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.

An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.

Assessing industrialized countries' environmental sustainability performances using an integrated multi-criteria model and software

This study seeks to identify and highlight the differences in the environmental sustainability and environmental protection practices of industrialized countries. For this purpose, thirty industrialized countries were examined using a multi-criteria integrated decision model based on fourteen environmental sustainability performance criteria. An open-source software based on the Python that allows different multi-criteria decision-making methods to be used through a user-friendly interface was developed for evaluations. The results from the developed software rank Sweden first and India last in their environmental sustainability performances. Sweden stands out notably in terms of negative greenhouse gas emission technologies, afforestation, reforestation, environmental laws, carbon tax, bioenergy, and national consciousness. India, on the other hand, does not appear to handle environmental problems in a timely and appropriate manner considering its large population and development needs. Development, utilization, and expansion of renewable energy sources, waste recycling, waste reduction, proper waste disposal, and prioritization of the development of technologies with zero or negative greenhouse gas emissions have emerged as significant factors for environmentally friendly industrialization.

Assessment of sustainable waste management alternatives using the extensions of the base criterion method and combined compromise solution based on the fuzzy Z-numbers

A number of factors have contributed to the increase in waste production and diversity of waste, such as the increase in population, alterations in consumption patterns, economic development, income changes, urbanization, and industrialization. The production of different types of waste, such as electronic, urban, hospital, and industrial waste, makes it necessary to classify waste accurately and recognize effective criteria for waste management. To design and operate waste management systems, it is necessary to understand the sources and types of waste, as well as information about their composition and rate of production. As a result, this study aims to rank 21 types of waste according to Iran's economic, social, and environmental criteria, as well as 13 sub-criteria related to those criteria. For this aim, proposed a novel decision-making approach based on the extension of the base criterion method (BCM) and combined compromise solution (CoCoSo) methods under fuzzy Z-numbers. Additionally, sensitivity analysis and comprehensive analysis are conducted on the results of the criteria and alternatives of sustainable waste management. Based on the results of this study, direct profit and reduced landfill are the most important criteria for assessing sustainable waste management alternatives. According to the results of this study, the sub-alternative of industrial metal waste is the most important waste management option. Examining the next sub-alternative ranks under sustainable waste management options (mobile, communication equipment, and battery) shows that electronic waste requires more attention for recycling and sustainable waste management.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Selection of plastic solid waste treatment technology based on cumulative prospect theory and fuzzy DEMATEL

Under the global implementation of a low-carbon economy, the treatment of municipal plastic solid waste (PSW) has become an important task to be solved urgently. In the actual decision-making process of PSW treatment, the evaluation information is usually fuzzy, and the decision-makers (DMs) are bounded rational. For selecting the most appropriate PSW treatment technology, we propose a multi-criteria decision-making (MCDM) method based on cumulative prospect theory and fuzzy decision-making trail and evaluation laboratory (DEMATEL). Firstly, we construct the criteria system of PSW treatment that consists of 9 sub-criteria from the perspectives of environment, economy, society, and technology. Then, considering the interdependences and interactions between these evaluation criteria and allowing multiple stakeholders to participate in decision-making, we propose a fuzzy DEMATEL method to deal with the fuzziness of evaluation in the decision-making process and determine the weights of the evaluation criteria. Subsequently, taking into account the different opinions of different stakeholders and psychological factors such as risk preference and loss aversion of stakeholders, we aggregate the evaluation information of different stakeholders and develop the PSW treatment alternatives to rank the orders by using the proposed multi-actor cumulative prospect theory (CPT) method. We study seven alternative processes for PSW treatment by the developed model, including landfill, recycling, pyrolysis, incineration, and the combination of landfilling and recycling, landfill and incineration, and recycling and pyrolysis. According to the ranking results, we find the combination of recycling and incineration is the best treatment alternative. We take the seven PSW treatment technologies in Shanghai as the case study to verify the effectiveness and feasibility of the proposed method. Through the sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and an acronym in Portuguese of the interactive and multi-criteria decision-making (TODIM) method, we illustrate the effectiveness and superiority of the proposed method. This research provides significant references for the PSW treatment technology selection problems under uncertain environments and extends the methods in the decision-making field.

