Health-care Waste Treatment Technology Research Articles

A novel Fermatean fuzzy BWM-VIKOR based multi-criteria decision-making approach for selecting health care waste treatment technology

Medical waste management (MWM) is a challenging issue for medical facility managers owing to its potential to prevent environmental and health risks. Many treatment technologies (TTs) have been used for MWM, and selecting appropriate TTs is a complex multi-criteria decision-making (MCDM) problem. In this paper, we propose a novel integrated MCDM method based on the best-worst method (BWM) and the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method under the Fermatean fuzzy environment to evaluate, rank, and select treatment technologies for medical waste management. First, novel distance measure and entropy measure for Fermatean fuzzy sets are developed, and their properties are examined. Second, different treatment technologies are evaluated by experts using Fermatean fuzzy sets. Third, the experts’ weights are determined using a novel method based on the entropy measure, and the criteria weights are calculated by a novel hybrid criteria weight calculation method. Finally, the ranking of different treatment technologies is determined by the proposed method. To validate the proposed method, a real case study in Jinan, China is presented, where the proposed method is applied to determine the optimal health care waste (HCW) treatment technologies from five TTs with eight criteria. The results are compared to other MCDM methods, which shows the effectiveness and reliability of the proposed method. Moreover, sensitivity analysis is also carried out to show the robustness of the proposed method. From the results, it can be found that the proposed method could provide reliable and robust results for obtaining the optimal health care waste treatment technology.

A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection

Healthcare waste (HCW) management is a complex issue influenced by many factors, including technological, economic, environmental, and social factors. It is possible to regard the evaluation of the best treatment technique for HCW management as a challenging case of MCDM (multi-criteria decision-making), where various alternatives and evaluation criteria must be considered. The presentation and handling of the shaky data are crucial to choosing the HCW treatment technology. In order to address the issue of MCDM issues with Fermatean fuzzy (FF) data, we first build a consensus-based WASPAS approach in this study. In the suggested integrated methodology, the rank of the alternatives is determined using the WASPAS method in an FF environment, and the attribute weights are estimated using the entropy measure technique. In the preceding, an HCW treatment technology assessment issue is considered to make the proposed structure's applicability more transparent. In this study, four HCW treatment methods—chemical disinfection, microwave disinfection, cremation, and autoclaving—are considered options. According to the study's findings, autoclaving is the most effective HCW treatment method. Additionally, we demonstrate a sensitivity assessment using several criteria weight sets to test the stability of our intriguing proposed approach. We also call attention to a contrast between our suggested approach to decision-making and the practices now in use.

Social Assessment of Healthcare Waste Management

Circular economy targets clearly define recovery and recycling as predominant actions for future waste management. However, circularity-oriented actions might not be easily implemented for some types of waste. Healthcare sectors generate non-hazardous municipal waste (up to 80–85 % of all generated waste according to the World Health Organization data) and hazardous infectious waste. Due to public health and the spread of infection risks, healthcare waste treatment needs to be organized in a special manner and moreover, recovered materials need to be tested repeatedly. The present paper aims to analyse the social aspects of health care waste recovery technologies along the supply chain (gate-to-gate approach) – from health care waste collection at medical care institutions to recovered materials at treatment plants. Rotoclave and chemical treatment healthcare waste treatment technologies were analysed as case studies. Methodological approach of the study is based on the UNEP/SETAC guidelines for social life-cycle assessment (S-LCA) of products and questionnaire-based indicators. The results of the assessment will bring the framework for improvement of social aspects of healthcare waste management.

