Framework for mapping large-scale nature-based solutions for drought mitigation: Regional application in Flanders

Droughts are classified as the most expensive climate disasters as they leave long-term and chronic impacts on the ecosystem, agriculture, and human society. The intensity, frequency, and duration of drought events have increased in the past and are expected to continue rising at global, continental, and regional scales. Nature-based solutions (NBS) are highlighted as effective solutions to cope with the future impacts of these events. Despite this, there has been limited comprehensive research on the effectiveness of NBS for drought mitigation, and existing suitability mapping frameworks often overlook drought-specific criteria. To address this gap, a new framework is proposed to identify areas suitable for two drought-coping NBS types at a regional scale: detention basins and managed aquifer recharge. Two multi-criteria decision-making techniques (MCDM), i.e. Boolean logic and Analytic- Hierarchy Process (AHP), were used to map suitable large-scale NBS. The new framework accounts for unique criteria to specifically address drought conditions. By incorporating climate change scenarios for both surface and groundwater, recharge, and different groundwater characteristics, it identifies suitable and sustainable locations capable of managing extreme drought events. Executed through Boolean logic at a regional scale in Flanders (Belgium), the framework's strict approach yields significant potential areas for detention basins (298.7 km²) and managed aquifer recharge (867.5 km²). Incorporating AHP with the same criteria introduces a higher degree of flexibility for decision-makers. This approach shows a notable expansion across Flanders, varying with the level of suitability. The results underscore the highly suitable potential for detention basins (2552.2 km²) and managed aquifer recharge (2538.7 km²), emphasizing the adaptability and scalability of the framework for addressing drought in the region. The comparison between potential recharge volume due to detention basin and groundwater use in the region indicated that the detention basins could partially compensate for the high water demand. Therefore, creating a framework targeting drought is vital for the sustainable management of water scarcity scenarios.

Utilizing multi-criteria decision-making analysis and 3D visualization techniques for dam site selection and irrigation area identification in Gedeb River, Ethiopia

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Correlation coefficients between normal wiggly hesitant fuzzy sets and their applications

The multi-criteria decision-making (MCDM) field has long sought tools capable of adeptly capturing the intricacies of human decision-making amidst uncertainty. Hesitant fuzzy sets (HFS) have become a cornerstone in the MCDM field due to their ability to capture the intricacies of human decision-making under uncertainty. Nonetheless, we identified a significant gap in traditional HFS formulations, which often fail to fully harness the nuanced and implicit preferences of decision-makers (DMs). This shortcoming can lead to suboptimal decision outcomes in complex and uncertain environments. We introduce the normal wiggly hesitant fuzzy set (NWHFS), a novel construct that encapsulates both explicit and implicit preferences within a more representative framework. This study pioneers the development of new correlation coefficients for NWHFSs, offering a robust quantitative measure to elucidate the intricate relationships between variables. Our findings demonstrate that NWHFSs significantly enhance the MCDM process, providing a nuanced perspective that traditional HFS models cannot match. The proposed correlation coefficients not only reveal the concealed preferences of DMs but also broaden the decision-making spectrum, offering a more profound understanding of the relationships between alternatives and criteria. We illustrate the superiority of our approach through comparative analysis with existing methods, highlighting its ability to discern subtleties that other models overlook. Moreover, we integrate NWHFSs into clustering analysis, showcasing their potential to classify data sources with shared attributes effectively. This integration is particularly noteworthy for its ability to navigate complex datasets, offering a new dimension in data mining and resource retrieval. In essence, our study redefines the MCDM paradigm by introducing NWHFSs and their correlation coefficients, setting a new standard for decision-making accuracy and insight. The implications of our work extend beyond theory, offering practical solutions to real-world decision-making challenges.

