A novel MCDM approach to selecting a biodegradable dynamic plastic product: a probabilistic hesitant fuzzy set-based COPRAS method.

The development of biodegradable polymers for both industrial and commercial uses is crucial nowadays due to the detrimental environmental effects of synthetic plastics. For a variety of uses, researchers have created numerous starch-based composites. The current study examines bioplastics made from maize and rice starch for packaging purposes. Several types of bioplastic samples are created using various ratios of gelatin, glycerol, citric acid, maize starch, and rice starch. People have discovered the value of plastics all around the world. It can be used for packaging, trash bags, liquid containers, throwaway quick service restaurant products, and other things. Regarding the negative aspect of plastics, their dumping after durability poses a serious risk to both people and wildlife. This prompted researchers to seek alternative natural resources that may be used to create flexible polymers that are recyclable, eco-friendly, and sustainable. It has been discovered that tuber and grain starches can be used to produce flexible biopolymers. The decision to choose the best among these choices is an MCDM problem because the carbohydrates from these suppliers have varying qualities. The Probabilistic Hesitant Fuzzy Set (PHFS)-based COmplex PRoportional ASsessment (COPRAS) method for solving uncertainty problems is utilized in this research study. To get the objective weights of the criteria in this case, we used the Critic method of weight determination. An example case of selecting the optimal hydrolyzes for biodegradable dynamic plastic synthesis was chosen to represent the applicability of the suggested approach. The findings demonstrate the feasibility of thermoplastic starches derived from rice and corn for packaging applications.

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method.

The process of developing and implementing sustainable strategies to prevent spread of COVID-19 for society typically requires integrating all social, technological, economic, governmental aspects in a systematic way. Since the clear understanding of risk factors contribute to the success of the strategies applied against COVID-19, a risk assessment procedure is applied in this study to properly evaluate risk factors cause to spread of pandemic as a multi-complex decision problem. Therefore, due to the evaluation of risk factors, which often involves uncertain information, the model is constructed based on interval-valued q-rung orthopair fuzzy-COmplex PRoportional ASsessment (IVq-ROF-COPRAS) method. While the developed framework is efficient to enhance the quality of decisions by implementing more realistic, precise, and effective application procedure under uncertain environment, it has capability to help governments for developing comprehensive strategies and responses. According to the results of the proposed risk analysis model, the top three risk factors are "The Approach that Prioritizes the Economy in Policies", "Insufficient Process Control in Normalization" and "Lack of Epidemic Management Culture in Individuals and Businesses". Lastly, to show applicability and efficiency of the model sensitivity and comparative analysis were conducted at the end of the study.

An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation.

The high contagion rates of COVID-19 and the limited amounts of vaccines forced public health authorities to develop vaccinations strategies for minimizing mortality, avoiding the collapse of health care infrastructure, and reducing their negative impacts to societies and economies. We propose a Multi Criteria Group Decision Making for prioritizing a set of COVID-19 vaccination alternatives, under a picture fuzzy environment, where the weights for Decisions Experts (DE) and criteria are unknown. A panel of six DEs assess six criteria for prioritizing four groups for vaccination. The weights for DE and criteria are handled in the form of fuzzy sets. Three types of weights are calculated: subjective, objective, and mixture weights. According to our results, three out of the six criteria hold 60% of the strategic importance: 1) allocation and distribution, 2) COVID-19 strains and 3) capabilities and infrastructures. However, persons with comorbidities became the group with the highest priority, followed by essential workers, women, and adults older than 40 years. Governments, decision makers, and policy makers can find rigorous scientific evidence for articulating effective vaccinations campaigns from this work, and contribute to minimize undesired outputs, such as high mortality rates or collapse of hospitals.

A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic.

The COVID-19 pandemic caused important health and societal damage across the world in 2020-2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries' COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.

New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis.

The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods.

Multiobjective Optimization of Fabrication Parameters of Jute Fiber/Polyester Composites with Egg Shell Powder and Nanoclay Filler.