A robust, resilience multi-criteria decision-making with risk approach: a case study for renewable energy location

Regarding hard situations like war, the increasing cost of extraction and exploration of fossil fuels make governments move toward green and clear renewable energy (RE). As a result, we propose a novel multi-criteria decision-making (MCDM) method for RE location (REL) for the first time. This model suggests a Robust, Resilience MCDM with Risk approach (RRMCDMR) for REL. We propose a risk approach by adding a risk function in MCDM. A robust convex approach is used to tackle the uncertainty of the model for the real world. We compare the RRMCDMR problem in a wind farm location in Iran with different risk coefficient functions. As defined, Khaf, Nehbandan, and Esfarayan are in locations one to three in all modes. We changed the normalized risk function and suggested two other risk functions that can help risk-averse and risk-neutral decision-makers. We varied the robust convex coefficient and considered that by increasing the robust convex coefficient, the alternative score increased.

An extended DNMA-based multi-criteria decision-making method and its application in the assessment of sustainable location for a lithium-ion batteries' manufacturing plant

Lithium-ion battery (LiB), a leading residual energy resource for electric vehicles (EVs), involves a market presenting exponential growth with increasing global impetus towards electric mobility. To promote the sustainability perspective of the EVs industry, this paper introduces a hybridized decision support system to select the suitable location for a LiB manufacturing plant. In this study, single-valued neutrosophic sets (SVNSs) are considered to diminish the vagueness in decision-making opinions and evade flawed plant location assessments. This study divided into four phases. First, to combine the single-valued neutrosophic information, some Archimedean-Dombi operators are developed with their outstanding characteristics. Second, an innovative utilization of the Method based on the Removal Effects of Criteria (MEREC) and Stepwise Weight Assessment Ratio Analysis (SWARA) is discussed to obtain objective, subjective and integrated weights of criteria assessment with the least subjectivity and biasedness. Third, the Double Normalization-based Multi-Aggregation (DNMA) method is developed to prioritize the location options. Fourth, an illustrative study offers decision-making strategies for choosing a suitable location for a LiB manufacturing plant in a real-world setting. Our outcomes specify that Bangalore (L ₂), with an overall utility degree (0.7579), is the best plant location for LiB manufacturing. The consistency and robustness of the presented methodology are discussed with the comparative study and sensitivity investigation. This is the first study in the current literature that has proposed an integrated methodology on SVNSs to select the best LiB manufacturing plant location by estimating both the objective and subjective weights of criteria and by considering ambiguous, inconsistent, and inexact manufacturing-based information.

Economic and environmental life cycle perspectives on two engineered wood products: comparison of LVL and GLT construction materials

The embodied carbon of building materials and the energy consumed during construction have a significant impact on the environmental credentials of buildings. The structural systems of a building present opportunities to reduce environmental emissions and energy. In this regard, mass timber materials have considerable potential as sustainable materials over other alternatives such as steel and concrete. The aim of this investigation was to compare the environment impact, energy consumption, and life cycle cost (LCC) of different wood-based materials in identical single-story residential buildings. The materials compared are laminated veneer lumber (LVL) and glued laminated timber (GLT). GLT has less global warming potential (GWP), ozone layer depletion (OLD), and land use (LU), respectively, by 29%, 37%, and 35% than LVL. Conversely, LVL generally has lower terrestrial acidification potential (TAP), human toxicity potential (HTP), and fossil depletion potential (FDP), respectively, by 30%, 17%, and 27%. The comparative outcomes revealed that using LVL reduces embodied energy by 41%. To identify which of these materials is the best alternative, various environmental categories, embodied energy, and cost criteria require further analysis. Therefore, the multi-criteria decision-making (MCDM) method has been applied to enable robust decision-making. The outcome showed that LVL manufacturing using softwood presents the most sustainable choice. These research findings contribute to the body of knowledge about the use of mass timber in construction.

Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic

Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of "Total Cases", "Total Recovered", "Total Deaths", "Active Cases", "Serious", "Total Tests" and "Day of Infection". The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of "Total Cases", "Total Deaths", "Serious", "Active Cases", "Total Recovered", "Total Tests" and "Day of Infection" are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.