Selection of renewable energy development path for sustainable development using a fuzzy MCDM based on cumulative prospect theory: the case of Malaysia

Malaysia's excessive energy consumption has led to the depletion of traditional energy reserves such as oil and natural gas. Although Malaysia has implemented multiple policies to achieve sustainable national energy development, the current results are unsatisfactory. As of 2022, only 2% of the country's electricity supply comes from renewable energy, which accounts for less than 30% of the energy structure. Malaysia must ensure energy security and diversified energy supply while ensuring sustainable energy development. This article uses the fuzzy multi-criteria decision-making(MCDM) method based on cumulative prospect theory to help decision-makers choose the most suitable renewable energy for sustainable development in Malaysia from four dimensions of technology, economy, society, and environment. The results show that solar power is the most suitable renewable energy for sustainable development, followed by biomass, wind, and hydropower, but the optimal alternative is sensitive to the prospect parameters. Finally, it was analyzed that efficiency, payback period, employment creation, and carbon dioxide (CO2) emissions are the most critical factors affecting the development of renewable energy in Malaysia under the four dimensions. Reasonable suggestions are proposed from policy review, green finance, public awareness, engineering education, and future energy. This research provides insightful information that can help Malaysian decision-makers scientifically formulate Sustainable development paths for renewable energy, analyze the problems encountered in the current stage of renewable energy development, and provide recommendations for Malaysia's future renewable energy transition and sustainable development.

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Pythagorean cubic fuzzy sets represent an advancement beyond conventional interval-valued Pythagorean sets, integrating the principles of Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets. Given the critical significance of distance measures in real-world decision-making and pattern recognition tasks, it is noteworthy that there exists a notable gap in the literature regarding distance measures specifically tailored for Pythagorean cubic fuzzy sets. The objectives of this paper are:•To define novel generalized distance measures between Pythagorean cubic fuzzy sets (PCFSs) to tackle intricate decision-making challenges.•These novel distance measures are undergoing testing on a real-world scenario concerning the management of anxiety and depression to evaluate their effectiveness and practical application.•We have illustrated the boundedness and nonlinear characteristics inherent in these distance measures. In addition, we conduct comparative analyses with existing approaches to validate the proposed methodology, thereby providing insights into its advantages and potential applications.

Multi-dichotomous sequencing method MDSM for ordering the importance of variants' properties in multi-criteria decision-making

There are plenty of Multi-Criteria Decision-Making (MCDM) methods that help to choose the most suitable solution assessed by several criteria (e.g. Saaty 1990; Simos 1990; Pamučar et al. 2018). They are applied in cases where several scales of different units describe the variants or the variants' properties are represented by linguistic, non-numbered terms. The inherent part of the MCDM algorithms is calculating the weights of the variants' properties, necessary for ordering the variants. If - in a certain problem - there are several properties to consider, sequencing their importance becomes a problem itself. The innovative method of sequencing is proposed in the article based on dichotomous splitting of the properties' importance. If made several times, it leads to the coherent - internally and with the decision-maker's intention - order of the properties' importance. Then the weights of the properties can be calculated with the use of different MCDM methods. The description of the method can be shortened as follows:•Divide the full set of features into two dichotomous subsets of lower and higher importance•Continue dichotomous divisions until there are only the subsets containing one element or subsets containing elements of equal importance.

A multi-criteria simulation-optimization coupling approach for effective emergency response in marine oil spill accidents

Effective marine oil spill responses are vital to reduce environmental, societal, and economic damage. This study developed a Multi-Criteria Emergency Response System (MC-ERS) to comprehensively evaluate response efficiency, operational costs, and environmental losses. The proposed system integrates dynamic multiphase simulation of oil weathering and oil cleanup processes and further provides effective planning for multi-stage resource allocation through system optimization. The developed weight-sum model improved the performance of response operations by reducing the complexity of multi-criteria decision-making. Particle Swarm Optimization (PSO) was chosen as the foundational optimization algorithm due to its efficiency in rapid convergence and suitability for complex problems. From extensive comparisons of PSO variants across benchmark functions and inertia strategies, the C-PSO algorithm was developed, demonstrating enhanced optimization performance for MC-ERS. The developed modelling system performance was demonstrated and evaluated through a representative case study. The optimization plan coordinated resource allocation from onshore warehouses to harbors and spill sites, balancing oil recovery efficiency, costs, and ecological losses. Optimized results indicate an oil recovery of up to 76.50% in five days. Additionally, the system cuts costs by 3.45% and environmental losses by 15.75%. The findings enhance the efficiency of marine oil spill emergency response and provide support for such incidents.