In the present study, a model is presented to optimize the fabrication parameters of natural fiber reinforced polyester matrix composites with dual fillers. In particular, jute fiber mat was chosen as reinforcement and eggshell powder (ESP) and montmorillonite nanoclay (NC) were selected as fillers. The weight per square meter (GSM) of the fiber, the weight percentage of ESP and NC have been chosen as independent variables and the influence of these variables on tensile, flexural and impact strength of the composite has been inspected. The permutations of the different combinations of factors are intended to accomplish higher interfacial strength with the lowest possible number of tested specimens. The experiments were designed by the Taguchi strategy and a novel multi-objective optimization technique named COPRAS (COmplex PRoportional ASsessment of alternatives) was used to determine the optimal parameter combinations. Affirmation tests were performed with the optimal parameter settings and the mechanical properties were evaluated and compared. Experimental results show that fiber GSM and eggshell powder content are significant variables that improve mechanical strength, while the nanoclay appears less important.

Environmental management partner selection for reverse supply chain collaboration: A sustainable approach.

The Indian electronics industry is facing immense pressure to include sustainability practices in order to meet customer expectations, comply with legislation and create an edge over competitors. This growing need for inclusion of sustainability is driving original equipment manufacturers (OEMs) to collaborate with third-party reverse logistics providers (3PRLPs) to sustainably manage returns. Collaboration with 3PRLP will put OEMs in a stronger position regarding compliance with government legislation, improving upon their corporate image and customer satisfaction. It is a win-win situation for the OEM. On the other hand, from the 3PRLP's point of view, it is important to know the capabilities of the OEMs before entering into a collaborative endeavour. Hence, it is firstly necessary to identify the most appropriate attributes of the manufacturer which are essential for a sustainable collaboration. In this context, the study proposes a novel framework for investigating the performance attributes of the OEMs from the economic, environmental and social aspects of sustainability. Since the evaluation of each attribute for each dimension of sustainability differs in terms of the nature of the information needed and the time and resources required, it is necessary to conduct a pre-evaluation of the attributes using the Complex Proportional Assessment (COPRAS) method. Furthermore, the shortlisted attributes for each sustainability dimension are evaluated and prioritized using the best worst method (BWM). The novelty of the attribute selection process lies in simultaneously considering the degree of importance of the attribute as well as the degree of difficulty of the collection of data required for the assessment of the OEM. The result of this study helps decision-makers and practitioners to comprehend the most influential attributes of OEMs which are crucial for collaboration, thus enhancing the overall sustainability impact of the supply chain. The managerial implications drawn from the resulting analysis provide the 3PRLP with a sustainable evaluation framework which can be ideally used for the selection of collaborative partners. The above model is validated using the case of an Indian 3PRLP company that handles electronic products.

A bipolar fuzzy decision-making system for assessing high-risk coexisting tuberculosis disease in pregnant women.

Tuberculosis (TB) diagnosis poses a formidable challenge in global healthcare, particularly impacting older individuals and pregnant women. Diagnosing TB disease during pregnancy and in comorbid patients is more challenging due to overlapping symptoms with normal pregnancy conditions and existing treatments for other diseases, necessitating careful assessment to differentiate TB symptoms from those of other underlying conditions. To address this issue, this study designs a novel bipolar fuzzy decision-support system by integrating the concept of complex proportional assessment (COPRAS) and a technique for order preference by similarity to the ideal solution (TOPSIS) approaches using bipolar heptagonal fuzzy numbers. The approach is utilized to assess the high-risk of TB coinfection disease in pregnant women. The bipolar fuzzy set provides positive and negative membership degrees of an element, which divulge a balanced perspective by both the presence and absence of the disease. Additionally, a defuzzification algorithm is proposed for bipolar heptagonal fuzzy numbers, converting bipolar heptagonal fuzzy into a bipolar crisp score (CBHpFBCS). The bipolar fuzzy entropy measure is utilized to weight the criteria. The findings highlight that TB+HIV (G3) coinfection is more severe in pregnant women compared to other TB comorbidities. Finally, sensitivity and comparative analyses are executed across diverse criteria weight scenarios and with existing fuzzy multi-criteria decision-making (MCDM) methods to validate the robustness of the proposed method and its outcomes.

