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Risk decision matrix has widely been favoured by the researchers in the area of construction safety risk assessment. Although it provides the construction safety professionals with the final illustration of the risks magnitude, it suffers from major shortcomings, including inability to considering the importance of probability and severity, impaired analysis resulting from the use of raw numbers for ratings, and the limited range of classifications for assessing the risks. All these shortages give an impaired insight to the concerned parties, deteriorating the involved workers’ safety. As such, this paper aims to develop a novel Risk Assessment Model (RAM) through the integration of the Fuzzy Best Worst Method (FBWM) with the Interval-Valued Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IVFTOPSIS). Based on the application of RAM to a real-life case study, it was observed that the developed RAM contributes to the body of construction safety risk assessment in five unique ways: (1) computing the importance of the two risk parameters (i.e. probability and severity) using fuzzy-reference-based comparisons, (2) obviating the needs for having statistical data, (3) prioritizing the identified risks using the combination of interval-valued triangular fuzzy numbers with TOPSIS, (4) providing the safety analysts with wider ranges of classifications for conducting risk assessment, and (5) providing the safety professionals with appropriate evaluation strategies for controlling the analysed risks. The developed model in the study can be applied to any projects, giving a conclusive plan to the concerned safety professionals for adopting the further prudent mitigation measurements.

Application of models for supplier assessment and selection in the clothing industry remains relatively underexplored. To fill this gap, this research study introduces the following fuzzy hybrid models for evaluating and selecting suppliers for clothing manufacturing firms: fuzzy set theory, Analytic Hierarchy Process method–fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (AHP–fuzzy-TOPSIS), AHP–fuzzy-Weighted Sum Model (WSM), and AHP–fuzzy-Weighted Product Mean (WPM). Criteria weights were established utilizing these models, which were applied to identify the optimal supplier. A practical study was conducted within a clothing firm to evaluate the effectiveness of these fuzzy hybrid models. The main results reveal that the AHP–fuzzy-TOPSIS model outperforms the AHP–fuzzy-WSM and AHP–fuzzy-WPM models in selecting the optimal alternative. Indeed, this approach has the potential to be adapted to different industrial sectors, considering the specific criteria and conditions that govern their supplying processes.

Maturity models help organizations identify the processes of transformation and needs by analyzing the current situation of production systems. Within the scope of Industry 4.0, in this study, several maturity models are used. Five maturity models that are mostly applied are reviewed to determine the maturity model that a manufacturing company would assess by considering Industry 4.0. Seven properties of the models are compared and analyzed with the fuzzy TOPSIS (FTOPSIS) and intuitionistic fuzzy TOPSIS (IFTOPSIS) methods. Industry 4.0 maturity models, the number of dimensions, the number of maturity level, release date, content, the definition of measurement properties, assessment expenditures, and the assessment method are determined by the three decision makers according to the evaluation. As a result, the Impuls readiness maturity model is found to be the most suitable model in FTOPSIS and IFTOPSIS methods for a solar cell manufacturing company.

PurposeThis paper aims to select key criteria for sustainable vendor assessment and spare-parts supplies in the Indian petroleum refining sector using stochastic fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS).Design/methodology/approachThe criteria for sustainable vendor evaluation and selection are identified from the review of the literature and further; it is finalized using the Delphi method. Eight supply chain (SC) experts from the Indian petro refining sector were identified as having more than five years of experience and agreed to participate in this study (known as decision-makers (DM)). Five vendors supplying spare-parts are shortlisted from the market with the discussion and consent of procurement experts from petroleum refineries. Subsequently, criteria and vendors are rated based on relative importance in linguistic terms from the group of eight DMs. As ratings involve uncertainties in the decision-making, the SFTOPSIS method is applied to determine criteria weight and vendor ranking at a distinct significance level (α). The ranking of the vendors is obtained for sustainable supply of spare-parts in the Indian petro refining sector using the SFTOPSIS method.FindingsThe ranking of sustainable vendors is obtained through the integrated application of the fuzzy and stochastic approach to capture the uncertainties in the ratings of DMs. The sensitivity analysis is carried out at distinct confidence limits of a normal distribution to obtain a robust ranking of the vendors. In this paper, a case application of SFTOPSIS in the Indian petro refining sector is presented in which key criteria and the vendor ranking are found to be changing with confidence limit for sustainable vendor evaluation.Practical implicationsThe fuzziness and randomness in relative ratings collects from a group of DMs are taken in the proposed methodology. The distinct approaches are compared with changing significance-level under stochastic, fuzzy and deterministic TOPSIS to acquire robustness in the ranking. The proposed SFTOPSIS model can be useful to practitioners from the petroleum sector.Originality/valueThe originality of the paper contributes to an application of the SFTOPSIS method that is the extension of FTOPSIS in the petro refining sector of a developing country. The sensitivity analysis with distinct significance-level shows the uncertainties in the collected ratings from the DMs that supports robustness in the ranking. It might be helpful for SC professionals from the petro refining sector, who assess the rank of the vendors at different confidence limits for sustainable supply of spare-parts. Further research in the petroleum industry from emerging economies needs to be undertaken to broaden its scope and applicability.

