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In the apparel industry, suppliers play a significant role, directly affecting customer service levels and business profits. Integrating sustainable requirements into supplier selection not only aligns with global environmental goals but also enhances business performance, social responsibility and overall industry well-being. This study aims to design a multi-criteria model to evaluate and select the most sustainable suppliers in the fashion industry, trying to balance the conflicts in the set of sustainable development criteria.

The integration of analytic hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) offers advantages in sustainable supplier selection within the apparel industry. The AHP plays a crucial role in engaging multiple decision-makers with conflicting criteria to reach a consensus during the decision-making process. Conversely, the TOPSIS is used to compute alternative ratings. By simultaneously determining criteria weights and incorporating stakeholder preferences, hybrid models enhance decision-making strength and overcome limitations observed in classical multi-criteria decision-making techniques.

This research identified and classified 16 critical criteria impacting the selection of apparel industry suppliers, focusing on sustainable development. The criteria were weighted, providing a robust statistical foundation for the selection model. The results indicated that the most influential criteria were staff training, production capability, flexibility and practice of recycling. The proposed sustainable supplier selection model explains to decision-makers how criteria influence supplier ranking results compared to traditional models, supporting managers in making informed and sustainable supply chain decisions through continuous updates and enhancements.

This research provides new insight into the weighted impact of factors related to sustainable supplier selection in the apparel industry. The combination of a precise process and scientific knowledge will improve the quality of sustainable supplier selection.

The purpose of this study is to assess the performance of United Arab Emirates (UAE) Government e-services by using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodology, integrating insights from the balanced scorecard (BSC) framework across financial, customer, internal business and learning and growth perspectives.

Using the fuzzy TOPSIS method, this paper evaluate three e-services in the UAE against 12 criteria representing the balanced scorecard perspectives. Expert evaluation and sensitivity analysis are used to identify the most sustainable e-service alternative.

The study findings emphasize the importance of prioritizing customer-centric metrics, improving service reliability and efficiency, and investing in employee training to enhance e-government service performance in the UAE. Sensitivity analysis reinforces the robustness of our results and identifies key criteria influencing decision-making.

The data was collected only from experts in selected UAE Government departments. This may affect the generalization of the findings. Also, only three e-services were evaluated. Future studies could include various e-services not covered in this study and use different multi-criteria decision-making methods.

Prioritizing customer satisfaction: Priority should be given to customer satisfaction as it is a critical factor in evaluating e-services because of its importance. It also highlights the importance of considering user feedback and ensuring that e-services have a high level of friendliness and responsiveness to their needs. It follows that minimizing errors and ensuring quick and efficient transactions are crucial. Emphasizing reliability and transaction efficiency: Reliable services and transaction efficiency are also essential criteria for evaluating e-government services. This suggests that e-government services should be designed to minimize errors and ensure that transactions are completed quickly and efficiently. Managing IT costs: To deliver e-government services affordably, effective IT cost management is crucial. This emphasizes how crucial it is to effectively manage IT costs to guarantee the efficient delivery of e-government services.

From a customer perspective, adopting BSC can create a favorable customer attitude, encourage long-term customer support, and increase customer satisfaction and loyalty. These factors have significant social implications for UAE and expatriate individuals who are using such e-government services.

This study contributes to the literature by showcasing the applicability of the fuzzy TOPSIS methodology in evaluating UAE e-government service performance. By examining multiple perspectives of the BSC, this paper provide valuable insights into enhancing the efficiency and effectiveness of e-services in the UAE Government sector.

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 paper aims to clarify the relationship of various enablers of supply chain (SC) management like lean enablers, agile enablers and leagile enablers. It proposes modeling the enablers to find the most appropriate strategy or methodology for determining the lean enabled SC agility.

The paper proposed the fuzzy SWARA-WASPAS methodology for determining the role of lean in enabling the SC agility. Also in continuation the AHP methodology is applied to find the priority weightage and ranking of leagile enablers, and a comparative analysis is done to select the best approach among the above two methodologies so that it would be beneficial for all the stakeholders.

The paper provides the investigation and identification of 28 lean enablers, 11 agile factors which are highly responsible to affect any SC specially focusing of automobile sector. Apart from above 9 leagile enablers were also identified in the paper. Finally, the comparative analysis has been done in the results obtained by two methodologies – AHP & fuzzy SWARA-WASPAS – to determine the lean enabled SC agility, and also to which strategy should be adopted by the organizations as per the customized requirement of their SC.

The research limitation is that in future, there may be more number of lean, agile and leagile enablers which may be explored by different researchers in their findings, which may vary the output result accordingly. Though the research implications focus on having an advantage and impact on all aspects whether it is social, economic or commercial, there is a possibility of exploration of new and better decision-making tools and approaches in future. Also, the researchers are encouraged to test the proposed propositions further by taking case study of any automobile manufacturing organizations for the validation of the results.

The paper includes implications for the development of a powerful interrelationships between lean enablers, agile enablers and leagile enablers, which will help organization and the managers to take decisions regarding selection of best strategy appropriate to them to enhance their SC. This will also help new researchers of the field to take help of the research findings for exploring new and better optimization tools and techniques in future.

The findings of the research work will definitely help society, as the successful implementation of the lean, agile or leagile strategies in their SC system will leads to an increase in their efficiency and productivity, which will ultimately results in huge advantage to all the stakeholders directly or indirectly connected with the organization. The productivity dynamics cycle will also improve which will lead to more benefits to all in the market and achieving higher living index with better living standards.

This paper fulfills an identified need to study the various enablers of lean, agile and leagile SC, as well as their interrelationships. Also there is a need to understand the importance and effect of lean in enabling the SC agility.

The successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.

Based on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.

The study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.

This study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

Over the last 2 decades, supply chain sustainability research has become a highly dynamic and fruitful study area. This field has garnered significant attention due to its potential to reshape decision-making processes within supply chains. At the same time, the practical side of supply chain operations remains intensely competitive in today’s business landscape. Furthermore, the current academic research aims to outline effective strategies for achieving sustainability across supply chains, particularly in the manufacturing sector. In response to these challenges, this research has conducted an integrated multi-criteria decision-making approach to evaluate sustainable supply chain performance from the triple bottom line perspective, including financial, environmental, and social performance.

The initial stage involves selecting the crucial criteria (short-term and long-term) and alternatives for sustainable supply chain performance (SSCP) from experts and conducting an in-depth literature review. Initially, there were 17 criteria, but after a pilot test with co-authors and online discussions with experts, the number of criteria was subsequently reduced to 9. In the second phase, the Best-Worst Method (BWM) was applied to rank and prioritize the criteria. The third and final stage examined the causal relationship between the identified criteria, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique.

Based on BWM analysis results, the top three criteria in terms of prominence are: (1) return on investment (ROI), (2) product quality, and (3) manufacturing lead time. Out of the three alternatives, financial performance (FP) is the most crucial dimension for SSCP, followed by environmental performance (ENP) and social performance (SP). On the other hand, the DEMATEL approach showed that work health and safety (short-term criterion), asset utilization (long-term criterion), energy consumption (long-term criterion), waste disposal (long-term criterion), manufacturing lead time (short-term criterion), and on-time delivery (short-term criterion) are categorized within the cause group, while criteria such as return on investment (ROI) (long-term criterion), customer-service level (short-term criterion), and product quality (long-term criterion) fall into the effect group.

The proposed study has certain drawbacks that pave the way for future research directions. First, it is worth noting the need for a larger sample size to ensure the reliability of results, the potential inclusion of additional criteria to enhance the assessment of sustainability performance, and the consideration of a qualitative approach to gain deeper insights into the outcomes. In addition, fuzziness in qualitative subjective perception could be imperative when collecting data to ensure its reliability, as translating experts’ perceptions into exact numerical values can be challenging because human perceptions often carry elements of uncertainty or vagueness. Therefore, fuzzy integrated MCDM frameworks are better suited for future research to handle the uncertainties involved in human perceptions, making it a more appropriate approach for decision-making in scenarios where traditional MCDM methods may prove insufficient.

The proposed framework will enable decision-makers to gain deeper insights into how various decision criteria impact SSCP, thus providing a comprehensive evaluation of SSCP that considers multiple dimensions, such as financial, environmental, and social performance within the manufacturing sector.

The proposed study is the first empirical study to integrate both BWM and DEMATEL approaches to evaluate sustainable supply chain performance in the manufacturing context.

With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

The study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).

This research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.

The systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.

The key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.

Heritage building management serves as a potent catalyst for sustainability, yet it poses a distinctive set of challenges. Achieving a harmonious balance between conserving the building's historical and cultural value and ensuring modern functionality and safety remains a primary concern. The present work proposes a socio-technical approach to the development and use of a digital twin (DT) that will integrate social data related to the use of heritage buildings with building and environmental data.

The paper presents a logical and systematic joined-up management framework to the targeted heritage buildings, according to a “Whole Building” approach. Our approach is informed by the underpinning assumption that a heritage building and even more a heritage neighborhood is a socio-technical, complex and dynamic system, the change of which depends on the dynamic interconnections of materials, competences, resources, values, space/environment, senses and time.

A heritage dynamics approach is adopted to unfold the dynamic nature of heritage and to better inform decisions that can be made in the present and future, achieving people-centered and place-based heritage management. This proposition underlines the heritage transformation as a complex systemic process that consists of nonlinear interconnections of multiple heterogeneous factors (values, senses, attitudes, spaces and resources).

This paper presents a multi-level framework of DTs that interact hierarchically to comprehensively understand, assimilate and seamlessly integrate intricate contexts, even when faced with conflicting conditions from diverse cultural heritage entities. This paper outlines the importance of the iterative system dynamics (SD) approach, which enables adaptive management and ensures the resilience of cultural heritage over time.

Material selection, driven by wide and often conflicting objectives, is an important, sometimes difficult problem in material engineering. In this context, multi-criteria decision-making (MCDM) methodologies are effective. An approach of MCDM is needed to cater to criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase.

The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system. The intermediate pipes are used to join the components of the exhaust systems. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on tensile strength (TS), hardness (H), elongation (E), yield strength (YS) and cost (C). A hybrid methodology combining entropy-based criteria weighting, with the TOPSIS for alternative ranking, is pursued to identify the optimal design material for an engineered application in this paper. This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes. After that, the authors searched for and considered eight materials and evaluated them on the following five criteria: (1) TS, (2) YS, (3) H, (4) E and (5) C. The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes, on their performance and on the cost. In this structure, the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment. This essentially measures the quantity of information each criterion contribution, indicating the relative importance of these criteria better. Subsequently, the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative. The results show that SUS 309, SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.

The material matrix of the decision presented in Table 3 was normalized through Equation 5, as shown in Table 5, and the matrix was multiplied with weighting criteria ß_j. The obtained weighted normalized matrix V_ij is presented in Table 6. However, the ideal, worst and best value was ascertained by employing Equation 7. This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively. For the future work, it is suggested to select more alternatives and criteria. The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE).

The results provide important conclusions for material selection in this targeted application, verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.