Optimal evaluation of crop residues for gasification in Ghana using integrated multi-criterial decision making techniques.

Lack of sustainable feedstock quantity and quality has been reported to be one of the major challenges confronting operations of gasifier plants in Ghana. In this paper, TOPSIS (Technique for order of Preference by Similarity to Ideal Solution) COPRAS (Complex Proportional Assessment) and VIKOR (Multi-criteria Optimisation and Compromise Solution) were used to select optimal crop residues for sustainable gasification in Ghana among thirteen residue types. The Analytical Hierarchy Process (AHP) was used as weighting criteria for the three Multi-Criteria Decision-Making (MCDM) techniques. Thirteen criteria based on particle size, proximate, ultimate analysis, calorific values, and quantity of residues were used. Five scenarios were considered; ranking of individual feedstock, consideration of the combination of the feedstock grouped under four categories. The ultimate and proximate analysis of the various crop residues as determined were generally within recommended levels as reported in the literature. Cashew shells and cassava peels have the highest heating value and annual technical residue potential of 23.4 MJ/kg and 880,750 tonnes respectively. Ranking of the individual crop residues confirmed cassava peels as the best alternatives of all the three MCDM techniques. The average rankings of the alternatives from the three MCDM in the order of best to worst are cassava peels, maize stalk, cocoa pod husk, maize cobs, rice straw, shea nut husk, groundnut husk, Palm kernel shells, EFB, rice husk, coconut husk/shells, cashew shells, and shea nut cake. Rankings of the alternatives under the four categories considered showed that feedstock blends containing four residues or more offer better opportunities for sustainable gasification than individual feedstock. Statistical analysis shows that there is a very strong, positive correlation between VIKOR, COPRAS, and TOPSIS. The strongest correlation is between VIKOR and TOPSIS with spearman's rank correlation index of 0.98. The analysis was sensitive to the weight of the strategy of group utility, recoverability ratio, residue-to-product ratio and annual crop production figures. It is recommended that optimal gasifier design and operational conditions taken into consideration the various feedstocks and their combination as determined from this study must be studied.

Understanding the exchange process between ground and surface water using mini drive point piezometer and mathematical models to identify suitable managed aquifer recharge sites.

In the current study, subsurface characteristics within the complex formation of the Shilabati basin system of West Bengal, India, extending over an area of 3888 km2, have been estimated using a cost-effective piezometer and MIKE FEFLOW package based on a steady-state numerical model. Pore size and fine particle content of streambeds are affected by two opposing flow contraptions. Such opposite flow conditions are likely to affect the hydraulic conductivity of the streambed. However, analogies of the hydraulic conductivity (Kh) of streambeds for losing and gaining streams have not been well documented in the recent past. The Kh value from the piezometer has been highest at the Dakshin Pairachali site (6.765 m/day), with the stream gaining water from the discharge of the local aquifer. Analysis of the stream-aquifer interaction using the FEFLOW model has allowed us to understand the groundwater water head of the basin ranging from 160.33 to 0.32 m.a.s.l (meters above sea level). The present study also constitutes the first attempt for the identification of suitable sites for the implementation of managed aquifer recharge (MAR) technology in West Bengal, India, to manage extreme drought events. The suitable sites have been identified by means of three fuzzy multi-criteria decision analysis based on nine criteria: river discharge, moisture content, porosity, drainage type, rainfall, land use type, geology, aquifer material, and hydraulic conductivity. To design a radial collector well and infiltration gallery for the selected site in an anisotropic, homogeneous, unconfined, and semi-infinite aquifer near a fully penetrating stream, a pumping test has been conducted to optimize a safe yield of 12.096 MLD (megaliters per day).