The disordered urban growth that may favour the emergence of the Aedes aegypti mosquito in cities is a problem of increasing magnitude in middle- and high-income countries in the tropical part of the world. Currently, the World Health Organization (WHO) considers the control and elimination of Ae. aegypti a world-wide high priority as it is the main vector of many rapidly spreading viral diseases, dengue in particular. A major difficulty in controlling the proliferation of this vector is associated with identification of the breeding sites. The use of Unmanned Aerial Vehicles (UAVs) can be an efficient alternative to manual search because of high mobility and the ability to overcome physical obstacles, particularly in urban areas where it can offer close-up images of potential breeding sites that are difficult to reach. The objective of this study was to find a way to select the most suitable UAV for the identification of Ae. aegypti habitats by providing images of potential mosquito breeding sites. This can be accomplished by a Multiple-Criteria Decision Method (MCDM) based on an Analytical Hierarchy Process (AHP) for the evaluation of weights of the criteria used for characterizing UAVs. The alternatives were analyzed and ranked using the Fuzzy Set Theory (FST) merged with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The methodology is explained and discussed with respect to identification and selection of the most appropriate UAV for aerial mapping of Aedes breeding sites.

In developing countries such as India, Bengaluru city has experienced extraordinary growth in vehicle traffic on arterial roads, causing intersections to operate over capacity under mixed traffic conditions. The speed on urban streets in central business district areas is lower than 15 kmph during peak hours. One of the main routes connecting the airport to the central business district is also vulnerable to this problem. Therefore, providing an alternative route to divert traffic from the mainstream is critical. This study's main goal is to identify alternate routes from Majestic to the airport using remote sensing and a geographic information system, and then evaluate those alternates using the Fuzzy‐TOPSIS approach. This study has used the GIS Arc software application to select the alternative while taking numerous factors such as time, speed, distance, environmental impact, and economic feasibility into consideration. The video graphic and moving car observer methods have been used for the extraction of data for the inventory of the existing route, while the geo image processing tool has been used to analyze alternative routes in the Bengaluru metropolitan area (BMA). The analysis has shown that the alternative route has a lesser travel time of 2 h 30 min in public transit and 1 h 30 min in private mode. The future scope of the study is to compare the alternative and the most suitable routes to divert traffic. Another public transport route network could be developed to minimize the external incoming vehicles in BMA, particularly to reach the airport on urban roads under heterogeneous traffic conditions. The primary objective of this study is to select the alternative routes from Majestic to airport streets using RS and GIS and the evaluation of alternatives using the Fuzzy‐TOPSIS method. This study has used the GIS Arc software application to select the alternative while taking numerous factors such as time, speed, distance, environmental impact, and economic feasibility into consideration.

In order to protect the environment, manufacturing sectors have begun implementing a green supply chain (GSC) strategy. Governments are enacting increasingly stringent environmental regulations; consequently, industries must reduce the environmental impact of their supply chains. Our research investigates the barriers to implementing a GSC in the agriculture equipment manufacturing industries (AEMI). This research aims to discover and prioritize the barriers that impede the implementation of sustainable supply chain strategies in the AEMI. Through an in-depth literature review, contributions from experts, and empirical analysis, seventy-one barriers are identified across ten categories. The top barrier in each category is determined using the Delphi approach. The Fuzzy Technique for Order of Preference by Similarity to the Ideal Solution (F-TOPSIS) method creates an exhaustive framework that evaluates and ranks these barriers. The top five barriers are the lack of an environmental partnership with buyers and suppliers, the design complexity when reusing or recycling old goods or products, carbon emissions, paint shop emissions, lack of environmental education and training professionals that lack the necessary skills and less manpower available for the greening supply chain. This framework facilitates decision-makers to organize resources and create effective strategies for overcoming identified barriers. In addition, we proposed a blockchain IoT integrated architecture and strategies. This integrated architecture and strategies will help to mitigate all GSC barriers. It also increases the supply chain's transparency, traceability and effectiveness, fostering sustainability practices and reducing environmental impacts. Blockchain and IoT facilitate real-time data collaboration, computerized transactions and the implementation of smart contracts, thereby enhancing cooperation, trust and collaboration among stakeholders. Implementing GSC practices enables manufacturers to reduce waste and increase productivity, thereby saving funds. In addition, adopting sustainable practices improves these industries' reputation and brand image among environmentally conscious customers, investors and other stakeholders.

Supplier performance evaluation is a decision-making problem that involves quantitative and qualitative metrics. Although several models that allow the use of linguistic terms such as \"low\" and \"high\" to evaluate suppliers, none of them enables the application of linguistic expressions, which is especially useful when decision makers hesitates to express their evaluations. This study proposes a model based on the Hesitant Fuzzy TOPSIS method to support the supplier performance evaluation. A pilot application was carried out in an automotive company considering 8 suppliers and 10 criteria. When compared to previous similar approaches, the proposed model presented the following advantages: it enables the use of linguistic expressions to assess the supplier performance in each criterion; it groups suppliers with similar levels of performance to develop appropriate management actions; and does not limit the number of criteria and suppliers evaluated.

The main objective of the study is to review different types of multi-criteria decision-making (MCDM) problems that are analysed by using the fuzzy TOPSIS method and its different variants. An effort has been made to review different studies in which either the TOPSIS method is used or its extensions have been developed to analyse any real-life MCDM problem. A total of 184 research papers were carefully selected and reviewed from 83 reputed journals published by Elsevier, Springer, Wiley, Taylor and Francis, and others from 1981 to the first quarter of 2023. These papers are selected on the basis of different kinds of applications in the fields of mathematics, engineering, science, environment, technology, management, business, etc. The selected papers are further categorised based on the application areas, publication year, contributions of the countries, authors, journals, and type of research. This study will give an idea of different types of TOPSIS methods, their extensions, and applications, along with recent trends in diverse research fields. Based on the findings, it is observed that in recent years, the number of papers in which TOPSIS and its extensions have been used has increased exponentially.

PurposeThe purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.Design/methodology/approachThe framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.FindingsThe results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.Originality/valueDisruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.