Evaluation of sustainable hydrogen production technologies on an industrial scale using comparative analysis of decision-making methods

Due to its various advantages in different industrial fields, hydrogen can provide energy based on sustainability goals and recreates a critical function in the economy of countries. In this regard, evaluating hydrogen production technologies on an industrial scale is necessary for industrial development and economic growth. Therefore, this study proposes a comprehensive, integrated framework of hybrid fuzzy decision-making for assessing hydrogen production technologies in Iran. In addition to considering sustainability factors, political, technical, and reliability indicators are also assessed in this research to make a comprehensive assessment. The Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) technique determines the importance of indicators, and the Fuzzy Weighted Aggregates Sum-Product Assessment (F-WASPAS) approach ranks technologies. The weighing findings indicated that the sub-indices of investment cost, technical infrastructure development, and implementation costs were introduced as the most significant sub-indices with weights of 0.226, 0.151, and 0.126, respectively. The evaluation findings with the F-WASPAS method and comparative analysis with various decision-making methods revealed that electrolysis based on solar energy and electrolysis based on wind energy technologies had the highest preference. In this regard, the infrastructure and costs of hydrogen production can be improved by presenting various incentives, such as improving financial conditions while attracting investment and increasing cooperation with top companies. So, sustainable development, economic growth, and industrial development are provided.

Evaluating strategies for developing renewable energies considering economic, social, and environmental aspects: a case study

Due to fast industrial expansion and the increasing population in Vietnam, this country is confronting a mounting lack of energy. While this country has considerable renewable energy (RE) potentials, including wind, solar, biomass, and hydropower sources, it has yet to exploit them entirely because of a lack of proper planning. This research aimed to find and assess solutions for encouraging RE growth in Vietnam. RE development solutions were formulated through SWOT analysis and evaluated in terms of their social, economic, and environmental dimensions. The SWARA approach weighed the strategy evaluation criteria. The most influential sub-criteria were initial investment cost, reduction of adverse environmental impacts, and natural capacity of the region, with weights of 0.155, 0.127, and 0.114, respectively. Strategy evaluation was performed using the Gray ARAS, and the results were validated with the Gray COPRAS, the Gray TOPSIS-G, and the Gray MABAC. In the strategy ranking, the top strategy is to reduce the cost of renewable power generation. This can be achieved by using advanced technologies and promoting cooperation between domestic and foreign industries and companies. Increasing domestic and foreign investment in RE infrastructure by providing financial facilities for investors, developing domestic and international cooperation, and creating a competitive environment between different companies to reduce electricity production costs were the most suitable strategies.

An extension of the best-worst method based on the spherical fuzzy sets for multi-criteria decision-making

The ambiguous information in multi-criteria decision-making (MCDM) and the vagueness of decision-makers for qualitative judgments necessitate accurate tools to overcome uncertainties and generate reliable solutions. As one of the latest and most powerful MCDM methods for obtaining criteria weight, the best-worst method (BWM) has been developed. Compared to other MCDM methods, such as the analytic hierarchy process, the BWM requires fewer pairwise comparisons and produces more consistent results. Consequently, the main objective of this study is to develop an extension of BWM using spherical fuzzy sets (SFS) to address MCDM problems under uncertain conditions. Hesitancy, non-membership, and membership degrees are three-dimensional functions included in the SFS. The presence of three defined degrees allows decision-makers to express their judgments more accurately. An optimization model based on nonlinear constraints is used to determine optimal spherical fuzzy weight coefficients (SF-BWM). Additionally, a consistency ratio is proposed for the SF-BWM to assess the reliability of the proposed method in comparison to other versions of BWM. SF-BWM is examined using two numerical decision-making problems. The results show that the proposed method based on the SF-BWM provided the criteria weights with the same priority as the BWM and fuzzy BWM. However, there are differences in the criteria weight values based on the SF-BWM that indicate the accuracy and reliability of the obtained results. The main advantage of using SF-BWM is providing a better consistency ratio. Based on the comparative analysis, the consistency ratio obtained for SF-BWM is threefold better than the BWM and fuzzy BWM methods, which leads to more accurate results than BWM and fuzzy BWM.