A hybrid DEMATEL-COPRAS method using interval-valued probabilistic linguistic term set for sustainable hydrogen fuel cell supplier of new energy vehicles.

With the continuous development of the global economy, global environmental pollution, climate degradation and global warming are becoming increasingly serious. In order to deal with the increasingly serious environmental problems, the government is vigorously supporting and promoting the development of new energy vehicles (NEVs). As the core unit of NEVs, one of the main challenges faced by hydrogen fuel cell (HFC) supplier is to select the best supplier for their business among all possible suppliers. Selecting the optimal supplier is a key decision in green supplier management. Therefore, it is extremely important and meaningful to select an optimal HFC supplier to provide power for NEVs. This paper proposes a new decision-making framework based on Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and Complex proportional assessment (COPRAS) method under interval-valued probabilistic linguistic environment to select the appropriate HFC supplier of NEVs. Firstly, this paper establishes the evaluation criteria system of HFC supplier assessment which is the synthesis of economical, environmental, social, technical, organisation and service aspects. Then, in order to express the uncertainty of expert decision-making, this paper uses interval-valued probabilistic linguistic term set (IVPLTS) to describe the evaluation information. Next, the interval-valued probabilistic linguistic term set decision-making trial and evaluation laboratory (IVPLTS-DEMATEL) method is applied to calculate the criteria weights. Moreover, this paper constructs the interval-valued probabilistic linguistic term set Complex Proportional Assessment (IVPLTS-COPRAS) model for the selection of HFC supplier of NEVs. Finally, a case in China with sensitivity analysis and comparison analysis are executed to illustrate the feasibility and validity of the proposed approach. This paper provides valuable references for investors and companies to select the most appropriate HFC supplier of NEVs under uncertain environment.

Application of q-rung orthopair fuzzy based SWARA-COPRAS model for municipal waste treatment technology selection.

Despite several methods available for the treatment of solid wastes, the management of municipal solid waste is still a crucial and complex process. The available methods for waste treatment range from advanced to conventional techniques. The identification of a proper method for municipal solid waste management involves several techno-eco and environmental considerations. To solve the real-world problems of municipal waste management, the research proposed an integrated q-rung orthopair fuzzy number-based stepwise weight assessment ratio analysis-complex proportional assessment (SWARA-COPRAS) mathematical model to rank the waste treatment techniques. The research aimed to develop a systematic approach for a suitable selection of waste treatment methods. Ten (10) different alternatives for waste treatments were ranked against seven (07) different techno-eco and environmental criteria. The ambiguity in the decision was handled by the q-rung orthopair fuzzy numbers. The proposed integrated model has identified upcycling and recycling of waste having priority values of 100% and 99.9%, respectively, as the suitable practices for the successful management of generated solid wastes, whereas landfilling has obtained a minimum priority value of 66.782% and, therefore, is least preferable for waste management. The ranking of the alternatives followed the sequence as upcycling > recycling > pyrolysis > hydrolysis > biotechnological > core plasma pyrolysis > incineration > composting > gasification > landfilling. The comparison between the rankings of the proposed model with other techniques has revealed that the values of Spearman's rank correlation coefficient are in the range of 0.8545 to 0.9272; thereby, the robustness of the proposed model is verified. Sensitivity analysis for the criteria weight has showed that the ranking results are influenced significantly by the change in criteria weights and suggested that an accurate estimation of the criteria weight is decisive in determining the overall ranking of the alternative. The study has provided a framework for decision-making in the technology selection for solid waste management.

A two-phased decision-making based grey theory framework for the best choice of payment methods in international trade.

