The evaluation and selection of an optimal, efficient and reliable supplier is becoming more and more important for companies in today’s logistics and supply chain management. Decision-making in the supplier selection domain, as an essential component of the supply chain management, is a complex process since a wide range of diverse criteria, stakeholders and possible solutions are embedded into this process. This paper shows a fuzzy approach in multi – criteria decision-making (MCDM) process. Criteria weights have been determined by fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) method. Chosen methods, fuzzy TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) and fuzzy ARAS (Additive Ratio Assessment) have been used for evaluation and selection of suppliers in the case of procurement of THK Linear motion guide components by the group of specialists in the “Lagerton” company in Serbia. Finally, results obtained using different MCDM approaches were compared in order to help managers to identify appropriate method for supplier selection problem solving.

Active systems have attracted a great deal of attention in the last few decades due to the potential benefits they offer over the conventional passive systems in various applications. Dealing with active systems requires the possibility of modeling and simulation of their behavior. The paper considers thin-walled active structures with laminate architecture featuring fiber reinforced composite as a passive material and utilizing piezoelectric patches as both sensor and actuator components. The objective is the development of numerically effective finite element tool for their modeling. A 9-node degenerate shell element is described in the paper and the main aspects of the application of the element are discussed through a set of numerical examples.

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The article considers thin-walled active structures, which utilize the piezoelectric patches as both sensor and actuator components. Most of the developed models for this type of application make an assumption of a constant electric field and, consequently, a linear distribution of the electric potential over the thickness of the piezopatches. Some recent papers use higher-order functions to model the mentioned electric quantities. In the study, it is demonstrated through an analytical deduction that a quadratic distribution of the electric potential and a linear distribution of the electric field are adequate for the piezoelectric patch that exhibits kinematics described by a first-order two-dimensional theory. A degenerated shell element is developed for modeling purposes and a set of numerical analyses is performed in order to demonstrate the additional stiffening effect caused by the refined functions for the electric quantities. The significance of the effect is discussed in detail.

In this paper, we investigate the multi-criteria decision-making complications under intuitionistic fuzzy hypersoft set (IFHSS) information. The IFHSS is a proper extension of the intuitionistic fuzzy soft set (IFSS) which discusses the parametrization of multi-sub attributes of considered parameters, and accommodates more hesitation comparative to IFSS utilizing the multi sub-attributes of the considered parameters. The main objective of this research is to introduce operational laws for intuitionistic fuzzy hypersoft numbers (IFHSNs). Additionally, based on developed operational laws two aggregation operators (AOs), i.e., intuitionistic fuzzy hypersoft weighted average (IFHSWA) and intuitionistic fuzzy hypersoft weighted geometric (IFHSWG), operators have been presented with their fundamental properties. Furthermore, a decision-making approach has been established utilizing our developed aggregation operators (AOs). Through the established approach, a technique for solving decision-making (DM) complications is proposed to select sustainable suppliers in sustainable supply chain management (SSCM). Moreover, a numerical description is presented to ensure the validity and usability of the proposed technique in the DM process. The practicality, effectivity, and flexibility of the current approach are demonstrated through comparative analysis with the assistance of some prevailing studies.

Healthcare systems worldwide are facing problems in providing health care to patients in a pandemic caused by the SARS-CoV-2 virus (COVID-19). The pandemic causes an extreme disease to spread with fluctuating needs among patients, which significantly affect the capacity and overall performance of healthcare systems. In addition, its impact on the sustainability of the entire economic and social system is enormous and certain sustainable management strategies need to be selected. To meet the challenges of the COVID-19 pandemic and ensure sustainable performance, national healthcare systems must adapt to new circumstances. This paper proposes an original multi-criteria methodology for the sustainable selection of strategic guidelines for the reorganization of a healthcare system under the conditions of the COVID-19 pandemic. The selection of an appropriate strategic guideline is made on the basis of defined criteria and depending on infection capacity and pandemic spread risk. The criteria for the evaluation of strategic guidelines were defined on the basis of a survey in which the medical personnel engaged in the crisis response team during the COVID-19 pandemic in the Republic of Serbia participated. The Level-Based Weight Assessment (LBWA) model and Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method were used to determine the weight coefficient criteria, while a novel fuzzy Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval (RAFSI) model was used to evaluate the strategic guidelines. The proposed multi-criteria methodology was tested in a case study in the Republic of Serbia. The validity of the proposed methodology is shown through the simulation of changes in input parameters of Bonferroni aggregation functions and through a comparison with other multi-criteria methodologies.

The conditions of globalization often dictate the functioning of transport markets, so it is necessary to conduct frequent research in order to achieve sustainable business. This is achieved through adequate risk and safety management at all levels. The research carried out in this paper includes determining the state of railway traffic safety in a total of nine railway sections in Bosnia and Herzegovina (B&H). The aim of this paper is to develop a new integrated Entropy-Fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment)-DEA (Data Envelopment Analysis) model for determining the state of safety in B&H under particular conditions of uncertainty. Additionally, the aim is to combine the advantages of linear programming (DEA), an objective method (Entropy), and a subjective method (Fuzzy PIPRECIA). In this way, an integrated objective–subjective model is created that provides accurate and balanced decision-making through their integration. Eleven sustainable criteria were defined and divided into six inputs and five outputs. The Entropy model was used to determine the weight values of the inputs, while due to the nature of the outputs, Fuzzy PIPRECIA was used to evaluate them. After the application of the two methods, the way of averaging their values was defined. The DEA model, which implies an input- and output-oriented model, was applied to determine which railway sections have satisfactory performance in terms of safety. Two sections were eliminated from further computation due to extremely poor performance and high risk. Then, the weighted overall efficiency ranking method was applied to determine the final ranking of the railway sections. The results obtained were verified through a sensitivity analysis, which involved changing the impact of the five most significant criteria and a comparison with two Multi-Criteria Decision-Making (MCDM) methods.

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.