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

The COVID-19 pandemic has caused a surge in essential medical supplies usage, leading to a notable increase in medical waste generation. Consequently, extensive research has focused on sustainable disposal methods to handle used medical equipment safely. Given the necessity to evaluate these methods considering qualitative and quantitative criteria, this falls within the realm of multi-criteria decision-making (MCDM). This study introduces a framework for selecting the most suitable medical waste treatment methods, taking into account economic, technological, environmental, and social aspects. Sixteen criteria were assessed using the Fuzzy Preference Selection Index (F-PSI) to determine the optimal waste disposal approach. Additionally, the Fuzzy Compromise Ranking of Alternatives from Distance to Ideal Solution (F-CRADIS) method was employed to evaluate nine technologies for medical waste disposal. Notably, disinfection efficiency emerged as the most crucial criterion, with autoclaving identified as the preferred method for medical waste treatment. A practical case study conducted in Sivas, Turkey, validates the feasibility of these strategies. Multiple sensitivity analyses were performed to ensure the stability and reliability of the proposed approach.

Indicator based multi-criteria decision support systems for wastewater treatment plants

Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.

An identification of optimal waste disposal method for dumpsite remediation using the Fermatean fuzzy multi-criteria decision-making method

Improperly managed wastes that have been dumped in landfills over the years pose various challenges, but they also offer potential benefits. The feasibility of recycling such waste depends on the type of wastes, the condition of dumpsites, and the technology implemented for disposal. The selection of an alternative waste disposal method from the many available options for dumpsite remediation is a complex decision-making process among experts. The primary aim of this study is to assist in an extended multi-criteria decision-making (MCDM) method to reduce complexity in the proposed dumpsite remediation problem influenced by multiple criteria and to identify the optimal waste disposal method. Data uncertainties are managed with the proposed Fermatean fuzzy preference scale, and the importance of all socio-economic criteria is assessed using the full consistency method (FUCOM). The final ranking results of the weighted aggregated sum product assessment (WASPAS) method identify that the Waste-to-Energy (WtE) process could play a significant role in the disposal of land-filled unprocessed wastes, promoting sustainable waste management. Meanwhile, the methodology explores the idea that financial and logistical constraints may limit the feasibility of large-scale recycling efforts. This combination of environmental science and decision science addresses real-world challenges, helping municipal solid waste management authorities implement sustainable waste management practices.

An integrative approach to medical laboratory equipment risk management

Medical Laboratory Equipment (MLE) is one of the most influential means for diagnosing a patient in healthcare facilities. The accuracy and dependability of clinical laboratory testing is essential for making disease diagnosis. A risk-reduction plan for managing MLE is presented in the study. The methodology was initially based on the Failure Mode and Effects Analysis (FMEA) method. Because of the drawbacks of standard FMEA implementation, a Technique for Ordering Preference by Similarity to the Ideal Solution (TOPSIS) was adopted in addition to the Simple Additive Weighting (SAW) method. Each piece of MLE under investigation was given a risk priority number (RPN), which in turn assigned its risk level. The equipment performance can be improved, and maintenance work can be prioritized using the generated RPN values. Moreover, five machine learning classifiers were employed to classify TOPSIS results for appropriate decision-making. The current study was conducted on 15 various hospitals in Egypt, utilizing a 150 MLE set of data from an actual laboratory, considering three different types of MLE. By applying the TOPSIS and SAW methods, new RPN values were obtained to rank the MLE risk. Because of its stability in ranking the MLE risk value compared to the conventional FMEA and SAW methods, the TOPSIS approach has been accepted. Thus, a prioritized list of MLEs was identified to make decisions related to appropriate incoming maintenance and scrapping strategies according to the guidance of machine learning classifiers.

A fuzzy decision support system for evaluating Lean-Green and company-specific production system

Lean production is regarded as systematic solution for identifying and eliminating waste through continuous improvement of manufacturing processes to meet customers' requirements. Due to rising environmental concerns and the significant environmental impact of companies operations, issues such as environmental management and green production are on board agendas. In essence, green production seeks to reduce the environmental impacts of business processes. In order to examine green production, an original equipment manufacturer in the supply chain of tractor manufacturing industry is selected for this work to enable a focus on lean production aspects and its specific requirements to evaluate a company-specific production system (XPS). The results suggest that the company utilizes six lean-green production criteria including suitable and green packaging, quality improvement, waste reduction, environmental pollution reduction, transport management and additional transportation reduction, and cost reduction. Furthermore, continuous improvement and integrated auditing are selected as two critical and fundamental elements with the highest ranks, regarding the relationships between the XPS elements and lean-green production criteria.

