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The process of criteria prioritization and weighting is an important part of multiple attributes decision making. The most frequently applied multi-attribute decision-making weighting tools include analytical hierarchy process, stepwise weight assessment ratio analysis, factor relationship, and best–worst method. When policies are at the core of decision making, stepwise weight assessment ratio analysis method is the most efficient method for criteria evaluation. It involves two important steps: the first is to prioritize the criteria by consulting experts, while the second is the weighting process. This research seeks to extend stepwise weight assessment ratio analysis to improve the quality of the decision-making process by incorporating the reliability evaluation of experts’ idea into the first step. Such a component is absent from the first step in all other similar models. Thus, an extended version of stepwise weight assessment ratio analysis can be applied for such evaluation. To test the applicability and performance of the proposed method, a numerical example from an earlier study was used. The proposed version can replace the classic version in future studies as an improved method in decision-making area.

Construction is a sector that accepts innovations slowly. Selection of effective technological systems in construction is a complex multi-criteria task. Many decision-makers refuse innovations once faced with similar difficulties. The article presents an original approach towards a development of multi-criteria assessment and ranking technique for alternatives of technology in construction. The problem was solved using different well-known MCDM methods ELECTRE IV and MULTIMOORA. Three hybrid methods SWARA-TOPSIS, SWARA-ELECTRE III, SWARA-VIKOR were used to solve the same problem. Priority of considered alternatives was determined based on the average of alternatives performance rank. The article presents a practical case study on evaluation of different alternatives for public buildings refurbishment using typical and novel thermal insulation technologies for facades. Research results demonstrate that novel facades thermal insulation alternatives for facades have a higher performance level than commonly used ones.

Bioenergy is a kind of renewable energy that can potentially contribute to a broad spectrum of economic, environmental, and societal objectives and aid sustainable development. The assessment, management, and monitoring of the diverse bioenergy production technology alternatives are complex in nature and deliver different benefits due to the lack of precise and comprehensive data. Selection of an optimal bioenergy production technology (BPT) alternative is considered a complex multi-criteria decision-making (MCDM) problem that involves many incompatible tangible and intangible as well as qualitative and quantitative criteria. The procedure of defining and evaluating the weights of the criteria is an important concern for decision experts because the assessment and the final selection of the BPT alternative are carried out on the basis of the defined set of criteria. Intuitionistic fuzzy sets (IFSs) have received considerable attention due to their ability to handle the imprecision and vagueness that can arise in real-life situations. Thus, this study presents an integrated approach, based on stepwise weight assessment ratio analysis (SWARA) and complex proportional assessment (COPRAS) approaches, for the selection of BPT alternatives. In the integrated framework, criteria weights are determined by the SWARA procedure, and the ranking of BPT alternatives is decided by the COPRAS method using IFSs. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. To express the efficiency and applicability of the integrated framework, a BPT selection problem is presented using IFSs. In addition, this study involved sensitivity analysis with respect to various sets of criteria weights to reveal the strength of the developed approach. The sensitivity analysis outcomes indicate that the agricultural and municipal waste of biogas (S3) consistently secures the highest rank, despite how the criteria weights vary. Finally, a comparative study is discussed to analyze the validity of the obtained result. The findings of this study confirm that the proposed framework is more useful than and consistent with previously developed methods using the IFSs environment.

The Fourth Industrial Revolution is a complex phenomenon characterized by rapid technological changes that significantly transform social and economic landscapes, particularly in the workplaces. This transformation creates a gap between university curricula and the skill sets needed in the job market. If educational institutions do not adapt, they risk obsolescence, making effective curriculum revision essential. This study investigates the impact of the Fourth Industrial Revolution on education in engineering, focusing on its implications for human resource training. The Fuzzy SWARA approach was employed to assess the severity, likelihood, and detectability of educational challenges, while the Fuzzy ARAS method prioritized these issues. Key challenges identified include weaknesses in instructional content development and university incompatibility, which require immediate attention. The proposed model’s effectiveness was compared to the FMEA method, demonstrating its superiority. Strategies to address these challenges include enhancing curriculum quality, promoting exchange programs with leading universities, and fostering local technology manufacturing. The findings aim to improve methods for bridging the skills gap, ensuring students acquire the necessary skills for their current and future roles.

The selection and prioritization of suitable strategies to address the challenges to the successful operation and implementation of the bus rapid transit (BRT) system is a common problem faced by practitioners and decision-makers. Recent research has widely discussed the issue, but such assessments have remained limited in the city of Dar es Salaam, Tanzania context, where there are mobility difficulties. The present study addresses this research gap and identifies the most critical challenges to BRT implementation and operation, and recommends the most appropriate strategy for overcoming them. Seven strategies are defined. To prioritize these strategies, five criteria are determined. An integrated multi-criteria decision-making model is introduced. Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis based on the Bonferroni operator was used to determine the importance of the criteria. Measurement of alternatives and ranking according to compromise solution was applied to assess and rank the strategies. The results indicate that “frequent flooding at the Jangwani bridge bus terminal” and “long waiting time at bus stops” are the most critical challenges while the fourth alternative “strengthening the operation and management” is the appropriate strategy to be implemented for successful operation and implementation of the BRT system. After that, a five-phase sensitivity analysis is performed to observe the robustness of the proposed approach. The results indicate the flexibility and applicability of the proposed approach can address real-life problems. The proposed methodology in this work can be instrumental in assisting mass transit operators with the successful implementation and operation of the BRT system.