Fermatean Fuzzy IWP-TOPSIS-GRA Multi-Criteria Group Analysis and Its Application to Healthcare Waste Treatment Technology Evaluation

The growth of healthcare waste (HCW) was driven by the spread of COVID-19. Effective HCW eradication has become a pressing global issue that requires immediate attention. Selecting an effective healthcare waste treatment technology (HCWTT) can aid in preventing waste buildup. HCWTT selection can be seen as a complex multi-criteria group evaluation problem as the process involves multiple types of criteria and decision-makers (DMs) facing uncertain and vague information. The key objective of this study is to create a useful tool for the evaluation of HCWTT that is appropriate for the organization’s needs. A novel index system for assessing the HCWTT during the decision-making evaluation process is first presented. Then a new approach based on entropy measure, decision-making trial and evaluation laboratory (DEMATEL), and game theory for the integrated weighting procedure (IWP) is presented under a Fermatean fuzzy environment. A multi-criteria group analysis based on IWP, a technique for order of preference by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), named IWP-TOPSIS-GRA framework suited to Fermatean fuzzy evaluation information, is developed. In a real-world case of HCWTT selection, through comparative analysis and sensitivity analysis, it is verified that the presented method is feasible and robust.

Novel Fermatean Fuzzy Bonferroni Mean aggregation operators for selecting optimal health care waste treatment technology

Fermatean fuzzy set (FFS) is an expedient tool in today’s world for coping with uncertainty, ambiguity, and incompleteness that emerge in many decision-making processes. In light of the relevance of FFS, this work offers a Multi-Criteria Group Decision Making (MCGDM) method together with Bonferroni mean and weighted Bonferroni mean operators in FF environment. These operators consider the interdependencies between the parameters into account during aggregation process in group decision making. On the other hand, health care waste (HCW) is an unavoidable byproduct of healthcare institutions, and so HCW management is vital owing to its negative influence on public health and environment. In this study, the proposed FFMCGDM has been applied to determine the optimal HCW treatment technologies (TT) for assuring sustainable environmental development, considering six TTs as alternatives assessing against nine criteria. Moreover, the proposed approach has been illustrated through an empirical case study of many district hospitals in Tripura, India. The obtained results are compared to other two well-known MCDM techniques as well as existing Fermatean Fuzzy Aggregation Operators (FFAOs). A sensitivity analysis has also been carried out to examine the stability of the model. From the analysis, it has been found that the proposed technique is competitive to obtain the optimal HCW TT. For further validation of the proposed method, a well-known MCDM problem, namely Green Supplier Selection (GSS) has been modeled and solved.

A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information

A generalized orthopair fuzzy set can express uncertain information easier than most other processes, which gives us more space for decision-making. In recent times, the selection of healthcare waste treatment (HCWT) technology can be considered as multi-criteria decision-making (MCDM) problem due to the involvement of multiple conflicting criteria. To reduce the unhealthy impact on the ecosystem and promote sustainable disposal, researchers have investigated MCDM problems to select apt HCWTs. In a bid to the minimize negative environmental impact of healthcare waste, researchers adopted MCDM, and faced challenges such as: (i) handling uncertainty/subjective randomness; (ii) imputation of missing values; and (iii) consideration to experts’ attitude and interdependencies during MCDM process. To remedy these, this paper has attempted to establish a novel decision model with generalized orthopair fuzzy information (GOFI). Initially, missing values are systematically imputed. Experts' preferences were fused to obtain an aggregated matrix by considering the interdependencies among experts. Also, criteria weights were computed utilizing attitude-based entropy measures, and HCWTs were ranked using the GOFI-evaluation based on distance from average solution (GOFI-EDAS) approach. Lastly, the methodology's superiority was validated using an illustrative example, followed by a comparison with extant models. The results confirm that the developed framework is more efficient than and consistent with earlier methods under uncertainty.

