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A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables. [ABSTRACT FROM AUTHOR]

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India. [ABSTRACT FROM AUTHOR]

Nowadays, to achieve sustainable development and avoid devastating impacts on the environment, India is making rapid and broad changes to green energy technology. In order to provide long-term energy security with lower emissions, renewable energy sources are essential. It is well known that renewable energy technologies, or RETs, have the capacity to significantly meet the demand for electricity while lowering pollution levels. The nation has set up an ecologically friendly energy route in recent years. In this paper, we apply multi-criteria decision making (MCDM) models to determine the best renewable energy technology for India. We use the MULTIMOORA model to identify the best technology under the pythagorean neutrosophic fuzzy set (PNFS) and the FUCOM to obtain the criteria weights. In this study, we investigate sources of renewable energy using the newly developed idea of the PNFS and recommend the best renewable energy source for India. [ABSTRACT FROM AUTHOR]

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Site selection for an institute or a university is a challenging task. The selection of sites for setting up a new university depends on multiple criteria. In backward, under privileged area people's perception towards the co-educational universities and women universities are different. Poor families with their conservative mentality possess inhibitions while sending their girl child to co-educational universities as they have concerns about safety, security and family honor. Hence many attributes which are not so important for co-educational universities are more pertinent for women university. In this research paper, we have considered a model for selecting women's university sites in different backward locations in the state of West Bengal, India. This model incorporated different types of uncertainty related to site selection. Ten important criteria are chosen for the selection of sites. To capture the uncertainty of the problem, trapezoidal neutrosophic numbers are used along with the Multi-criteria Decision Making tool Analytic Hierarchy Process (AHP) for obtaining criteria weights. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are applied for ranking of the sites. Comparative and sensitivity analyses are conducted to check the steadiness of the techniques used. [ABSTRACT FROM AUTHOR]

Soil erosion is the inherent and destructive threat affecting agricultural production and livelihood of million mouths. The increased frequency of floods and land use/land cover changes has made Upper Jhelum Sub-catchment susceptible to soil erosion risk. Morphometric based watershed prioritization for soil erosion risk may help in sustainable management of natural resources. Thus, this paper endeavors to prioritize watersheds of Upper Jhelum Sub-catchment in India based on morphometric parameters for soil erosion risk using geospatial techniques. Weights to the morphometric parameters were assigned through a multi-criteria decision method. The watersheds in the Sub-catchment have been categorized into low, medium, high and very high priority classes based on prioritization ranks that were determined by computing the compound value for the soil erosion risk, based on prioritization ranks obtained through compound value for the soil erosion risk. The results revealed 1E1D3 and 1E1D8 watersheds accorded very high priority. The watersheds namely IE1D2 and IEID4 were found under high priority. Medium priority for soil erosion risk was determined in IEID5 and IED7 watersheds while 1E1D1 and IE1D6 watersheds were identified for low priority. The study calls for implementing soil conservation practices in the Sub-catchment. The Sub-catchment can be made less hazardous for the soil erosion risk by implementing contour farming, building check dams, terrace farming, afforestation and limiting large scale overgrazing. The findings of this study may offer valuable insights for stakeholders for conservation of soil resource. The approach utilized in the study may be linked with soil loss estimation for effective conservation of natural resources in further future studies. [ABSTRACT FROM AUTHOR]

Identification of hospital sites and their ranking is important for the planning and development of any country's health infrastructure. The site selection problem is a typical multi-criteria decision making problem involving multiple stakeholders and their interests. Multi-Criteria Decision Analysis (MCDA) is a promising approach to solve a location-based problem due to the constitution of various criteria involved in decision making. In this research, eleven criterion are chosen which are classified under three main criteria; socio-economic, geographical and environmental. This research aims to identify the appropriate MCDA method for the selection of a new hospital sites. Here, two MCDA methods named Analytical Hierarchy Process (AHP) and Fuzzy AHP (FAHP) are used. Further, Geographical Information System (GIS) based MCDA methodology is proposed in this paper. The results obtained with both AHP and FAHP methods are compared. This comparison is based on criterion rankings, proposed hospital locations and sensitivity analysis. The main difference in results is shown in the result of sensitivity analysis in which constant variation in site ranking is obtained when weight change analysis is performed using AHP. The FAHP result shows only one variation in site ranking after a change in weight from +10 to +20%. The result suggests that FAHP may be a better approach to the hospital site selection problem. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a novel extension of a very recently developed multicriteria decision making (MCDM) algorithm known as the preference ranking on the basis of ideal-average distance (PROBID) method in a picture fuzzy (PF) environment. We use the full consistency method (FUCOM) with picture fuzzy numbers (PFNs) for deriving the criteria weights. We attempt to apply our proposed model for addressing a real-life complex decision making problem in social science research that gets influenced by the dynamics of discrete human behaviors. We compare eight popular video conferencing (VC) tools used for teaching-learning and meeting purposes in India using our novel integrated multicriteria decision making (MCDM) framework of FUCOM-PROBID with PF information. The criteria have been derived using the theoretical foundation of usability and user experience (UX). Based on the opinion of the decision makers (DM) or users who took part in the study, we find that ease of operations, compatibility with multiple systems and devices, quality of the voice, and video transmission and features are given more emphasis while Zoom, Microsoft Teams, and Google Meet are found to be preferable options to the users. The result of the proposed model shows stability and robustness as evident from the validation test and sensitivity analysis. [ABSTRACT FROM AUTHOR]

