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The purpose of this study is to evaluate the sustainability performance of modular construction from a life cycle perspective. So far, the sustainability performance of modular buildings has been explored from a life cycle viewpoint. There is no comprehensive study showing which material is the best choice for modular construction considering all three sustainable pillars. Therefore, a life cycle sustainability performance framework, including the three-pillar evaluation framework, was developed for different modular buildings. The materials are concrete, steel and timber constructed as a modular construction method.

Transitioning the built environment to a circular economy is vital to achieving sustainability goals. Modular construction is perceived as the future of the construction industry, and in combination with objective sustainability, it is still in the evaluation phase. A life cycle sustainability assessment, which includes life cycle assessment, life cycle cost and social life cycle assessment, has been selected to evaluate alternative materials for constructing a case study building using modular strategies. Subsequently, the multi-criteria decision-making (MCDM) method was used to compute the outranking scores for each modular component.

The calculated embodied impacts and global warming potential (GWP) showed that material production is the most critical phase (65%–88% of embodied energy and 64%–86% of GWP). The result of embodied energy and GWP shows timber as an ideal choice. Timber modular has a 21% and 11% lower GWP than concrete and steel, respectively. The timber structure also has 19% and 13% lower embodied energy than concrete and steel. However, the result of the economic analysis revealed that concrete is the most economical choice. The cost calculations indicate that concrete exhibits a lower total cost by 4% compared to timber and 11% higher than steel structures. However, the social assessment suggests that steel emerges as the optimal material when contrasted with timber and concrete. Consequently, determining the best single material for constructing modular buildings becomes challenging. To address this, the MCDM technique is used to identify the optimal choice. Through MCDM analysis, steel demonstrates the best overall performance.

This research is valuable for construction professionals as it gives a deliberate framework for modular buildings’ life cycle sustainability performance and assists with sustainable construction materials.

Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making.

In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method.

The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market.

In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness.

The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

The existing literature reflects that the connection between enablers of Industry 4.0 (I4.0), Supply Chain (SC) sustainability and reliability is understudied. To cover this gap, the purpose of this study is to identify and benchmark the enablers of I4.0 for SC sustainability to build a Reliable Supply Chain (RSC).

This study benchmarks the I4.0 enablers for SC sustainability for building a RSC and analyses them with a multi-method approach. The identified potential enablers are validated empirically. A multi-method approach of Analytical Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-II) was used to investigate the influence of the identified benchmarking enablers and develop an interrelationship diagram among the identified enablers.

This study benchmarks the potential enablers of I4.0 to achieve high ecological-economic-social gains in SCs considering the Indian scenario. Digitalization of the supply chain, decentralization, smart factory technologies and data security and handling are the most prominent enablers of I4.0 for SC sustainability to build a RSC.

The findings from the study may benefit managers, practitioners, specialists, researchers and policymakers interested in I4.0 sustainability applications.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This study focuses on the selection of armed unmanned aerial vehicles (AUAV), which have recently taken an important place on the world agenda, are used effectively in the defense industry and change the war systems of countries. This study aims to select the most suitable armed AUAV by using and comparing multi-criteria decision-making (MCDM) methods.

There are various types of (unmanned aerial vehicles) UAVs, and some of them are Armed UAVs. This study used the criteria obtained from the market and previous UAV studies and ranked/selected various AUAVs produced in line with the determined criteria. The AHP method was used to prioritize the criteria, and the PROMETHEE method, a powerful ranking method, was used to rank/select the alternatives.

By the expert judgments, the payload capacity (28.2%) criteria took first rank by far as the most important criteria. The AUAV alternatives are listed as 1-6-5-2-7-3-4, respectively.

UAVs around the world have been showing significant and rapid developments recently, and those concerned closely follow developments in this field. Depending on the development of the aviation industry and technology, UAVs provide services to individuals or institutions in various ways for civil or military use.

The difference from similar studies is the research of Armed UAVs. Sensitivity analysis was performed and alternatives were analyzed by their weights. Comparisons were made using the MEREC, LOPCOW, and ELECTRE methods.

This study aims to determine the order of importance of blockchain technology gains for foreign trade management. The second aim of the research is to select the most suitable blockchain type for the foreign trade process.

The data required for the research analysis were collected through comparison matrices scored by foreign trade experts. The data were analyzed with the analytical hierarchy process method, and the gains of blockchain technology for the foreign trade process were prioritized. Finally, the most suitable type of blockchain technology was selected with the PROMETHEE method.

As a result of the study, subject matter experts perceived that the most critical blockchain gain for the foreign trade process would be cost savings. In addition, it has been observed that the general trade process differs compared to the logistics and payment processes. Finally, open blockchain was the most suitable blockchain type for foreign trade processes.

In this study, the positive gains of blockchain technology are considered. In future studies, it is recommended to consider the existing negative factors to be able to use blockchain technology in foreign trade processes. In addition, it is suggested to conduct a study by dividing foreign trade into two, import and export.

All stakeholders who want to integrate blockchain technology into their foreign trade processes, including foreign trade companies, software developers, policymakers and international institutions, can benefit from the results of this study. A blockchain technology software created for foreign trade management can be shaped according to the results of this study.

Although the gains that blockchain technology provides to foreign trade processes are frequently emphasized in the literature, which gain would be greater has not been examined. The lack of an answer to the issue of how blockchain technology should be designed for foreign trade processes has been the missing part of the relevant literature. To the best of the authors’ knowledge, this study is the first experimental study in the literature that prioritizes blockchain gains and selects the appropriate blockchain type for foreign trade processes.

Although disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.

The epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.

The effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.

In this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.