Multiple Attribute Group Decision-Making Based on Intuitionistic Fuzzy Schweizer-Sklar Generalized Power Aggregation Operators

An intuitionistic fuzzy set (IFS) is a valuable tool to execute uncertain and indeterminate information. IFSs are more suitable to identify decision-maker’s evaluation data in decision-making problems. Intuitionistic fuzzy aggregation operators (AOs) are of enormous consequences in multiple attribute group decision-making (MAGDM) problems with an intuitionistic fuzzy environment. Consequently, the main impacts of this article are: firstly, to instigate various new novel generalized power AOs based on Schweizer-Sklar operational rules for IFS. Secondly, the study aims to discuss characteristics and particular cases of AOs. The core edge of proposed AOs is that they can eradicate the influence of uncomfortable data which could be too high or too low, making them more admirable for efficiently solving more and more complex MAGDM problems. Thirdly, we instigate two new algorithms to deal with MAGDM established on the generalized Schweizer-Sklar power AOs. Lastly, we appertain the anticipated method and algorithms to health care waste treatment technology selection (HCW-TT) to show the competence of the anticipated method and algorithms. The prevailing novelties of these items are duplex. Firstly, new generalized AOs established on Schweizer-Sklar operational rules are initiated for IFNs. Secondly, two new approaches for IF MAGDM are initiated, one for known decision-makers (DMs) and attributes weights, while the other for unknown DMs and attributes weights.

Cloud decision support framework for treatment technology selection of health-care waste

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods.

An integrated qualitative group decision-making method for assessing health-care waste treatment technologies based on linguistic terms with weakened hedges

The assessment of the treatment technologies is a vital point for health care waste (HCW) management. The assessment process is regarded as a multi-attribute group decision-making problem. Considering individual knowledge backgrounds and psychological preferences, and various uncertainty factors which are difficult to describe numerical and precisely, experts often portray their individual assessments by employing qualitative expressions. Linguistic term with weakened hedge (LTWH) is an effective tool to quantify the qualitative information. Additionally, the Bonferroni mean (BM) can capture the interrelationship between input data and the combined compromise solution (CoCoSo) method could produce reliable outcomes. In this paper, we extend the BM operator into the LTWH environment and propose the linguistic term with weakened hedge BM (LTWHBM) operator and the weighted version, i.e., LTWHWBM, to depict the indeterminacy in the HCW management and cope with assessment of the treatment technology problems based on the developed novel operational rules of LTWHs. Various properties and special cases of these operators are investigated in detail. Furthermore, we design a LTWHWBM based CoCoSo group decision-making model, herein the LTWHWBM operator is employed to integrate individual experts’ preferences and the extend CoCoSo method is utilized to yield the ranking of alternatives. Ultimately, a case study concerning the assessment of the HCW treatment technologies is performed. The feasibility and favorable characteristics of the established model are justified by the comparisons with several prevailing methodologies and sensitivity analyses.

A novel framework for selecting sustainable healthcare waste treatment technologies under Z-number environment

Health-care waste (HCW) management has been regarded as an increasingly important issue in environmental protection. The evaluation and selection of HCW treatment technologies are essential in HCW management. In this paper, we present a novel group decision making framework based on Z-numbers and the TODIM method to synthesise the opinions of the experts and assist them in selecting an optimal HCW treatment technology. This framework comprises three phases, namely, (I) transformation of decision-making information, (II) determination of the criteria weights and (III) ranking of the alternatives. It can take advantage of Z-numbers to express the opinions of experts and to analyse their reliability. The TODIM method captures the bounded rationality of these experts and enhances the reasonability of the selection. An illustrative example is presented to demonstrate the feasibility of the proposed framework.

Establishing an interval-valued fuzzy decision-making method for sustainable selection of healthcare waste treatment technologies in the emerging economies

Healthcare services provided by hospitals and clinics inevitably produce waste that may hazardous to the environment and society. However, there is a lack of an effective and comprehensive evaluation framework that takes uncertainty and fuzziness into account to assess healthcare waste treatment technologies in the emerging economies. The objective of this paper is to present a new integrated multi-criteria decision-making method based on interval-valued fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and interval-valued fuzzy TOPSIS for evaluating healthcare waste treatment technologies in the emerging economies from a sustainability perspective. In this study, the decision makers are allowed to determine the weights of the evaluation criteria and prioritize the alternatives using linguistic variables. The weights of the evaluation criteria are determined by the interval-valued fuzzy DEMATEL method, and the prioritization of the alternatives is determined by the interval-valued fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. Four alternatives for healthcare waste treatment technologies including incineration, steam sterilization, microwave and landfill are studied, and the results show that our established method is effective to help the decision-makers to determine the prioritization of the alternatives for healthcare waste treatment technologies.