Improving wayfinding in hospitals for people with diverse needs and abilities: An exploratory approach based on multi-criteria decision making

Hospital wayfinding systems that are based solely on signage do not provide adequate solutions for wayfinding needs, especially for users with impairments. Moreover, the interaction between user characteristics and the inner space of the building also determines wayfinding efficiency. The aims of this study, therefore, were to identify architectural features that affect spatial orientation and wayfinding behaviors; demonstrate the implementation of a multi-criteria decision-making (MCDM) approach for improving wayfinding in a diverse range of users; and produce a set of quantitative values (i.e., weights) for each selected architectural feature, based on the individual's preferences. Doing so could enable the formulating of practical design guidelines for hospital buildings, tailored to the needs and abilities of the users, to minimize disorientation and confusion - as demonstrated in this paper through a case study. The MCDM approach was chosen as it is based on observations whereby wayfinding resembles a continuous decision-making process, throughout which, users continuously select those architectural features that they perceive as having the greatest wayfinding value.

Waste-to-energy incineration site selection framework based on heterogeneous fuzzy regret-PROMETHEE model considering life-cycle carbon emissions

Waste incineration technology has received extensive attention for its advantages of being harmless, reducing, and recycling. However, the waste-to-energy incineration project confronts significant "not-in-my-backyard (NIMBY) concerns," and irrational location choices will have negative effects on the project's economy and sustainability; it is also a great challenge to the credibility of the government. To this end, a multi-criteria decision-making framework is constructed for the site selection of waste-to-energy incineration projects. To begin with, a site selection criteria system is established including 16 sub-criteria from four aspects, where probabilistic linguistic term sets are introduced to depict the qualitative sub-criteria and probabilistic hesitant fuzzy sets are employed to express the uncertainty of quantitative sub-criteria. An optimization model is then built to determine the weights of criteria based on the Pearson correlation coefficient and least square method. Furthermore, a regret-preference ranking organization methods for enrichment evaluations (PROMETHEE) model is presented to rank alternatives in a heterogeneous decision environment. Finally, a case study in China is conducted to validate the applicability of the proposed framework; the result of the site selection demonstrates that alternative A2 located in Miyun, Beijing, is the most optimal option. This work provides investors with scientific decision reference and also extends the methods in the decision-making field.

Assessment of governmental strategies for sustainable environment regarding greenhouse gas emission reduction under uncertainty

Since greenhouse gas emissions (GHGE) directly impact climate change that affects the environment, human health, society, and ecosystems, the reduction of GHGE is one of the essential actions for the sustainability of the environment. To reduce global GHGE, the United Nations has defined strategies at three levels: government, private, and public. Choosing between these strategies is a difficult process since there are relationships and contradictions among them. The process also includes uncertainties due to some reasons, such as lack of information, social structure, decision makers' hesitancy, and imprecision in the collected data. In this paper, a hierarchically structured methodology based on a decision-making procedure is proposed for the evaluation of the governmental strategies determined to decide the best strategies by integrating expert knowledge and a literature review. For this aim, interpretative structural modeling, cognitive mapping, and inference systems are integrated as a two-stage decision-making methodology based on fuzzy sets to address uncertainties and imprecision in the evaluation of government strategies for GHGE reduction in Türkiye. Based on the results of the first stage, "Transportation" is determined as the most influential sector for GHGE mitigation. In the second stage, strategies of the transportation sector are assessed and ranked. "Promoting the significant public health benefits of low-carbon policies, including increased public transportation and non-motorized mobility" is determined as the most appropriate transportation strategy for the governmental action plan regarding the climate change reduction objective. This paper contributes to applying knowledge and experiences from the current environmental characteristics and social fields to the strategic decision of the GHGE reduction area, to streamline its assessment process, provide human-centered solutions, and accelerate governmental actions.