Selecting suitable locations for the disposal of medical waste is a serious matter. This study aims to propose a novel approach to selecting the optimal landfill for medical waste using Multi-Criteria Decision-Making (MCDM) methods. For better considerations of the uncertainty in choosing the optimal landfill, the MCDM methods are extended by spherical fuzzy sets (SFS). The identified criteria affecting the selection of the optimal location for landfilling medical waste include three categories; environmental, economic, and social. Moreover, the weights of the 13 criteria were computed by Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (SFSWARA). In the next step, the alternatives were analyzed and ranked using Spherical Fuzzy Weighted Aggregated Sum Product Assessment (SFWASPAS). Finally, in order to show the accuracy and validity of the results, the proposed approach was compared with the IF-SWARA-WASPAS method. Examination of the results showed that in the IF environment the ranking is not complete, and the results of the proposed method are more reliable. Furthermore, ten scenarios were created by changing the weight of the criteria, and the results were compared with the proposed method. The overall results were similar to the SF-SWARA-WASPAS method.

This paper is focused on solving the evaluation and selection of 3PL’s by applying multi-criteria decision-making methods. Nigerian Breweries, Nigerian Bottling Company (NBC), AG Leventis, Kobo logistics, and Flour Mills of Nigeria (FMN) were understudied. The main criteria on which evaluation is based were established: Cost, Service level, Financial Capability, Reputation and Long-term relationship. A combination of two quantitative models was adopted in the study. Relevant data were collected through an oral interview with managers and key decision-makers at the companies. SWARA was first applied to the collated data to determine the relative weights of the criteria. Afterwards, the TOPSIS was applied to the weights developed using SWARA and on the performance of the selected service providers. After the analysis, the best service provider was identified as supplier 2 while the worst was supplier 5.

Fossil fuels as the primary energy source create career opportunities, provide industries with vital raw material and energy resources, have harmful emissions to the environment and are also related to finite natural resources. They rely on them as the main source of energy supply is unsustainable. Sustainability assessment tools may be useful in developing a more sustainable scenario. However, the resiliency of nature is not taken into account in this linear assessment. The detrimental effect of these fuels on the environment during their life cycle would suggest transitioning from cradle-to-grave to the cradle-to-cradle lifecycle viewpoint. This study implements the Circular Economic (CE) in fossil fuel development to minimize the unsustainable effects and ensure the environment's resiliency. In this context, three different fossil fuels are assessed based on the CE model's proposed lifecycle phases to find out the most sustainable fossil fuel option. A case study is carried out in an industrial location with high-level decision-makers. CE criteria are evaluated based on the E-SWARA method to ensure the assessment's reliability at this critical step. Next, a novel MCDM method, MARCOS, is applied to this study. Based on the results, gas is the most sustainable energy generation plant in the intended region.

Attaining green environment for various machining processes has now caught the attention of many manufacturing industries. The input parameters involved in those machining processes are mainly responsible for achieving the desired performance as they are directly related to the process outputs. Hence, proper selection of the input process parameters becomes vital for having sustainable machining environment. In this paper, an integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods is presented to identify the optimal parametric combinations of two green dry milling processes. In the first example, cutting speed, depth of cut, feed rate and nose radius are treated as the input parameters, while power factor, electric consumption and surface roughness are the responses. On the other hand, in the second example, cutting speed, feed rate, depth of cut and width of cut, and surface roughness, active cutting energy and material removal rate are respectively considered as the input parameters and responses. Instead of considering equal weights, SWARA method assigns relative subjective importance to the responses based on the preference set by the decision-makers, while CoCoSo ranks the experimental trials from the best to the worst. The derived optimal parametric settings are finally analyzed using the developed regression equations. It is observed that SWARA-CoCoSo method outperforms the other popular optimization techniques in identifying the best parametric intermixes for the green dry milling processes for having improved machining performance with minimal environmental effect.

Internet of Things-enabled technologies help to collect data and make it understandable, especially in supply chain processes, thus minimizing the problems that may arise in supply chains. It is extremely important to support this process with Internet of Things-enabled technologies, especially in supply chains that are vulnerable to disruptions such as the dairy supply chain. Moreover, dairy supply chains are the type of supply chains where the most waste is generated; evaluating this waste is very beneficial to the circular economy. Therefore, monitoring data in dairy supply chains and using Internet of Things-enabled technologies prevent losses; it is critical to have Internet of Things-enabled circular dairy supply chains in operation. The aim of this study is to determine the success factors of Internet of Things-enabled circular dairy supply chains based on the various stages of these chains; we hope to match each dairy supply chain stage with a success factor of Internet of Things-enabled technology and determine a ranking for these factors. Hence, six success factors of Internet of Things-enabled circular supply chains are weighted for each stage of the chain; Internet of Things-enabled digital technologies are then matched with each stage of the chain, and the success factor is determined. The ranking of factors can then be drawn up through the integration of Step Wise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference Similar to Ideal Solution (TOPSIS). The outcome of this study will provide managers and policy makers with insights into Internet of Things-enabled circular dairy supply chains.