New integrated approaches based on MC-HFLTS for healthcare waste treatment technology selection

Purpose Selecting the most appropriate healthcare waste treatment technology (HCWTT) is an uncertain and complex decision-making problem because there exist more than one alternative and many conflicting qualitative and quantitative criteria. However, the use of fuzzy and comparative values, instead of specific crisp values, provides more accurate results, so that the alternatives may be evaluated in accordance with hesitant human nature. The purpose of this paper is to select the best HCWTT using a hesitant fuzzy linguistic term set (HFLTS). Design/methodology/approach Five main criteria were identified for HCWTT selection, such as economic, social, environmental, technical and ergonomic criteria. In total, 19 sub-criteria were examined, and the hierarchy of the criteria was formed. The criteria weights were determined using the multi-criteria hesitant fuzzy linguistic term set (MC-HFLTS). The selection processes of incineration (A1), steam sterilization (A2), microwave (A3) and landfill (A4) alternatives were carried out using the multi-attributive ideal-real comparative analysis (MAIRCA) and multi-attributive border approximation area comparison (MABAC) methods. In the comparative analyses, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to an ideal solution (TOPSIS) methods were used. Findings The comparison of the results of the MABAC and MAIRCA methods with the results of VIKOR and TOPSIS methods indicated that A2 (steam sterilization) alternative was the best one and produced the same ranking of the technology alternatives (A2 > A3 > A1 > A4). As a result, the study concluded that these methods can be successfully used for HCWTT selection problems. Originality/value To the best of the authors’ knowledge, MC-HFLTS has not been used to select HCWTT in the existing literature. For the first time, MC-HFLTS&MAIRCA and MC-HFLTS&MABAC approaches were used in order to choose the best treatment method for healthcare waste under the effect of multiple conflicting hierarchical criteria. It has been provided that MABAC and MAIRCA select alternative choices by taking into consideration the hierarchical criteria. Unlike other studies, this study also considered ergonomic criteria that are important for people working during the process of using the treatment technology.

Multi-Attribute Group Decision Making Based on Intuitionistic Uncertain Linguistic Hamy Mean Operators With Linguistic Scale Functions and Its Application to Health-Care Waste Treatment Technology Selection

How to select an appropriate and effective health-care waste treatment technology (HCW-TT) is an especially important task in the management of health-care waste (HCW), which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the selection of HCW-TT, the expression of evaluation information given by decision makers (DMs) under uncertain decision-making environment and the reflection of interrelationships among multi-attributes are two critical issues. In response, a new MAGDM technique is presented for the selection of HCW-TT based on the intuitionistic uncertain linguistic Hamy mean with linguistic scale functions (LSFs). First, considering the lack of closeness and flexibility of existing operations, some new operations of intuitionistic uncertain linguistic variables (IULVs) are redefined by combining with LSFs. New expected value and accuracy function are also presented to compare IULVs. Then, based on the new operations of IULVs, the intuitionistic uncertain linguistic Hamy mean and its weighted version (IULWHAM) are proposed to aggregate IULVs. The proposed operators can simultaneously model the interrelationship among multi-inputs and handle the semantic translation requirements of different DMs. Meanwhile, several attractive properties and special cases of these two operators are studied and analyzed. Subsequently, a MAGDM method (IUL-MAGDM) is presented based on the proposed IULWHAM. Finally, a numerical example is given to demonstrate the proposed IUL-MAGDM method, and results indicate that the proposed IUL-MAGDM can effectively and flexibly handle the selection of HCW management by comparing with other IUL-MAGDM methods.