Multi-criteria decision analysis for supporting the selection of subclinical mastitis screening tests to use in large- and small-scale dairy farms in Türkiye

The production of high-quality and safe milk is closely associated with the udder health of dairy cows. While there are many mastitis diagnostic tests/methods available, choosing the most appropriate diagnostic test for a sustainable udder health control program could be a challenge. This study was aimed at selecting tests for the screening of subclinical mastitis on small- and large-scale dairy farms in Türkiye, using multi-criteria decision-making methods. An integrated approach employing the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) together was used to select subclinical mastitis screening tests for on-farm use. While the AHP determines the weights of the evaluation criteria, the TOPSIS provides a final ranking. Nine different subclinical mastitis screening (SCM) methods (DeLaval somatic cell counter, PortaSCC test, California mastitis test (CMT), rapid culture, portable/hand-held electrical conductivity meter, infrared thermography, leukocyte esterase strip test, milk pH, UdderCheck test) were analyzed on the basis of five selection criteria (the market availability of the test, the diagnostic accuracy of the test, the cost of the test, the cow-side use of the test, and the practicality of the test). The selection criteria were determined based on literature review and stakeholder input. The weighting of the criteria with the AHP was based on the pairwise comparison of the criteria by stakeholders. The criteria were weighted from 1 to 9 according to their relative importance as follows: "1: equally important," "3: moderately important," "5: strongly important," "7: very strongly important," "9: extremely important," and "2, 4, 6, 8: intermediate values." Final ranking of SCM tests with the TOPSIS was based on the stakeholder evaluations of fulfillment of the criteria by the alternatives. The most appropriate screening test for both large- and small-scale dairy farms was determined to be the CMT. The CMT is a very useful, easy to perform, and low-cost tool for detecting subclinical mastitis. Being a major element of udder health control programs, the CMT, if regularly used on dairy farms in Türkiye, would enable the culling of chronically infected animals and the reduction of mastitis-associated economic losses. Furthermore, regular CMTs would contribute to reducing milk SCC and improving milk quality. In conclusion, multi-criteria decision-making methods not only provide a systematic approach that may assist both veterinarians and farmers in deciding on the best choice among the different tests available for the screening of subclinical mastitis but also offer potential benefits to policymakers, researchers, and other industry stakeholders.

Strict and weak ordinal relations for estimating the criteria weights in Ordinal Priority Approach (OPA)

The Ordinal Priority Approach (OPA) is a recent multiple attribute decision-making that was published in 2020. This method uses a linear programming approach to solve decision-making problems in real-life situations. Due to its application in real-world situations, it has been employed by scholars widely in recent years. This study proposes two forms of the OPA for multiple attribute decision-making; one with strict ordinal relations and the other with weak ordinal relations. New forms are crucial in understanding the mathematical theory behind the original OPA. The study shows that one of the proposed forms and the original OPA are two forms of the same model. The study proves that the OPA is strict rather than weak. The study also found some new properties of the OPA. The application of the strict and weak OPA models is presented in a consumer modeling problem.•Revisits the Ordinal Priority Approach (OPA) to Multiple Attribute Decision Making.•Proposes the OPA with Strict Ordinal Relations (OPA-S) and Weak Ordinal Relations (OPA-W).•Proposes a model to consider both weak and strict ordinal relations at the same time.

Landfill site selection using MCDM methods and GIS in the central part of the Nile Delta, Egypt

One of the most prevalent and serious issues afflicting developing countries is the lack of adequate space for waste disposal. Al-Gharbia Governorate, located in the middle of the Nile Delta in Egypt, suffers from random selection of sites for solid waste disposal, resulting in significant environmental challenges. The aim of this study is to determine optimal landfill locations within Al-Gharbia Governorate and validate the existing landfill sites. Four techniques of multi-criteria decision-making (MCDM) were applied to generate suitability maps for the Governorate: the analytical hierarchy procedure (AHP), ratio scale weighting (RSW), straight rank sum (SRS), and Boolean method. Eleven effective criteria were considered: groundwater, surface water, elevation, slope, soils, land use, roads, railways, urban areas, villages, and power lines. The suitability maps were categorized into four different classes: suitable, moderately suitable, low suitable, and unsuitable. The latest suitability map was determined by combining the results from the different methods, providing decision-makers with the means to select the optimal landfill site. The suitable zone encompasses a small area (3%), predominantly located in the northeast region (Al-Mahalla), central region (Tanta), and northern region (Kotour). Conversely, the unsuitable area covers a substantial portion (72.7%) due to the agricultural nature of the governorate, high population density, and elevated groundwater levels. Furthermore, all existing landfill sites fall within unsuitable or low suitable areas, inflicting severe impacts on the nearby environment, public health, and groundwater integrity.