A decision-making framework for COVID-19 infodemic management strategies evaluation in spherical fuzzy environment

100 years after the Spanish flu, the COVID-19 crisis showed that large-scale epidemics and pandemics do not belong to the past. On the report of the World Health Organization, COVID-19 is the most significant public health problem of the twenty-first century. Like previous epidemics, the current crisis is accompanied by uncertainty, mistrust, doubt and fear, and this has led to an infodemic connection to the epidemic. So not only are we fighting an epidemic, but also, we are brawling an infodemic. To reduce the social and economic consequences and harmful effects of infodemic health, and to overcome it, we need to implement strategies against infodemic. Evaluating strategies based on multiple characteristics can be considered multi-criteria decision-making (MCDM) problem. According to the literature, there is no study that aims on proposing an integrated approach to evaluate infodemic management strategies under uncertain environment. Therefore, in this paper, an integrated framework based on the extended version of best-worst method (BWM) and Combined Compromise Solution (CoCoSo) methods under a spherical fuzzy set (SFS) is developed for the first time to address the COVID-19 infodemic management strategies selection. Initially, the criteria are weighted using the developed SFS BWM which reduces uncertainty in pairwise comparisons. In the next step, the 15 selected strategies are analyzed and ranked using SFS CoCoSo. The outputs of this paper illustrate that online tools for fact checking COVID-19 information and engage and empower communities are placed in the first and second priorities, respectively. The comparison of ranking results SFS-CoCoSo with other MCDM methods demonstrates the performance of the proposed approach and its ranking stability.

A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China

Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best-worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts' opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.

Importance of green roof criteria for residential and governmental buildings: a multi-criteria decision analysis

Megacities recently are experiencing a shortage of green spaces basically due to the rapid growth of urbanization and increasing demand for different building types. Consideration of sustainable urban development is essential since the expansion of city facilities should be in line with social, economic, and environmental aspects. In this regard, green roof technology has been recommended as an effective solution for the growth of green spaces per capita and improving sustainability means of urban developments due to its diverse advantages. This study thus aimed at prioritizing sustainability indicators and relative sub-criteria of adopting green roof technology for residential and governmental buildings in the city of Mashhad, Iran, which has a dry climate. For this purpose, thirteen sub-criteria, which are extracted from the existing literature, are classified into three main sustainability indicators (environmental, economic, and social). Also, the best-worth method (BWM) as a multi-criteria decision-making technique was implemented to prioritize indicators and sub-criteria by analyzing the expert's opinion. The results indicated that respective economic and environmental indicators attract the highest priority in residential and governmental buildings. Additionally, the most important sub-criteria in environmental, economic, and social groups are air quality, roof longevity, and public health in both building types, respectively. However, when all criteria were considered, the respective highest priorities belong to roof longevity and air quality in residential and governmental buildings, while biodiversity conservation is the least important one in both building types. The results of this research can be beneficial in other cities with similar economic and climate conditions.

To dream or not to dream in Havana: multi-criteria decision-making for material and energy recovery from municipal solid wastes

Currently, solid waste management strategies in Havana are outdated. This paper aimed to select the most suitable alternative for integrating material recovery facilities (MRF) with waste-to-energy technologies in the city of Havana, Cuba. Seven scenarios were considered: combustion, gasification, and hydrothermal carbonization (HTC) with and without carbon capture, and anaerobic digestion (AD). The selection was based on environmental, techno-economic, and social parameters using an analytic hierarchy process (AHP) as a multi-criteria decision-making tool (MCDM). The MCDM-AHP accounted for qualitative criteria (based on experts' judgments) and quantitative (based on Aspen Plus simulation models). From the MRF, 63% of the input recyclable materials were recovered, representing an energy saving of 256 kW-h/t_MSW. The AHP results showed that environmental criteria had the highest priority, resulting in ~63% and ~73% higher than social and techno-economic criteria, respectively. Likewise, from the techno-economic, environmental, and social sub-criteria analysis, investment risk, pollution, and work safety had the major concern compared with the other sub-criteria levels. Overall, MRF+AD was the most suitable scenario (21% preference) for treating Havana's municipal solid waste (MSW), followed by combustion and gasification with carbon capture, respectively. This study confirms that AD is a preference option for emerging economies like Cuba, mainly due to low environmental pollution, high social acceptance, and financial stability in the long term.

