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The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability.

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes.

The goal of this study is to present a DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance. The study demonstrates a real-life use of the proposed model, mainly designed for hospitals. Data envelopment analysis enhanced with fuzzy analytic hierarchy process are collectively utilized to quantify the data and structure the model in decision-making. The juxtaposition of the two methods is used to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid model provides several benefits, one of which is the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model.

In the World Air Quality Report 2022, India ranked 8th in terms of average PM2.5 concentration globally with a value of 53.3 μg/m3, and a number of the 7 most polluted cities are located in India out of 50 top cities. The presence of poor air quality in mining cities has a positive relationship with mining activities, and this scenario persists in different cities of Jharkhand state. In this study, we draw the API (Air Pollution Index) with the help of the fuzzy analytical hierarchy process (FAHP) across the state of Jharkhand by considering parameters such as CO, O3, NO2, SO2, and PM10 and the relationship between Sentinel 5P aerosol optical depth (AOD) and CPCB published ground data of PM10 (i.e., monthly and seasonal) was also explored. The outcome depicts that a high concentration of API is dominant along the north-eastern part of the state due to the intensive mining activity along this part of the state, and the trend of concentration of PM10 in the air is continuously increasing from 2012-2018 as per the GOJ (Government of Jharkhand) report. This study will give insight into the pollution scenario in the mining-dominated state of Jharkhand, and along with that, it will also spread awareness of the impact of mining activities on the atmosphere.

Flash floods are among the most dangerous natural disasters, as they cause widespread damage to property and loss of lives, especially in desert and mountainous areas. This study aims to evaluate the Wadi Habban basin to be exposed to the risk of sudden floods using remote sensing data, geographic information systems (GIS), and the pyramid analysis methodology (AHP). The spatial distribution of hazardous areas has been evaluated through the weight and reclassification of ten main criteria that include geomorphology, elevation, slope, rainfall, drainage density, distance to watercourse, land use and land cover, soil texture, Topographic Wetness Index (TWI), and Stream Power Index (SPI), were integrated into a Geographic Information System (GIS) platform. The analysis classified the basin into five risk categories: 4.3% (very high), 10.2% (high), 29.4% (medium), 42.2% (low), and 13.7%. (very low). The results revealed that 14.5% of the basin area is exposed to severe and high floods, which confirms the necessity of protective strategies, such as constructing flood barriers near vulnerable valleys and enhancing infrastructure and drainage systems. These results provide essential insights for disaster preparedness and infrastructure development, serving as a significant reference for policymakers and planners to enhance flood risk management and mitigate susceptibility in analogous settings.

Landslides and instability slopes are major risks for human activities which often lead to losing economic resources and damaging properties and structures. The main aims of this study are identifying the underlying effective factors of landslide occurrence in Bijar, Kurdistan Province, and evaluating the regions prone to landslide to prepare the susceptibility map using the logistic regression (LR) and analytical hierarchy process (AHP). At first, using field surveys, questionnaires, geological and topographic maps and reviewing the related studies, ten effective factors including the elevation of sea level, slope inclination, slope aspect, geology, distance from the linear elements (fault, road, and river), precipitation and land use were recognized. Then, they were processed using ARC GIS 10 and ILWIS 33. The dependent variable included 144 of slopes prone to landslide selected across the region as the landslide data (code 1), and also 144 stable landslide slopes were randomly selected as landslide free data (code 0). The results of the evaluation showed that LR model with PCPT index equals to 83.4; −2LL index equals to 229.226; and ROC index equals to 98.5% and landslide susceptibility map based on SCAI index had high verification in the case study. Therefore, 75.489% of the area had very low susceptibility, 10.037% low susceptibility, 3.628% moderate susceptibility, 4.062% high susceptibility and 6.784% very high susceptibility. Based on the preferences of the AHP method, the weighting of selected parameters was logically performed so that the parameters could be arranged according to their priorities. The results of the AHP model showed that 3.4% of the area had very low susceptibility, 30.43% low susceptibility, 46.68% moderate susceptibility, 18.14% high susceptibility, and 1.33% very high susceptibility.

Global warming, climate change, and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution, greenhouse effect, poor working conditions, child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system, actor, objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the inter-relationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance, reciprocity, fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance, social performance and channel efficiency of the supply chain, thereby ensuring sustainability and socio welfare of all the supply chain.

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

Purpose – The purpose of this paper is to propose a multi-criteria supplier selection model using fuzzy analytical hierarchy process (FAHP) approach for a leading automobile company in India. Design/methodology/approach – FAHP approach followed by a sensitivity analysis has been used. Findings – In this study, a FAHP-based supplier selection model is proposed to provide useful insights in choosing appropriate suppliers in dynamic situations in order to enhance long-term relationship with them. Practical implications – This study proposes a supplier selection model for an automobile industry which often faces heterogeneous supply environments. This model may have a high acceptability where a large number of suppliers are available to supply the materials or provide the services. As analytic hierarchy process is the most widely used methodology for supplier selection, however, it becomes less efficient in case of inconsistencies observed in the data. However a FAHP-based approach may overcome this difficulty. Originality/value – It contributes to supplier selection process and points out the importance of supplier selection problem, especially in the context of multi-criteria decision-making in Indian scenario.

Due to the ever-increasing water scarcity problem across the globe, the treatment of wastewater is an important public health and socio-economic issue. Treating wastewater through proper technology is vital to protect the ecosystem from unsafe and contaminated matter available in wastewater. Identification of suitable wastewater treatment technologies is a complex Multi-Criteria Decision Making (MCDM) problem since it includes many conflicting assessment criteria. The objective of the paper is to construct an integrated model using the Analytical Hierarchy Process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating wastewater treatment technologies (WWTTs). AHP is applied to calculate criteria weights, and the VIKOR method is applied to prioritize and select the best WWTTs. The proposed model is applied to selecting the best WWTT among four alternatives and seven criteria. It is found that the proposed model yields better results when compared with other MCDM solutions.