Green 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.

Purpose Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI. Design/methodology/approach This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria. Findings Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further. Research limitations/implications Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed. Originality/value This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

Renewable energy development in Libya faces numerous obstacles that hinder its progress. This paper aims to identify these obstacles and propose effective strategies to overcome them. Based on the literature review and expert opinions, eight obstacles were identified: lack of infrastructure, dependence on fossil fuels, lack of a stable investment climate, political instability, weak regulatory framework, varying environmental conditions, lack of public awareness, and technological barriers. The analytic hierarchy process (AHP) method was used to calculate the weights of these obstacles. The results showed that lack of infrastructure was the most critical obstacle, followed by dependence on fossil fuels. Seven strategies were suggested to overcome these obstacles: encouraging private sector investment, providing financial incentives, strengthening the regulatory framework, capacity building, promoting public awareness, technology transfer, and international cooperation. The combined compromise solution (CoCoSo) method was used to rank these strategies based on their effectiveness. The results showed that encouraging private sector investment was the most important strategy to overcome the obstacles. The findings of this paper can support decision-makers in Libya to take the right decisions and allocate resources effectively to overcome the identified obstacles and promote renewable energy development. Additionally, the paper provides insights into other countries facing similar challenges in the development of renewable energy.

Road 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.

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations.

The application of Industry 4.0 (I4.0) in the field of logistics leads to the emergence and development of the concept of logistics 4.0. Many I4.0 technologies have been applied in the field of logistics. The goal of this research is to analyze the applicability of nine key I4.0 technologies in logistics centers (LC). For this purpose, an integrated MEREC (MEthod based on the Removal Effects of Criteria)—fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) model was developed. The applicability of nine I4.0 technologies was evaluated based on 15 subcriteria within three main groups of criteria, namely, technological, social and political, and economic and operative. Using the MEREC method, the weight values of the criteria and subcriteria were determined, while the technologies were ranked using the fuzzy MARCOS method. Based on the results obtained by applying this integrated MCDM (multicriteria decision-making) model, CC was identified as the best alternative, i.e., the technology that is most applicable in logistics centers, followed by IoT and big data. An analysis of the sensitivity of the obtained results to the change in the importance of the criteria was carried out, which shows certain changes in the ranking when the importance of the most important criterion changes.

When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria.

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 2ρσρ+σ 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.

Supply chain management (SCM) is deeply affected by the COVID-19 pandemic besides breakdowns occurred in all sectors. Nowadays, managers need techniques for protecting supply chains from serious and costly disruptions, establishing permanent relationships with the customers and partners and preventing breakdowns throughout the process. Each firm needs to determine SCM strategies to be prepared for breakdowns in an intense competitive environment. With COVID-19, the change in business and trade environments has taken a different dimension, and it has revealed a new relationship between the efforts to perpetuate supply chains and strategies for supply chain management and enabled new models. In this study, it is aimed to prioritize the factors that lead to SCM breaks needed in project management and the realization of projects, and to choose the most successful SCM strategy considering COVID-19. For this purpose, rough SWARA was used for weighting factors and rough MARCOS was used for the alternative selection. According to the findings, the transportation capacity factor was found to be the most important factor leading to SCM breakdowns. The most ideal supply chain management strategy has been the “collaborative supply chain management strategy.” In the food manufacturing sector, the study can be considered as a roadmap in terms of preventing supply chain management breaks during the COVID-19 process and helping to ensure a sustainable production. As another theoretical and practical importance of the study, it is aimed to propose a robust, powerful, and practical decision-making model that can cope with the current uncertainties.

The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company’s business through the establishment of distribution centers. To answer this question, we conducted research in the Brčko District of BiH in order to determine the best location for a distribution center using expert decision-making based on linguistic values. In order to use these values when selecting locations, a fuzzy set was formed using the IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to the Ideal Solution) methods. The IMF SWARA method was utilized to determine the weights of the criteria, and the fuzzy CRADIS method was employed to rank the locations based on expert ratings. The location for the construction of distribution centers at Bodarište was rated the worst, while the McGowern Base location was rated the best. Based on these findings, the research question was answered, and it was demonstrated that fuzzy methods could be utilized in the selection of distribution center locations. Hence, we recommend that future research be performed on the application of fuzzy methods in the expert selection of potential sites for distribution centers.