Fuzzy incomplete linguistic preference relations

The effectiveness of preference relations in modeling decision-making processes makes it one of the most common representations of information use for solving decision-making problems. This research presents the fuzzy incomplete linguistic preference relations (Fuzzy InLinPreRa) approach as evaluated by decision-makers dealing with increasing complexity and uncertain economics, as well as social and managerial problems. By using Fuzzy InLinPreRa, the consistency measurements of decision-makers' evaluations will provide more accurate and reasonable solutions, allowing decision-makers to consider the objective weights of both the criteria and experts. An empirical example of the measurement of brand personality is included herein to demonstrate the feasibility of this method.

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

Interval-valued Pythagorean fuzzy multi-criteria decision-making method based on the set pair analysis theory and Choquet integral

This paper proposes a novel fuzzy multi-criteria decision-making method based on an improved score function of connection numbers and Choquet integral under interval-valued Pythagorean fuzzy environment. To do so, we first introduce a method to convert interval-valued Pythagorean fuzzy numbers into connection numbers based on the set pair analysis theory. Then an improved score function of connection numbers is proposed to make the ranking order of connection numbers more in line with reality in multi-criteria decision-making process. In addition, some properties of the proposed score function of connection numbers and some examples have been given to illustrate the advantages of conversion method proposed in the paper. Then, considering interactions among different criteria, we propose a fuzzy multi-criteria decision-making approach based on set pair analysis and Choquet integral under interval-valued Pythagorean fuzzy environment. Finally, a case of online learning satisfaction survey and a brief comparative analysis with other existing approaches are studied to show that the proposed method is simple,convenient and easy to implement. Comparing with previous studies, the method in this paper, from a new perspective, effectively deals with multi-criteria decision-making problems that the alternatives cannot be reasonably ranked in the decision-making process under interval-valued Pythagorean fuzzy environment.

New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach

The most straightforward approaches to checking the degrees of similarity and differentiation between two sets are to use distance and cosine similarity metrics. The cosine of the angle between two n-dimensional vectors in n-dimensional space is called cosine similarity. Even though the two sides are dissimilar in size, cosine similarity may readily find commonalities since it deals with the angle in between. Cosine similarity is widely used because it is simple, ideal for usage with sparse data, and deals with the angle between two vectors rather than their magnitude. The distance function is an elegant and canonical quantitative tool to measure the similarity or difference between two sets. This work presents new metrics of distance and cosine similarity amongst Fermatean fuzzy sets. Initially, the definitions of the new measures based on Fermatean fuzzy sets were presented, and their properties were explored. Considering that the cosine measure does not satisfy the axiom of similarity measure, then we propose a method to construct other similarity measures between Fermatean fuzzy sets based on the proposed cosine similarity and Euclidean distance measures and it satisfies the axiom of the similarity measure. Furthermore, we obtain a cosine distance measure between Fermatean fuzzy sets by using the relationship between the similarity and distance measures, then we extend the technique for order of preference by similarity to the ideal solution method to the proposed cosine distance measure, which can deal with the related decision-making problems not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate the reasonableness and effectiveness of the proposed method, which is also compared with other existing methods.

Evaluation of wind power generation projects to reduce air pollution using multi-criteria decision-making methods in Saudi Arabia

Currently, Saudi Arabia has very limited renewable energy generation capacity, as most of the country's electricity sector is dependent on cheap fossil fuels. However, in recent years, the Saudi government has announced a national development program called "the Saudi Vision 2030," whereby the country intends to increase the share of renewable energies in its total power generation to 20% by 2030. This research is aimed on the possibility of developing wind farms in Saudi Arabia's Al-Jawf area, which is known to be rich in wind sources. The potential of wind energy in the region was examined in the first phase of the research, which focused at the environmental, economic, and technical aspects. For this goal, the two-parameter Weibull function was used to model wind energy in the area. The economic assessment was performed in terms of the Levelized Cost of Energy and payback period. Multi-criteria decision-making approaches were employed in the second phase of the study to determine the most proper sites for harvesting wind energy in the study region based on eight factors including technical, economic, environmental, and social aspects. The most proper site for wind farms was identified by the combined use of Stepwise Weight Assessment Ratio Analysis and Weighted Aggregated Sum Product Assessment. The results showed that the most proper site for locating wind farms in the study area is the city of Al-Qurrayyat, where, using 1 MW turbines, it will be possible to produce 2357 MWh/year of electricity at a cost of 0.092 $/kWh, resulting in a payback period of 8.1 years. From the environmental perspective, wind power generation in Al-Qurrayyat will result in 1124.15 ton/year of CO₂ emissions reduction.

Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models

The idea of the circular economy (CE) has gained prominence in the policies of the European Union (EU), commerce, and academic studies. Basically, CE is capable of achieving the best value and resolving many of the systemic challenges in the society and commerce of a country, thus leading to sustainable development and preventing irreparable damage to the environment. Medical waste management has proved a daunting challenge with the increase in the global population and the demand for medical services. Fuzzy multi-criteria decision-making approaches try to cover the different and uncertain views of decision-makers (DMs). The present study suggests a novel strategy based on multi-objective optimization using the ratio analysis (MOORA) in the area of spherical fuzzy sets (SFSs) to counterbalance the disadvantages of the failure modes and effects analysis (FMEA) method, such as the lack of weight assignment for risk factors and consideration of uncertainty. In the proposed method, first, the barriers are identified using the FMEA method, and the risk factors are given values. Then, the barriers identified using MOORA are prioritized in the spherical fuzzy (SF) area. The computational procedure of the proposed methodology is established through a case study of the barriers to circular economy implementation in designing sustainable medical waste management systems problems under an SF environment. The proposed approach was compared with IF-MOORA and was found that the results are more reliable using the proposed method, also the ranking in the MOORA method was compared with the TOPSIS method in terms of degree of correlation.

A multi-criteria decision model for assessing health and self-care ability

Population ageing together with the greater prevalence of multimorbidity add to the need for and complexity of healthcare services. This makes it important to encourage and empower patients with chronic diseases to take care of themselves. An associated goal of such efforts is to significantly reduce the burden on healthcare systems and positively impact patients' health outcomes and quality of life. The paper presents a multi-criteria decision model for assessing the health and self-care of patients with chronic diseases in the home environment. The model is based on the DEX methodology and was tested on ten cases. The model assists with the timely recognition of relevant symptoms and signs in decision-making about health and self-care. It can be used to promote patients taking on an active role with respect to caring for their health and well-being. The model could be integrated into self-care processes. It might also serve as a basis for an interprofessional approach to supporting older patients with chronic diseases living as fully and independently as possible in the environment in which they feel most comfortable.

Robust sensor placement for sustainable leakage management in water distribution networks of developing economies: A hybrid decision support framework

Urban water distribution networks (WDNs) in developing economies often refrain from investing in sensor-based leakage management technologies due to financial constraints and other techno-managerial issues. Thus, this study proposes a generalized decision support framework based on network sensitivity analysis (NSA) and multi-criteria decision-making (MCDM) to assess the prospect of effective leakage control through robust sensor placement in existing deficient WDNs. Four sensitivity parameters are formulated for NSA to ascertain the pressure response of the potential sensor positions for diverse hydraulic and leak scenarios. Subsequently, selecting the optimal number of sensors and their relative positions within the WDN is framed as an MCDM problem that entails the simultaneous maximization of Euclidean distances among the potential sensor positions and the leak-induced pressure residuals obtained at these sensors. The proposed methodology is developed on a numerical benchmark network assuming ideal conditions, and its applicability is verified on a sensor-equipped experimental network considering realistic system uncertainties. The outcome of this study aims to provide an insightful understanding of the system behavior that governs its leak localization potential and ascertain the practical challenges of sensor-based leakage monitoring in existing WDNs. Decision-makers of resource-strained utilities can beneficially utilize the proposed framework to assess the environmental and cost trade-offs of employing sensor-based technologies for leakage management and proactive decision-making before its actual implementation.