This current research paper measures the performance of Indian private sector banks through various multi-criteria decision-making (MCDM) techniques. To measure the performance of the banks the data about various criteria such as profit after tax, borrowings, advances, adjusted EPS, enterprise value, and NPAs from the Annual reports of the banks were extracted. The MCDM techniques, SDV (standard deviation) CRITIC (CRiteria Importance Through Intercriteria Correlation), ARAS (Additive Ratio Assessment), MOORA (Multi-objective Optimization on the basis of Ratio Analysis) are applied to analyze the data and measure the performance of the banks. In MCDM techniques, different methods provide different weights of the criteria, and also different ranks are obtained by different methods. Sensitivity analysis was carried out by measuring the criteria weights by SDV and CRITIC and the alternatives are ranked using two MCDM techniques, ARAS and MOORA. The results of the study show that among the private banks, HDFC created a benchmark and leading while Yes bank has shown poor performance on the basis of annual reports of 2020. [ABSTRACT FROM AUTHOR]

An approach has been made in this study to delineate the groundwater potential zones of the Varuna river basin, Uttar Pradesh, India, using the analytical hierarchy process (AHP) and multi-influence factor (MIF) techniques. The present groundwater estimation exhibits an increase in the draft (10%) due to expansion in population, agricultural extent, and industrialization, which ultimately causes water table depletion. This backdrop justifies the need for this particular analysis in the multilayer aquifers of the central alluvial zone. The shallow aquifers are silty and unconfined, whereas the deeper aquifers are coarse, sandy-gravelly, and semi-confined. Basement faults and highs often control the thickness of aquifers in the subsurface. The study considered an integrated approach of AHP and MIF methods with remote sensing and GIS approaches. Various themes (land use/land cover (LULC), soil type, geology, elevation, slope, rainfall, normalized difference vegetation index (NDVI), drainage density, recharge rate, groundwater depth) determined by considering different conditioning factors and eventually employed for computation of groundwater potential index (GWPI) and classified for identifying the groundwater potential zones (GWPZ). Two methods applied to capture the results in a more tangible form as the AHP model works on building a pair-wise comparison matrix to relate conditioning factors to each other. Still, the MIF model considered interrelations among the conditioning factors. The GWPZ of the study area generates with overlay weighted sum method by integrating all thematic layers. The resulting groundwater potential index map is categorized into three groundwater potential zones, namely good, moderate, and poor. Ultimately, by constructing the receiver operating characteristic (ROC) curves for both the groundwater potential models, determine the efficiency of performances and the GWPZ map validated using yield data collected from wells scattered over the study area. The findings of the present paper have important implications for ensuring exploration and sustainability groundwater plans in that particular area. [ABSTRACT FROM AUTHOR]

The long-term evolution of multi agent multi criteria decision making (MCDM) and to obtain sustainable decision a novel methodology is proposed based on evolutionary game theory. In this paper multi agent MCDM is represented as an evolutionary game and the evolutionary strategies are defined as sustainable decisions. Here we consider the problem of decision making in Indian Tea Industry. The agents in this game are essentially Indian Tea Estate owner and Indian Tea board. The replicator dynamics of the evolutionary game are studied to obtain evolutionary strategies which could be defined as sustainable strategies. The multi agent MCDM in Indian Tea Industry is considered under different socio-political and Corporate Social Responsibility scenario and groups of Indian Tea Industry. Again, the impacts of imprecision and market volatility on the outcome of some strategies (decisions) are studied. In this paper the imprecision on the impact of the strategies are modelled as fuzzy numbers whereas the market volatility is taken into account as white noise. Hence the MCDM problem for Indian Tea Industry is modelled as a hybrid evolutionary game. The probabilities of strategies are obtained by solving hybrid evolutionary game and could be represented as a Dempster-Shafer belief structure. The simulation results facilitate the Decision Makers to choose the strategies (decisions) under different type of uncertainty. [ABSTRACT FROM AUTHOR]