Spatial assessment of sediment risk with integrated entropy-based WASPAS and fuzzy clustering methods in Turkey: impact of forestry activities and meteorological factors

The sediment formed during erosion processes is acknowledged as one of the important contributors to environmental damage. In forest areas, sediment production occurs as a result of meteorological factors and wood harvesting, as well as forest road construction activities carried out for the continuity of forests. In this context, the aim of the study is to reveal the sediment risk in forest areas at the provincial level in Turkey by using entropy-based weighted aggregated sum product assessment (WASPAS) and fuzzy clustering methods. The results establish that the weights of the factors affecting the sediment risk are the forest road construction rate (0.3721), the wood harvesting rate (0.3463), the average precipitation (0.2227), and the average temperature (0.0587). Seven provinces were found in the highest risk class of the cluster in terms of sediment risk. Two of those (Yalova and Kocaeli) are located in the Marmara Region, while the others (Bartın, Düzce, Giresun, Ordu, and Zonguldak) are located in the Black Sea Region. Four of the provinces in the lowest risk class of the cluster (Bingöl, Bitlis, Erzincan, and Malatya) are located in the Eastern Anatolia Region, while a fifth (Siirt) is located in the Southeastern Anatolia Region. As is seen in the study, multi-criteria decision-making methods make it possible to evaluate different scenarios for sedimentation estimation and control and to select the most effective solutions. In conclusion, the use of WASPAS, entropy, and fuzzy clustering methods can provide a comprehensive and effective approach for assessing the sediment risk.

Managing surgical waiting lists through dynamic priority scoring

Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.

Addressing the supplier selection problem by using the analytical hierarchy process

ACI Pharma spends $ 12 to 15 million yearly to promote its brands. The brand promotional activities of the company were hampered, due to supplier selection subjectively, which impacted market share. The study selects the best supplier with supplier management and purchasing policy for printing materials for ACI Pharma based on objective judgments. The required data has been collected from the company. This study uses the Analytical Hierarchy Process (AHP) to address supplier selection and management problems. Five attributes, cost, quality, delivery, flexibility, and communication, and ten suppliers have been evaluated to select the best supplier. Spark Printers is the best supplier for ACI Pharma as it obtains the highest score, 0.968, whereas Marvelous Printers Limited and Lutfur Enterprise are the second-best and third-best suppliers. The application of AHP in the pharmaceutical supply chain industry is very limited. Further, Previous studies emphasized supplier selection rather than management and purchasing policy. Moreover, industry practitioners, especially in developing countries, might not use complex methodologies due to time, money, or technical constraints. Considering these thoughts, this study is conducted to select and manage the right supplier with purchasing policy using AHP in industry-friendly language and analysis patterns. Other organizations suffering from similar problems, such as ACI Pharma, may benefit from this research. The study has selected AHP, purposively, without any methodological justifications, although many Multi-Criteria Decision-Making (MCDM) techniques are present. Further studies might be conducted to find which MCDM method fits best to address the supplier selection problem in the pharmaceutical supply chain industry.

Doctors ranking through heterogeneous information: The new score functions considering patients' emotional intensity

With the popularity of the Internet and the growing complexity of COVID-19, more and more patients tend to consult doctors online. With the difficulty of doctor selection caused by a massive amount of information, this study proposes a hybrid multi-criteria decision-making framework, which can model patients' emotional intensity through heterogeneous information and rank doctors. Firstly, online reviews (ORs) are transformed into probabilistic linguistic term sets through sentiment analysis. Then, new score functions are proposed considering the nonlinear influence of doctors' information and the patients' negative bias toward ORs. Next, a method of weight determination combining the Term Frequency Inverse Document Frequency and the Decision-making Trial and Evaluation Laboratory method is proposed. Finally, the proposed score functions are applied to the Combined Compromise Solution (CoCoSo) method to aggregate information and rank doctors. The proposed method is verified in a case study on haodf.com. The results show that considering the emotional intensity of heterogeneous information will make the recommendations more realistic. Comparative analysis and sensitivity analysis are further performed to illustrate the availability and effectiveness of the proposed method.