Flexible decision-making framework for developing operation protocol for water distribution systems

Past water distribution systems (WDS) management studies derived operation protocols to maximize WDS reliability by using residual chlorine as the sole surrogate parameter for water quality reliability. Albeit the advancement in mechanistic modeling to examine the WDS water quality, emerging water quality parameters of concern are not yet involved in solving WDS management problems. This paper attempts to overcome this limitation by developing a flexible decision-making framework -integrating EPANET-C, a mechanistic modeling tool for WDS water quality, with Analytic Hierarchy Process (AHP), a multi-criteria decision-making method - to rank the possible water quality parameter-based operating alternatives (organic matter and residual chlorine levels at the source points) for WDS. The uncertainty analysis was incorporated into the mechanistic modeling using the Monte Carlo method to realize insufficient knowledge about the complex biological and physicochemical interactions inside WDS. Six cases, each ranking the alternatives diversely, were applied to reflect the expert judgment impressions on the AHP outcomes. The consistency of the proposed decision-making framework was verified by deriving the operation protocol for two test networks by making trade-offs between the multiple and conflicting microbiological, chemical, and organoleptic quality criteria. The disinfection by-products formation control and taste and odor problems control emerged as the most critical water quality criteria determining the WDS performance under the operating alternatives examined. Altogether, the obtained results suggested the practicality of adopting a flexible operation protocol to maintain the water quality benchmarks over various plausible WDS performance scenarios, ranging from worst to best.

Performance analysis of machine learning algorithms and screening formulae for β-thalassemia trait screening of Indian antenatal women

BACKGROUND

Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCS_BTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16].

METHODS

A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS.

RESULTS

It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCS_BTT. K-means clustering and the ranking from MCDM methods show that SCS_BTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies.

CONCLUSION

Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCS_BTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCS_BTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

A spatial directivity-based sensitivity analysis to farmland quality evaluation in arid areas

Multi-criteria decision-making (MCDM) is an important means for evaluating resources and environment, and sensitivity analysis can enhance understand the robustness of evaluation results. Spatial visualization has been used in sensitivity analysis of MCDM, but the sensitivity results are still generally summarized by presenting traditional statistical measurements that omit the spatial information. To address this issue, this paper proposed a novel spatially measurement approach of sensitivity analysis by introducing the spatial barycenter model (SBM), which overcame the limitations of existing statistical methods and provided the spatial directivity of uncertainty for the MCDM results. According to our proposed method and its application in farmland quality evaluation (FQE) in an arid area of China, the mean of the absolute average change rate (MACR) and the SBM were applied to test the sensitivity of farmland quality to different evaluation factors from both numerical and spatial perspectives. From the numerical perspective, the soil organic matter and irrigation capacity were the most sensitive factors determined by the MACR. From the spatial perspective, the ≥10 °C accumulated temperature (AT) and precipitation were the most sensitive factors measured by the SBM. Based on the SBM, the spatial configuration of farmland quality index was most sensitive to increase of AT in a northwesterly direction. Calculating the SBM is computationally inexpensive and provides a straightforward indication of spatial direction for the changes of FQE results with changes of parameters. This means it can provide improved understandings and new insights into the comprehensive measurement of sensitivity analysis and agricultural production layout.

A comparative analysis of three multi-criteria decision-making methods for land suitability assessment

Natural resource management relies on identifying the ecological constraints, assessing land suitability, and considering the socio-economic demands in the region. However, in many developing countries, natural resources are extensively overused in favor of economic growth. This is due to the fact that conservation and natural constraints are not always taken into consideration during the planning phase, especially when the decision-making process is mainly influenced by political or economical views. To avoid these subjective plannings, environmental planners are encouraged to consider quantitative planning approaches that can integrate environmental, social, economic, and political matters through a non-bias procedure. The present study, therefore, examines the application of three multi-criteria decision-making methods (MCDM), namely, analytic hierarchical process (AHP), fuzzy analytic hierarchical process (fuzzy AHP), and technique for order of preference by similarity to ideal solution (TOPSIS), for the assessment of land suitability afforestation. Siahpoosh Watershed, in Iran, is used as a case study to compare three MCDM methods. To achieve this, a set of land suitability criteria (i.e., slope, elevation, aspect, soil texture, soil depth, drainage, erosion, temperature, rainfall, and vegetation type and cover) was defined and weighted using the AHP and fuzzy AHP methods. TOPSIS was then used to prioritize and rank the suitability of different sections of the study area for afforestation. The study demonstrates that the fuzzy AHP method combined with TOPSIS generates more reliable outcomes than the AHP method. The results could be useful for making more informed decisions about afforestation in the region.