Sustainability of sheep and goat production systems is a significant task for any organization that aims for long term goals. Housing and feeding selection for goat farming is the most important factor that should be considered before setting out the goat farm. The decision framework of housing selection should include environmental, social and human impact for the long term, rather than on short-term gains. In the selection process, various parameters are involved such as housing materials, area to prevent water stagnation, ventilation, enough space for the pen and run system, space for feeders and water troughs. Those parameters highlight the quality of housing in relation to aspects of traditional breeding provided by the organizations. However, the process of housing selection is often led by hands on experience which contains vague, ambiguous and uncertain decisions. To overcome this issue it is necessary to frame an efficient algorithm which could remove the entire barrier in the decision making process. In this paper we propose a neutrosophic multi-criteria decision making framework that combines the TODIM method with the SD- HNWA operator. The resulting multi-criteria decision analytical MCDM framework is then applied in selecting the best system in housing and feeding of goats at a mixed farming agrofarm in India. The proposed approach allows us to establish the neutrosophic based value function that measures the degree to which one alternative is superior to others by calculating accurate number of information in pair wise comparison in terms of gain and loss. The outcomes of the proposed method are compared with the use of the TOPSIS method to prove its efficiency and validate the results. [ABSTRACT FROM AUTHOR]

• An optimize ensemble machine learning model has been proposed for breast cancer classification on the basis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Crow Search algorithm. • The proposed model improves the overall performance metrics consists of accuracy, recall, precision, AUC, F1 score, Kappa and MCC. • Even with the limited number of samples, the proposed model performs better. In this paper, we propose a novel framework for breast cancer classification using optical coherence tomography (OCT) images. As the incidence of breast cancer in India continues to rise, early detection is essential for increasing survival rates. For precise classification, an efficient classifier model is required. Due to the variety of datasets and performance metrics, a single classifier may not be sufficient. Thus, we propose an efficient ensemble classifier based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Crow Search Algorithm (CSA). TOPSIS, a technique for Multi-Criteria Decision Making (MCDM), ranks and selects the ensemble's base classifiers. The CSA fine-tunes the weights of the base classifiers. In addition, we employ SHAP (SHapley Additive exPlanations) values to visualize feature attributions at the observation level, thereby facilitating model interpretation and comprehension. The proposed classifier performs admirably on the experimental testing dataset, with values of 0.921, 0.921, 0.923, 0.921, 0.846 and 0.846 for precision, recall, F1-score, Kappa, and MCC, respectively. This model's ultimate goal is to reduce reliance on skilled pathologists, reduce interobserver variability, and speed up the processing of breast tissue assessments. [ABSTRACT FROM AUTHOR]

Developing groundwater potential zones (GWPz) is important to determine the realistic groundwater availability scenario and to formulate plans for its optimal utilization to achieve groundwater sustainability. This investigation aimed to delineate GWPz in the Nand Samand Catchment, Rajasthan, India, utilizing an integrated method of remote sensing, geographic information system, and analytical hierarchy process (AHP). The present study used ten thematic layers, viz., lineament density, well recharge, geomorphology, soil, topographic elevation, slope, rainfall, transmissivity, the post-monsoon water level in the well and land use/land cover for the GWPz delineation. The delineated GWPz in the research area were classified as 'good (G),' 'moderate (M),' 'poor (P)' and 'very poor (VP).' Results showed that 'good' GWPz constituted about 21.23% of the catchment area. The southeast area and small patches of the western side of the research area have 'moderate' groundwater potential, covering 53.18% of the total area. Approximately 23.77% area was found with poor GWPz. In the research area, 1.82% area represents very poor groundwater potential. In the present study, the GWPz map was validated using sixteen randomly selected wells yield data (liters per day), and an accuracy of 81.25% was found with the delineated GWPz map. Furthermore, the receiver operating characteristics curve was also used for the validation, indicating a satisfactory accuracy prediction (AUC = 0.889). Validation by both methods justifies the efficiency of the AHP technique for the groundwater potential zoning. Finally, this work establishes a viable method for assessing the potential availability of groundwater, which will be helpful in the future for the planning and managing groundwater resources. [ABSTRACT FROM AUTHOR]

The hydrologic cycle's fundamental component, evapotranspiration, is a crucial parameter for research on climate, hydrology, and water use in agriculture. Efficient water management in agriculture requires an accurate assessment of reference evapotranspiration (ETo). The Food and Agriculture Organization recommended Penman–Monteith (PM) approach is among the most precise models for computing ETo. Nevertheless, this technique requires a comprehensive climate dataset, typically only accessible at a few meteorological observatories. As a result, estimating ETo using a small climatic dataset may be an alternative for water management aspects. Thus, in the current study, 12 temperature, ten radiation, and seven mass transfer-based models were assessed using 31 years of meteorological data (1990–2020) and compared with the traditional PM approach in the semiarid environment of Central Delhi, India. Moreover, ranking different models in data-limited situations was attempted using multi-criteria decision-making (MCDM) techniques viz.,TOPSIS (technique for order performance by similarity to ideal solution) and entropy. Results showed that the Priestley–Taylor (PRTY) and Blaney–Criddle (BLCD) models were found to be the most appropriate alternatives to the PM model, with Pearson's coefficient of correlation (r) values of 0.93 and 0.96, respectively, and mean absolute percentage error (MAPE) values of 13.30 and 21.11%. According to the performance evaluation indices, the performance of the radiation-based models was superior to that of the temperature and mass transfer models. Nevertheless, this ranking would aid in precisely estimating ETo with minimal data, resulting in efficient irrigation scheduling and water resource management. [ABSTRACT FROM AUTHOR]

