Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

: Within the evolving landscape of modern business, a proficient logistics sector stands paramount in fostering regional and global competitive edges. A country's logistics performance, gauged aptly, can influence not just the business outcomes of individual enterprises but also shape the nation's holistic logistics efficacy. This study delves into an examination of logistics standards in European Union (EU) countries, viewed through the lens of the Logistics Performance Index (LPI) as reported by the World Bank. The primary focus is on the LPI data for 2023, with a subsequent exploration of the EU’s performance trajectory from 2007 to 2018. The findings illuminate that specific EU countries consistently uphold superior logistics proficiency, while striving for advancements. Beyond these front runners, many EU countries manifest commendable logistics outcomes, positioning themselves favorably on the global stage.

The sustainable development of mountain tourism is crucial for preserving the delicate ecosystems and resources found in these unique landscapes. This research paper investigates the sustainability of mountain lodges, which serve as essential facilities for delivering mountain tourism services. To assess sustainability, expert decision making involving eight selected experts was employed. A hybrid approach combining the IMF SWARA (IMproved Fuzzy Step-wise Weight Assessment Ratio Analysis) method with Fuzzy Dombi Aggregation Operators was utilized to determine the weights of various sustainability criteria. The IMF SWARA method assigned initial weights based on expert input, which were subsequently adjusted using Fuzzy Dombi Aggregation Operators. The findings highlight the significance of two key criteria as per expert evaluations: the quality of the services offered (C21) and the preservation of natural resources (C15). To rank and evaluate the mountain lodges, the fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method was employed, ultimately identifying Zabrana (ML6) as the top-ranked mountain lodge. The validity of these results was confirmed through result validation and sensitivity analysis. This research contributes by providing insights into the current state of mountain tourism and offering guidelines for enhancing the overall mountain tourism experience through the integration of fuzzy methods.

In the context of sustainable buildings, an ecological study of building and insulating materials is critical since it may assist affirm or shift the path of new technology development. Utilising sustainable material is a part of the sustainable improvement. For this reason, material fabrication is the primary process for the energy usage and release of intense environmental gaseous. The fabrication of the insulation and building materials, as in every fabrication process, comprises an energy consumption of crude materials in addition to the pollutants’ release. In buildings, insulation is a relevant technological resolution for cutting energy usage. This study aims to assess the primary energy consumption and the environmental effects of the fabrication of building and thermal isolation materials by using a new hybrid MCDM model. The proposed new hybrid MCDM model includes Fuzzy FUCOM, CCSD and CRADIS methods. While the subjective weights of the criteria were determined with the fuzzy FUCOM method, the objective weights of the criteria were determined with the CCSD method. Construction materials were listed with the CRADIS method. According to the fuzzy FUCOM method, the most important criterion was determined as the CR3 criterion, while the most important criterion according to the CCSD method was determined as the CR1 criterion. According to the combined weights, the most important criterion was determined as the CR3 criterion. According to the CRADIS method, the material with the best performance was determined as Cement Plaster. The methodology used in this study is a novel approach therefore it has not been used in any study before. In addition, since the CRADIS method is a newly developed MCDM method, the number of articles related to this method is low. Therefore, this research gap will be filled with this study.

Production enterprises are enterprises that produce goods or services that aim to meet human needs such as machinery-equipment materials and labour. In order for a manufacturing enterprise to carry out its activities successfully, it must make the right choice when choosing its inputs. The correct execution of production activities and the selection of machinery, which requires high capital investments, also affect the efficiency of the enterprises, the correct use of materials and their costs. Therefore, it is an important decision for business managers to choose the right machine. At this stage, multi-criteria decision making (MCDM) methods are used for choosing the right machine. MCDM methods are methods used in the evaluation of alternatives using more than one criterion. In addition, the MCDM method is used in machine selection as well as in many areas. In this study, PSI, SV and MARCOS methods, which are among the MCDM methods, were used for peanut butter machine selection. First, the criteria and alternatives to be used for the peanut butter machine selection were determined by interviewing a peanut butter factory manager. In the study, while the criteria weights were determined, PSI and SV methods were used, while the machines were ranked with the MARCOS method. In addition, the MARCOS method was compared with other MCDM methods such as PIV, CODAS and WEDBA methods. After the rankings were found according to the methods, the relations between the rankings were examined using the Spearman Correlation method. The main purpose of the study is to determine the suitable butter machine for a peanut paste production factory. Contribution of this study to the literature PSI, SV and MARCOS methods were used together for the first time. In addition, no study has been found in the literature related to peanut butter machine. Therefore, this study is original and contributes to the literature.

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.