This study aims to propose a two-staged Analytic Hierarchy Process and COmplex PRoportional ASsessment model-based Grey Theory (AHP-G and COPRAS-G) to explore critical elements affecting international payment method selection in the case of import-export Small and Medium-Sized Enterprises (SMEs) from emerging countries. First, the AHP-G model is applied to determine the relative weights of essential factors. Second, the COPRAS- G approach prioritizes international payment method alternatives. From the literature review and experts' opinions, five main criteria were established, including: "relationship between the two parties" (RE), "type of goods" (GO), "political-economic framework factor" (MA), "characteristics of payment method" (PA); "opinions and requirements of external parties" (EX) and four international payment methods are considered as frequent payment alternatives. A real-world case study from Vietnamese SMEs in the import-export industry is presented to exemplify the applicability and robustness of the proposed approach. The weighting results indicated that (PA) had the most significant impact on international payment method selection, followed by (MA) and (RE). Letter of credit (L/C) is considered the most reliable payment method for SMEs. Furthermore, a comparative analysis was performed to examine the validity of the proposed model. The proposed approach would assist stakeholders in import-export SMEs and other industries in effectively selecting suitable payment methods in international trade.

Evaluating and improving the sustainability of ecosystem services in river basins under climate change.

This paper presents a new framework for evaluating the sustainability of basin-wide ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services. In this framework, the Soil and Water Assessment Tool (SWAT) and MODSIM1 models and experts' opinions are used to evaluate the ESs. To show the applicability of the proposed framework, it is applied to the Zarrinehrud river basin under three different climate change (CC) scenarios (i.e., RCP 4.5, 6.0, and 8.5) for two different time horizons (i.e., 2020-2049 and 2020-2098). This basin is the main water supplier of the largest hypersaline lake in the Middle East, Lake Urmia. In the next step, 128 water resources management (WRM) scenarios are taken into account considering the projects defined by Urmia Lake Restoration National Committee (ULRNC). All ecosystem services are evaluated considering all WRM and CC scenarios. Finally, a group COPRAS-based decision-making approach is used to determine the best WRM scenario under climate change. The results show that WRM scenario 128 is the best scenario for improving ecosystem services in the study area. This scenario includes some projects such as allocating water to the lake from new resources, rehabilitating irrigation and draining networks, and improving cropping patterns.

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection.

Selecting the optimal renewable energy source (RES) is a complex multi-criteria decision-making (MCDM) problem due to the association of diverse conflicting criteria with uncertain information. The utilization of Fermatean fuzzy numbers is successfully treated with the qualitative data and uncertain information that often occur in realistic MCDM problems. In this paper, an extended complex proportional assessment (COPRAS) approach is developed to treat the decision-making problems in a Fermatean fuzzy set (FFS) context. First, to aggregate the Fermatean fuzzy information, a new Fermatean fuzzy Archimedean copula-based Maclaurin symmetric mean operator is introduced with its desirable characteristics. This proposed operator not only considers the interrelationships between multiple numbers of criteria, but also associates more than one marginal distribution, thus avoiding information loss in the process of aggregation. Second, new similarity measures are developed to quantify the degree of similarity between Fermatean fuzzy perspectives more effectively and are further utilized to compute the weights of the criteria. Third, an integrated Fermatean fuzzy-COPRAS approach using the Archimedean copula-based Maclaurin symmetric mean operator and similarity measure has been developed to assess and rank the alternatives under the FFS perspective. Furthermore, a case study of RES selection is presented to validate the feasibility and practicality of the developed model. Comparative and sensitivity analyses are used to check the reliability and strength of the proposed method.

An integrated method for hybrid distribution with estimation of demand matching degree.

Timely and effective distribution of relief materials is one of the most important aspects when fighting with a natural or a man-made disaster. Due to the sudden and urgent nature of most disasters, it is hard to make the exact prediction on the demand information. Meanwhile, timely delivery is also a problem. In this paper, taking the COVID-19 epidemic as an example, we propose an integrated method to fulfill both the demand estimation and the relief material distribution. We assume the relief supply is directed by government, so it is possible to arrange experts to evaluate the situation from aspects and coordinate supplies of different sources. The first part of the integrated method is a fuzzy decision-making process. The demand degrees on relief materials are estimated by extending COPRAS under interval 2-tuple linguistic environment. The second part includes the demand degrees as one of the inputs, conducts a hybrid distribution model to decide the allocation and routing. The key point of hybrid distribution is that each demand point could be visited by different vehicles and each vehicle could visit different demand points. Our method can also be extended to include both relief materials and medical staffs. A real-life case study of Wuhan, China is provided to illustrate the presented method.

Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem.

With the increasing risk to human health and environmental issues, the selection of appropriate management and treatment of healthcare waste has become a major problem, especially in developing countries. There are various alternatives to dispose of health care waste. The important is to assess the best alternative among them. The assessment of each alternative should be done based on public health, psychological, economic, environmental, technological, and operational aspect. The selection of the best health care waste treatment (HCWT) alternative is a complicated, multi-criteria decision-making (MCDM) problem involving numerous disparate qualitative and quantitative features. Hence, in this research article, the MCDM method is presented for estimating and choosing the best alternative of HCWT by COPRAS technique in a Pythagorean fuzzy set (PFS). Here, in this paper, first of all, a new entropy measure on PFSs is proposed and its validity is studied. Thereafter, the MCDM technique Complex Proportional Assessment (COPRAS) is discussed in which the criteria weights are assessed by the proposed entropy measure and score function to enhance an efficacy and efficiency of the proposed technique. Furthermore, the above-defined technique is employed to resolve the real-life problem to obtain the best treatment alternative to disposal of the health care waste. Finally, sensitivity analysis is presented to rationale the proposed viewpoint for prioritizing HCWT alternatives.

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment.

COVID-19, which emerged in December 2019, has affected the entire world. Therefore, COVID-19 has been a subject of research in various disciplines, especially in the field of health. One of these studies was the report made by the Deep Knowledge Group (DKG) consortium in which safe regions for COVID-19 were determined. In the report, the main criteria of quarantine efficiency, government efficiency of risk management, monitoring and detection, health readiness, regional resilience, and emergency preparedness are used in the evaluation of countries and regions (alternatives). As the data and research structure used in this report are based on multi-criteria, the purpose of this study is to evaluate and analyse the safety levels of 100 regions in the world in terms of COVID-19 using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Complex Proportional Assessment (COPRAS) methods. The data and information required in the methods were obtained from a report prepared by the DKG. The results of the methods were compared with the ranking results presented in a report of the DKG. Accordingly, it has been observed that the method that provides the closest results to the results of the report is the COPRAS method, and the method that gives the most distant results is the VIKOR method.

Inter-basin water transfer planning with grey COPRAS and fuzzy COPRAS techniques: A case study in Iranian Central Plateau.

To overcome water scarcity and increasing water demands, achieving economic and social development, and to make regional equilibrium especially in countries with climate variations such as Iran, the inter-basin water transfer is considered as one of the crisis reduction methods. In this study, the COPRAS method, which is one of the new MCDM methods, is used for the first time to evaluate inter-basin water transfer projects in three configurations of integer, fuzzy, and grey (intervals). For this purpose, eight inter-basin water transfer scenarios from the Great Karoon Basin to the Central Iranian Plateau, are determined with the aim of overcoming drinking water scarcity. They are assessed by four important and practical criteria for inter-basin water transfer, including technical risks and the difficulty of execution, political and social problems, environmental problems, and cost per cubic meter of water according to the UNESCO standards. The weights of the criteria were obtained using three methods of AHP, DEMATEL, and Shannon entropy in the integer, fuzzy, and grey ways. Then, scenarios were prioritized using the COPRAS method. Finally, the results were ranked using the Borda count method, which is a method to aggregate the different ranking models. In this study, the results showed that among the criteria, technical risks, and then the cost per cubic meter of water are the essential criteria, respectively. Considering all ranking methods shows that among the scenarios, the eighth scenario, including water transfer from Behesht-Abad basin to Isfahan province and from Kharsan basin to Yazd and Kerman provinces, is superior to the other scenarios. Final COPRAS value of this scenario varies between 0.155 and 0.194 depending on the applied ranking method, and its total Borda score is 63. The findings of this study can help the decision-makers to evaluate the inter-basin water transfer projects in uncertain conditions.