The popularity of mutual funds, which are necessarily portfolios, has been drawing more attention from the people of India over the last three decades. In a mutual fund, one can invest his or her money in the securities of different sectors traded mainly in the stock exchange markets to get expected return bearing tolerable risks. A mutual fund other than the passive mutual funds is directed by an active fund manager. The performance of a mutual fund depends on the shares and securities of different companies it contains and the fund manager's performance also. Risk and return can be measured based on different criteria. Before investment, one should select such a mutual fund that can fulfill his or her anticipation as much as possible within the risk–return skeleton. Therefore the selection of a mutual fund is rigorously a multicriteria decision making. This paper has considered five open‐ended, large‐cap, direct, suspended sales mutual funds for the research work. In the first stage, we have shortlisted the more crucial criteria comparatively from a list of criteria with the help of the Fuzzy Analytic Hierarchy Process. In the second stage, we have applied the Pythagorean fuzzy Gray Relation Analysis approach to determine the weights of shortlisted criteria as well as the rank of those mutual funds. Finally, a comparison has been drawn between the present model and the Pythagorean fuzzy Interactive Multicriteria Decision‐Making model, to show the effectiveness of the present model. [ABSTRACT FROM AUTHOR]

The world is facing immense pressure due to expanding discourse on resource security. The study is conducted in the water–energy–food (WEF) Nexus context to understand the inter-relatedness of the sectors for sustainable development, eliciting stakeholders' perspectives. Banda District lies in Bundelkhand region of India. It is drought-hit and lacks basic infrastructure and hence has been selected for this research. It is facing challenges stemming from WEF resources insecurity which has triggered mass migration, farmer suicide, unemployment, and indebtedness. In this exploratory research study, nineteen parameters are analyzed using multi-criteria decision analysis and weighted index method to calculate the Stakeholder opinion-based development index (SODI). GIS is used for spatial mapping to characterize the grassroots disparity highlighting the perspective and expectations of the 534 surveyed households. The SODI of Banda District is calculated to be 10.157, which lies in the average category. Most parameters (78.95 percent) fall below the average category, showing that the region is backward and the spatial mapping helps in identifying lacunae in the development of the District. The novelty of this research lies in using the analytical method to examine qualitative parameters that highlight stakeholders' perspectives and usage of unique methodology in formulating the index to study the region holistically for sustainable development. It is concluded with recommendations based on findings using a system-based approach. The modified structural approach of the Stakeholder Opinion-based Development Index and research methodology can be employed for profiling and analyzing the Districts of the same characteristics in and out of India. [ABSTRACT FROM AUTHOR]

The present work has three broad objectives. First, it intends to develop a novel objective measurement framework for comparing corporate social responsibility (CSR) performance. The extant literature shows a plenty of work has been done for exploring the benefits of CSR. But there is a limitation of work that provides a multi-criteria based objective measurement of CSR performance in financial terms. The second objective of the current work is to examine the impact of the recent COVID-19 on CSR performance of Indian firms. Thirdly, the present study proposes a new hybrid multi-criteria decision making (MCDM) framework with multiple normalizations using two recent models such as Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Proximity Index Value (PIV) methods. In line with the objectives set, the ongoing work defines a new set of indicating variables to compare CSR performance from the perspectives of major stakeholders such as customer, society, government, employee, environment and shareholders. Top 20 manufacturing firms listed in the Bombay Stock Exchange (BSE)-100 in India have been selected for comparison. The study period is considered as FY 2019-20 (before pandemic) and FY 2020- 21 (after pandemic). It is seen that the firms having higher market capitalization did well in their CSR performance. We observe that the overall CSR performance has not undergone any substantial changes. Further, post COVID-19 more firms from the drugs and pharmaceutical category could able to enter the top bracket. To test the reliability, a comparison with another MCDM models has been done and result is found satisfactory. The sensitivity analysis (SA) has also been conducted to investigate the stability in the outcome of the proposed model. The present work provides the policy makers a stable and reliable MCDM framework for analyzing and accessing their CSR performance with peers and evaluate their market standing to take decisions for future course of action. [ABSTRACT FROM AUTHOR]

