ASsessment) techniques with the help of T2NN based on the Bonferroni function (T2NN WASPAS'B). The three main focal points of the proposed approach are (i) setting the influential criteria to select the appropriate Ro-Ro vessel in the second-hand vessel market; and (ii) presenting a flexible group decision-making approach, which is proper to real decision-making problems. (iii) detecting the interrelations among criteria and eliminating the negative impacts of undesirable and excessive values in input variables on the results. Practical use of the proposed approach is demonstrated to select the appropriate Ro-Ro vessel in the second-hand market. The analysis results show that the most effective and determinative factor is Trailer Lane length, and the most effective alternative is GREIFSWALD. Besides, the consistency and validity of the obtained results have been verified with the help of a stability and robustness check. The results prove that the proposed novel T2NN WASPAS'B model is robust, powerful, and reliable for making rational and realistic decisions.

Traffic congestion is, nowadays, one of the most important highway problems. Highway tolls with booth operators are one of the causes of traffic congestion on highways, especially in rush hour periods, or during seasonal holiday travels. The value of driver waiting time (needed to stop and pay the toll) and the cost of the toll booth operators can reach up to about one-third of the revenue. In this paper we propose a novel methodology for continuous-time optimal control of highway tolls by predicting the optimal number of active modules (booths) in toll stations. The proposed methodology is based on a combination of recurrent neural networks, queuing theory, and metaheuristics. We utilized several recurrent neural network architectures for predicting the average intensity of vehicle arrivals. Moreover, the prediction error of the first recurrent neural network was modelled by another one in order to provide confidence estimates, additional regularization, and robustness. The predicted intensity of vehicle arrival rates was used as an input of the queuing model, whereas differential evolution was applied to minimize the total cost (waiting and service costs) by determining the optimal number of active modules on a highway toll in continuous time. The developed methodology was experimentally tested on real data from highway E70 in the Republic of Serbia. The obtained results showed significantly better performance compared to the currently used toll station opening pattern. The solutions obtained by solving a system of differential equations of the queuing model were also validated by a simulation procedure.

The current study is mainly devoted to explore and extend the measurement and ranking of alternatives according to the compromise solution under the background of 2-tuple linguistic q-rung picture fuzzy sets. The capability of q-rung picture fuzzy sets to accommodate a broad range of information, and the salient features of 2-tuple linguistic term sets to manage qualitative data motivate us to present the notion of 2-tuple linguistic q-rung picture fuzzy set. The linguistic terms are very useful to deal with the situations where the data providing the assessment of attributes are not easily quantified. This paper proposes and utilizes the Dombi operations to develop certain aggregation operators: 2-tuple linguistic q-rung picture fuzzy Dombi weighted averaging operator and 2-tuple linguistic q-rung picture fuzzy Dombi weighted geometric operator. The presented methodology utilizes the aforementioned operators to handle and aggregate data in decision-making problems. Furthermore, an example to select the best option among the collection of considered projects is handled through the proposed method to substantiate the extended approach. Ultimately, the validation of the developed technique is conducted through a comparative analysis.

This study investigates the adoption of blockchain technology in small and medium-sized enterprises’ (SMEs) supply chains. Blockchain has been deemed a “Game Changer” for its potential benefits but also has several limitations due to its properties and SMEs’ limited resources. Small and medium-sized companies (SMEs) have more problems employing Industry 4.0 Technologies in practice than larger companies. So, the employment of BC in SMEs’ supply chains, as the economy's backbone, deserves a full investigation which has been addressed in this paper. We use the Technology–Organization–Environment framework to examine both positive and negative factors affecting blockchain adoption in SMEs. A Delphi method was used to refine a list of 42 factors down to the 22 most essential ones. These factors were then prioritized using the Fuzzy Logarithmic Additive Weights Methodology based on expert preferences. The findings indicate that while financial factors play a crucial role in adoption, transparency, and traceability are significant benefits that motivate organizations to adopt blockchain. The organizational dimension also has a substantial impact on decision-making. In this study, we conclude that SMEs must carefully consider their circumstances before adopting blockchain technology, given its infancy state. The barriers to adoption are just as significant as incentives, and SMEs must weigh the financial and organizational factors before making the decision. This study extends the literature by comprehensively looking at all possible factors influencing BC adoption by SMEs. Furthermore, we have highlighted valuable insights for policymakers and SME owners to make the best decision.

Traffic congestion is, nowadays, one of the most important highway problems. Highway tolls with booth operators are one of the causes of traffic congestion on highways, especially in rush hour periods, or during seasonal holiday travels. The value of driver waiting time (needed to stop and pay the toll) and the cost of the toll booth operators can reach up to about one-third of the revenue. In this paper we propose a novel methodology for continuous-time optimal control of highway tolls by predicting the optimal number of active modules (booths) in toll stations. The proposed methodology is based on a combination of recurrent neural networks, queuing theory, and metaheuristics. We utilized several recurrent neural network architectures for predicting the average intensity of vehicle arrivals. Moreover, the prediction error of the first recurrent neural network was modelled by another one in order to provide confidence estimates, additional regularization, and robustness. The predicted intensity of vehicle arrival rates was used as an input of the queuing model, whereas differential evolution was applied to minimize the total cost (waiting and service costs) by determining the optimal number of active modules on a highway toll in continuous time. The developed methodology was experimentally tested on real data from highway E70 in the Republic of Serbia. The obtained results showed significantly better performance compared to the currently used toll station opening pattern. The solutions obtained by solving a system of differential equations of the queuing model were also validated by a simulation procedure.

Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision -making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.