A GIS-based novel approach for suitable sanitary landfill site selection using integrated fuzzy analytic hierarchy process and machine learning algorithms

Disposal of waste without treatment is the least preferable way of sustainable solid waste management (SWM). But most cities in developing nations still use open dumps, causing negative impacts on the environment and human health. This study offered a novel approach for selecting landfill sites and sustainable SWM in Aligarh city, India. This was done through data collection, selecting models for criterion weighting, and validation. In order to prepare a landfill site suitability map, a geographic information system (GIS)-based ensemble fuzzy analytic hierarchy process-support vector machine (FAHP-SVM) and fuzzy analytic hierarchy process-random forest (FAHP-RF) models were implemented. Considering the previous studies and the study area characteristics, eighteen thematic layers were selected. The result revealed that land value; distance from residential roads, hospitals and clinics, and waste bins; and normalized difference built-up index (NDBI) have a fuzzy weight greater than 0.10, indicating significant factors. In contrast, land elevation, land slope, surface temperature, soil moisture index, normalized difference vegetation index (NDVI), and urban classification have a zero fuzzy weight, indicating these criteria have no importance. The result further revealed that FAHP-RF with an area under curve (AUC) value of 0.91 is the more accurate model than FAHP-SVM. According to the final weight-based overlay result, seven potential landfill sites were identified, out of which three were determined as most suitable by considering current land cover, public opinions, and environmental and economic concerns. This research proposed a zonal division model based on landfill sites location for sustainable SWM in Aligarh city. However, the findings may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

Balancing the push and pull factors of land-use change: a New Zealand case study

New Zealand is increasingly facing environmental and social challenges associated with its current land-use choices. There is therefore a drive to find ways to continue to add value to its primary sectors, which are of significant economic value to the country whilst at the same time mitigating the externalities associated with the use of land in primary production. Next-generation systems (NGS) are identified as potentially being able to address these challenges. Through the application of a multi-criteria decision making tool, this paper identifies the factors that are important to individual land managers in terms of choice of land-use and how these factors may act as barriers or facilitators of change. By examining land-use change as a combination of push and pull factors between alternative systems, this paper highlights the complex and context specific nature of decision-making at the individual land-manager level and the importance of risk perceptions. It argues that simply pushing land managers away from land-uses that have "undesirable" characteristics through regulation is unlikely to lead to a sustainable transition without the existence of viable alternatives. There is a need to balance increasing the risk of current land-uses whilst at the same time reducing the risk of transitioning to next-generation systems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10113-021-01865-0.

Modeling the blockchain readiness challenges for product recovery system

Product Recovery System (PRS) transfers products from their typical final place to their source to arrest some value on the product. There are obstructions, such as costs, associated with the modification of accounts and assessment of products and refunds associated with the implementation of PRS. Blockchain Technology (BCT) emerged as an innovative approach to constructing trust in a trust less environment and assures the availability, traceability, and security in data management. It also presents a valuable solution to PRS. This study aims to analyze the Blockchain Readiness Challenges (BRCs) to PRS in the context of manufacturing industries. The study observes 20 readiness challenges linked with the implementation of BCT in PRS. The BRCs are identified from the literature survey and confirmed after consequent examinations with industry experts and researchers. The study employed a Multi-Criteria Decision-Making (MCDM) i.e., the Decision-Making Trial And Evaluation Laboratory (Fuzzy DEMATEL) approach to find the cause-and-effect interactions to prioritize BRCs. The Maximum Mean De-Entropy (MMDE) algorithm was adopted to establish the threshold value based on the information entropy of the interactions among the BRCs for PRS. The fuzzy set theory was adopted to tackle the uncertainty and vagueness of personnel biases and data deficiency problems. The findings from this study reveal that inadequate financing for PRS exercises, lack of governance and standards, and security challenges to BCT implementation are the most influential readiness challenges for the adoption of blockchain in PRS. The study is useful to manufacturing organizations for identifying the potential BRCs to implement PRS among all existing readiness challenges so that they can take suitable measures before proceeding to adopt blockchain in PRS. The managers are suggested to eliminate the readiness challenges and widen the blockchain technology adoption in PRS.

Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.