Logistics management has been playing a significant role in ensuring competitive growth of industries and nations. This study proposes a new Multi-Criteria Decision-making (MCDM) framework for evaluating operational efficiency of logistics service provider (LSP). We present a case study of comparative analysis of six leading LSPs in India using our proposed framework. We consider three operational metrics such as annual overhead expense (OE), annual fuel consumption (FC) and cost of delay (CoD, two qualitative indicators such as innovativeness (IN) which basically indicates process innovation and average customer rating (CR)and one outcome variable such as turnover (TO) as the criteria for comparative analysis. The result shows that the final ranking is a combined effect of all criteria. However, it is evident that IN largely influences the ranking. We carry out a comparative analysis of the results obtained from our proposed method with that derived by using existing established frameworks. We find that our method provides consistent results; it is more stable and does not suffer from rank reversal problem. [ABSTRACT FROM AUTHOR]

Background: India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies. Methods: We compared the performance of a recently developed formula SCS BTT and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures. Results: MCDM methods revealed that the Shine & Lal and SCS BTT were the best-performing formulae. Further, a modification of the SCS BTT formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS BTT along with the condition MCV ≤ 80 fl was recommended for a higher heterogeneous population set. It was found that SCS BTT can classify all BTT samples with 100% sensitivity when MCV ≤ 80 fl. Conclusions: We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS BTT and its web application SUSOKA can provide 100% sensitivity when MCV ≤ 80 fl. [ABSTRACT FROM AUTHOR]

• The study depicts societal issues associated with the development projects. • A road map developed that focuses on avenues related to establishment of new development projects. • The SEETA model, based on Multi-Criteria Decision Making techniques is proposed. • Model is demonstrated using comparison of fourteen hydropower plants. • The hydropower plants are compared around five main and fifteen sub-criteria. Selecting an appropriate location for a large infrastructure project poses difficult situations. It shall satisfy sustainability indicators and establish harmony among multiple goals of multiple stakeholders. In such a situation, Multi-Criteria Decision Making techniques allow assessing qualitative and quantitative attributes, analysing and removing subjective biases and help in arriving at objective decisions. However, a single Multi-Criteria Decision Making technique may not be an effective tool to generate concrete results as there is no validation of the results. By utilising hybrid Multi-Criteria Decision Making methods, one can incorporate, gauge, and assess the range of social, economic, and environmental impacts precisely and achieve an accurate result by validating results by other methods. Therefore, this paper develops a model called as Social, Economic, Environmental and Technical Assessment model. It combines four fuzzy Multi-Criteria Decision Making techniques viz. Fuzzy Stepwise Weighted Assessment Ratio Analysis, Fuzzy Multi-Objective Optimization by Ratio Analysis, Fuzzy Weighted Aggregated Sum Product Assessment and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. This paper utilises the case of hydropower plants in India, how the policy and decision-makers can arrive at the selection of the best location for an infrastructure project. According to the assessment value, Teesta Low Dam IV (Darjeeling, West Bengal) is the preferred hydropower plant for all selected methods. The State could bring social justice, ecological stability; achieve economic benefits and sustainability by appropriating this proposed model in other development fields. [ABSTRACT FROM AUTHOR]

Smartphones have become an inevitable part of every facet of modern society. The selection of a particular smartphone brand from multiple options that are available is a complex and dynamic decision-making problem, involving multiple conflicting criteria that are associated with imprecise asymmetric information imposed by the uncertainty of the consumers. In this paper, we propose a novel hybrid full consistency method (FUCOM) and a combinative distance based assessment (CODAS) based on the multi-criteria group decision-making (MAGDM) framework in the Fermatean fuzzy (FF) domain for smartphone brand selection. We derive the criteria using the UTAUT2 (unified theory of acceptance and ese of technology) model. A group of 15 decision makers (DMs) participated in our study. We compare 14 leading smartphone brands in India and find that the brands having superior features of a good quality and selling a brand image at a affordable price outperform other smartphones. To check the validity of our framework, we compare the results using extant multi-criteria decision-making (MCDM) models. We observe our model provides a consistent solution. Furthermore, we carry out a sensitivity analysis for ascertaining the robustness and stability of the results generated by our model. The results of the sensitivity analysis show that our proposed framework delivers a stable and robust solution. [ABSTRACT FROM AUTHOR]

The post-harvest and marketing system is a chain of interlinked activities starting after the harvest time and continuing until the delivery of the food product to the consumers. An efficient post-harvest system ensures that the harvested product reaches the customer in the shortest possible time interval without compromising the volume, quality, and safety of the product. The objective of this paper is to identify and model the critical causal factors of post-harvest losses (PHL) in the fruit and vegetable (F&V) supply chain in India, with the help of a detailed literature review and expert opinions. Fifteen causal factors were identified using the decision making and trial evaluation laboratory (DEMATEL) method, which is a multi-criteria decision making (MCDM) tool. This was applied for determining the cause-effect relationships among the identified factors. The results highlighted that the most critical factors that should be tackled to ensure progressive PHL reduction are: lack of proper packaging facilities (PHL3), lack of proper storage facilities (PHL1), insufficient infrastructure (PHL4), improved handling of the products at the farm and marketplace (PHL2), lack of processing facilities (PHL5), lack of linkage between the farmers and processing units (PHL6), lack of linkages in the marketing channel (PHL9), and large number of intermediaries (PHL15). The findings of this study are intended to guide various supply chain members and decision-makers for reducing PHL and improving the overall performance of the F&V supply chain. [ABSTRACT FROM AUTHOR]

The Indian Railways is India's biggest employer and undeniably influences the country's transportation network, economy, and social and cultural systems. The network is split into zones for operational reasons. It is vital to evaluate these railway networks to identify their strengths and shortcomings and to improve their performance. Past works often use Data Envelopment Analysis to evaluate railway services. Our contribution lies in the inclusion of several aspects not previously considered, such as (i) both tangible and intangible criteria, (ii) the hierarchical nature of the problem, and (iii) additional useful criteria and data to analyze the performance of the zones, including physical assets, operating ratio, accidents, comfort, travel experience, flexibility, transparency, etc. We use the novel Hierarchical Fuzzy Axiomatic Design method to evaluate the performance of sixteen zones in the Indian Railways since it suits our problem well. We find that the Southern Railway zone performs best, while the Northeast Frontier zone is ranked last. We also identify the strengths and weaknesses of all railway zones. [ABSTRACT FROM AUTHOR]

The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information. [ABSTRACT FROM AUTHOR]

The contrivance of this paper is two-dimensional. The first is to formulate an extended Dombi-Hamy mean operator in an interval-valued neutrosophic environment for assembling inter-related multiple parameters. The second one is to develop a new ranking procedure named as GLF ranking by introducing the concept of Gaussian likelihood function for solving multi-criteria decision-making problems in an interval-valued neutrosophic circumstance. Also, we have proposed a novel algorithm named Gaussian likelihood function based algorithm or GLF algorithm in solving multi-criteria decision-making problem from the field of medical science to find out the vital cause responsible for the occurrence of cancer in India. Finally, we have verified the flexibility and etiquette of GLF method by comparing it with the existing multi-criteria decision-making methods such as Aiwu's method and Jun Ye's method. • The formulation of the Gaussian likelihood function and its characteristics. • Research into a new GLF ranking system has been done. • The Dombi–Hamy mean operator is an aggregation operator that has been created. • A brand-new GLF strategy has been developed to address practical MCDM issues. [ABSTRACT FROM AUTHOR]

The objective of study is to analyze the significance of various Technology Push (TP) strategies affecting sustainable development in Indian manufacturing organizations. An extensive survey of 92 companies has been executed for the present context. The study examines the use of Multiple-Attribute Decision Making (MADM) and Multi-Criteria Decision Making (MCDM) method for evaluation of substantial TP strategies. For the purpose, a comparative analysis of various TP strategies using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) technique has been executed in the study. The outcomes of empirical calculations signify that the proposed techniques are appropriate to analyze the significance of TP strategies. The comparison of results exhibit that ranking of TP strategies evaluated using VIKOR technique is similar to the ranks obtained by TOPSIS. The focus of the paper is on the distinguishable contributions made by TP strategies like, innovative capability, research and development, corporate strategy and export orientation, for realization of sustainable development in manufacturing industries. It has been acknowledged in the investigation that manufacturing enterprises need to work more actively on managing certain TP strategies. [ABSTRACT FROM AUTHOR]

The flood hazard risks and vulnerability in the urban areas alongside major rivers of India have been gradually increasing due to extreme climatic events. The present study is intended to assess flood hazard vulnerability and potential risk areas and aims to ascertain the management strategies in Nabadwip Municipality, a statutory urban area of West Bengal. The multi-criteria decision making (MCDM) of selected criteria and geospatial techniques have been employed to determine the urban flood vulnerability in the study area. The study has been conducted using secondary datasets including relevant remotely sensed data and participant observation. The potential flood-affected zones have been determined using the normalized difference flood index (NDFI) and flood vulnerability index (FVI). The analysis of the standardized precipitation index (SPI) of 20 years of monthly precipitation shows the variability of seasonal rainfall distribution in the study area. Furthermore, the spatial distribution of the composite Ibrahim index of socio-economic development accents that the urban development of the study area was uneven. The municipal wards situated in the central and northeastern portions of Nabadwip Municipality were extremely vulnerable, whereas the western and southwestern wards were less vulnerable. It is also revealed from the strengths–weaknesses–opportunities–challenges (SWOC) of the principal management strategies of the flood situation analysis that the unplanned sewerage system is one of the most effective weaknesses in the area. All-embracing and integrative flood management strategies need to be implemented in the study area considering the intra-regional vulnerability and development for the resilient and sustainable development of the study area. [ABSTRACT FROM AUTHOR]

Background: The north-eastern part of India, dominated by a population belonging to various tribal communities, possesses diverse socioeconomic characteristics. The variation across communities in terms of availing maternity care is linked to socioeconomic traits. Objective: To conduct a comparative performance appraisal of all 16 regions in North-East India regarding the availing of maternity care and explore the linkage between each state's socioeconomic characteristics and its relative success in delivering maternity care. Methods: The study is based on the data from India's National Family Health Survey (NFHS - 4). We considered a total of 16 geographical areas, 11 criteria of maternity care, and 17 socioeconomic characteristics. We utilized hybrid models of AHP-TOPSIS and Entropy-TOPSIS for the ordinal evaluation of the regions concerning the delivery of maternity care and socioeconomic attainments. Finally, we analysed the associations between the two. Results: The ordinal evaluations rank all 16 regions regarding socioeconomic attainments and the delivery of maternity care. The study finds that Manipur, as a state, excels in delivering maternity care, while Meghalaya is the poorest performer. Female literacy, female educational attainment, male literacy, and female fertility are critical factors significantly impacting women's availing of maternity care. [ABSTRACT FROM AUTHOR]

This world has a wide range of technologies and possibilities that are available to control air pollution. Still, finding the best solution to control the contamination of the air without having any impact on humans is a complicated task. This proposal helps to improve the air quality using the multi-criteria decision making method. The decision to improve air quality is a challenging problem with today's technology and environmental development level. The multi-criteria decision making method is quite often faced with conditions of uncertainty, which can be tackled by employing fuzzy set theory. In this paper, based on an objective weighting method (CCSD), we explore the improved fuzzy MULTIMOORA approach. We use the classical Interval-Valued Triangular Fuzzy Numbers (IVTFNs), viz. the symmetric lower and upper triangular numbers, as the basis. The triangular fuzzy number is identified by the triplets; the lowest, the most promising, and the highest possible values, symmetric with respect to the most promising value. When the lower and upper membership functions are equated to one, we get the normalized interval-valued triangular fuzzy numbers, which consist of symmetric intervals. We evaluate five alternatives among the four criteria using an improved MULTIMOORA method and select the best method for improving air quality in Tamil Nadu, India. Finally, a numerical example is illustrated to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Facility location is one of the critical strategic decisions for any organization. It not only carries the organization's identity but also connects the point of origin and point of consumption. In the case of higher educational institutions, specifically B-Schools, location is one of the primary concerns for potential students and their parents while selecting an institution for pursuing higher education. There has been a plethora of research conducted to investigate the factors influencing the B-School selection decision-making. However, location as a standalone factor has not been widely studied. This paper aims to explore various location selection criteria from the viewpoint of the candidates who aspire to enroll in B-Schools. We apply an integrated group decision-making framework of pivot pairwise relative criteria importance assessment (PIPRECIA), and level-based weight assessment LBWA is used wherein a group of student counselors, admission executives, and educators from India has participated. The factors which influence the location decision are identified through qualitative opinion analysis. The results show that connectivity and commutation are the dominant issues. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%–95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ − ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers. [ABSTRACT FROM AUTHOR]

The need for energy sources in India has increased abnormally in recent years due to industrial and societal growth. To meet this demand, it was a necessary choice of renewable energy sources (RESs) as a solution to lack of nonrenewable energy sources. Due to the multiplicity of involved factors, selecting the most appropriate RESs is a multiattribute decision making (MADM) problem. There is a large number of work associated with the development of MADM techniques, especially under ambiguous and uncertain conditions. However, the effective embedding of uncertainty and ambiguity and in decision‐making remains a difficult challenge, and thus this study introduces a new framework for solving the problem of selecting the most suitable RESs which is based on the neutrosophic set and TODIM (an acronym in Portuguese of interactive and multicriteria decision‐making) method. It also reduces human intervention by being systematically applied. First, it transforms the linguistic terms presented into neutrosophic values and implements systematic techniques to compute missing values in the decision matrix using the case‐based technique. Second, it calculates the weight of every linguistic variable as well as those of the decision‐makers (DMs) and weighted attributes. Furthermore, it creates an aggregated single valued neutrosophic decision matrix for DMs. Finally, it calculates the overall dominance‐degree matrix, derives the overall values, and ranks the alternatives. It is applied to select RESs in Karnataka, India, and the obtained results show that wind energy is the most suitable RES for India, with small hydroenergy second most appropriate. [ABSTRACT FROM AUTHOR]

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approaches for alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies. [ABSTRACT FROM AUTHOR]

Background: The urban water system is the worst hit in global climate change. Water resilience is the system's ability to retaliate and recover from various water-related disruptions. The present study aims to delineate the water resilience zones in Chennai city, Tamil Nadu, India, by effectively integrating the geographic information system, remote sensing, and analytical hierarchy process (AHP). Methods: The methodology incorporated 15 vital factors. A multi-criteria decision analysis technique was adopted to assign a weight to each parameter using the AHP. A pairwise decision matrix was constructed, parameter's relative importance and the consistency ratio were established. Integration of all maps by weighted overlay analysis technique depicted water resilience intensities of five different classes. Results: Very low, low and moderate water resilience areas accounted for more than three-fourth of the study area. Area Under Curve score (80.12%) depicted the accuracy of the developed model. Sensitivity analysis determined the significance of the parameters in the delineation. The logical structural approach can be employed in other parts of India or elsewhere with modifications. Conclusion: This study is novel in its approach by holistically analyzing water resilience by integrating disruptions related to flood, drought and the city's water infrastructure system's adequacy and efficiency. Researchers and planners can effectively use the study results to ensure resilience as a new perspective on effective water resource management and climate change mitigation. It becomes a decision aid mechanism identifying where the system is vulnerable to potential water-related risks for employing resilience measures. [ABSTRACT FROM AUTHOR]

Abstract: Many technological alternatives for wastewater treatment are available, ranging from advanced technologies to conventional treatment options. It is difficult to select the most appropriate technology from among a set of available alternatives to treat wastewater at a particular location. Many factors, such as capital costs, operation and maintenance costs and land requirement, are involved in the decision-making process. Sustainability criteria must also be incorporated into the decision-making process such that appropriate technologies are selected for developing economies such as that of India. A scenario-based multiple-attribute decision-making (MADM) methodology has been developed and applied to the selection of wastewater treatment alternative. The four most commonly used wastewater treatment technologies for treatment of municipal wastewater in India are ranked for various scenarios. Six scenarios are developed that capture the regional and local societal priorities of urban, suburban and rural areas and translate them into the mathematical algorithm of the MADM methodology. The articulated scenarios depict the most commonly encountered decision-making situations in addressing technology selection for wastewater treatment in India. A widely used compensatory MADM technique, TOPSIS, has been selected to rank the alternatives. Seven criteria with twelve indicators are formulated to evaluate the alternatives. Different weight matrices are used for each scenario, depending on the priorities of the scenario. This study shows that it is difficult to select the most appropriate wastewater treatment alternative under the “no scenario” condition (equal weights given to each attribute), and the decision-making methodology presented in this paper effectively identifies the most appropriate wastewater treatment alternative for each of the scenarios. [Copyright &y& Elsevier]

Rising water scarcity in agriculture has been a major concern worldwide. As resource managers seek to address this issue, Integrated Water Resources Management (IWRM) has become a widely accepted sustainability paradigm. The purpose of this study is to evaluate restoration alternatives of irrigation tanks by applying multi-criteria and probabilistic benefit–cost analysis for a rural watershed in India. We incorporate the principles of local-IWRM, namely, hydrological balance, efficiency, equity, stakeholders' involvement, and uncertainty. We use the mixed-method approach of data collection, including remotely sensed hydro-ecological data, walk-through field observations, focus groups, and household surveys. The study region produces a large percent of runoff water (i.e., about 67% of the total precipitation) which can be partially captured to sustain irrigation tanks. The majority of the tanks in the study area do possess moderate to high irrigation potential yet remain in poor conditions. A proposed lift irrigation scheme with a 75% or more increase in water availability could return from ₹ 1.23 to ₹ 1.73 on every Indian rupee (₹) invested, in addition to other socio-ecological benefits. The increase in water availability could lead to future crop area expansion, which comes with a high price tag. Therefore, using additional water on the existing crop area can be just as economically viable as water-induced crop expansion. A coordinated effort on the part of local agencies and water users is necessary for efficient and equitable use of incremental water that comes from any restoration efforts in the study area or elsewhere. [ABSTRACT FROM AUTHOR]

Despite considerable efforts to improve water management, India is becoming increasingly water stressed due to multiple factors, including climate change, increasing population, and urbanization. We address one of the most challenging problems in the design of water treatment plants: how to select a suitable technology for a specific scenario or context. The process of decision making first requires the identification of feasible treatment configurations based on various objectives and criteria. In addition, the multiplicity of water quality parameters and design variables adds further complexity to the process. In this study, we propose a novel Decision Support Tool (DST), designed to address and support the above challenges. In this user-friendly tool, both Multi-Criteria Decision Analysis (MCDA) and Multi-Objective Optimization (MOO) methods are employed. The integration of MCDA with MOO facilitates the generation of feasible drinking water treatment solutions, identifies optimal options, and ultimately, improves the process of decision making. This implemented approach has been tested for different contexts, including for different types of raw water sources and system implementation scales. The results show that this tool can enhance the process of decision making, supporting the user (e.g., stakeholders and decision makers) to implement the most suitable water treatment systems, keeping in view the trade-offs. [ABSTRACT FROM AUTHOR]