A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers

Health-care waste (HCW) management is a complex problem influenced by many aspects, such as, economic, environment, technical, social, etc. A selection of the best treatment technology for HCW management can be regarded as a complex multi-criteria decision making problem where a number of alternatives and multiple evaluation criteria are need to be considered. In addition, decision makers often express their personal assessments by using multi-granularity linguistic term sets. Due to the involvement of human judgment, various uncertainties are introduced in the HCW process. One critical issue of the HCW treatment technology selection is the representation and handling of uncertain information. In response, a novel multi-criteria decision making method is proposed for the HCW treatment technology selection problem based on an effective representation model of uncertain information, called D numbers. In the proposal, the assessment results of HCW treatment are expressed and modeledby D numbers. It provides a new framework for the HCW treatment technology selection problem in which it was effective and feasible to handle MCDM problems under various uncertainties environment. Comparing with the existing method, the proposed method is clear and concise. Finally, an empirical case study in Shanghai, China is illustrated to validate the feasibility and applicability of the proposed method. As shown in the results, the proposed method selects steam sterilization as the optimal technology to deal with the health-care wastes which is consistent with the existing work where fewer pollutants are discharged and non-hazardous residues are produced so that it decreases more impacts on the environment and public health. The experimental results demonstrate that the proposed method can handle the HCW treatment technology selection problem effectively under complex and uncertain environments.

Algorithm for Life Cycle Inventory of Medical Waste Treatment Technologies Emphasizing the Role of Treatment Efficiency

Treatment of infectious health care waste is a prerequisite to ensure environmental and public health protection. In case of infectious health care waste, treatment can be organised at (1) a centralised waste treatment company or (2) on-site at the health care institutions. The efficiency of the treatment process is defined through efficiency of infectious and pathogen agents destroyed during the process and thus ensured to not come into contact with the environment. However, from an all-inclusive perspective, the environmental effects of health care waste treatment technologies should also include those impacts generated at least during the operation of technologies. The current paper presents a life cycle inventory framework of three on-site health care waste treatment technologies with a special focus on life cycle inventory data collection incorporating the disinfection efficiency factor.

An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder

With increased worldwide awareness of environmental issues, healthcare waste (HCW) management has received much attention from both researchers and practitioners over the past decade. The task of selecting the optimum treatment technology for HCWs is a challenging decision making problem involving conflicting evaluation criteria and multiple stakeholders. In this paper, we develop an integrated decision making framework based on cloud model and MABAC method for evaluating and selecting the best HCW treatment technology from a multiple stakeholder perspective. The introduced framework deals with uncertain linguistic assessments of alternatives by using interval 2-tuple linguistic variables, determines decision makers’ relative weights based on the uncertainty and divergence degrees of every decision maker, and obtains the ranking of all HCW disposal alternatives with the aid of an extended MABAC method. Finally, an empirical example from Shanghai, China, is provided to illustrate the feasibility and effectiveness of the proposed approach. Results indicate that the methodology being proposed is more suitable and effective to handle the HCW treatment technology selection problem under vague and uncertain information environment.

Considerations for choosing appropriate healthcare waste management treatment technologies: A case study from an East Midlands NHS Trust, in England

Through their decision-making processes, organisations can play a key role in addressing global environmental challenges. However, to be effective, these processes need to be based on evidence. This paper aims to evaluate the ‘optimum’ healthcare waste treatment technology, using a National Health Service organisation in the East Midlands region of England, as the case study organisation. Using analytic hierarchy process as the research tool, this research determined that the ‘optimum’ approach was a mix of technologies. However, this result was largely driven by costs considerations. Thus the findings suggest the need for a holistic approach to the decision-making process for the procurement of their healthcare waste management services. The use of analytic hierarchy process generally worked well in informing the decision-making process.

Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method.

Health-care waste (HCW) management is a major challenge for municipalities, particularly in the cities of developing nations. Selecting the best treatment technology for HCW can be regarded as a complex multi-criteria decision making (MCDM) issue involving a number of alternatives and multiple evaluation criteria. In addition, decision makers tend to express their personal assessments via multi-granularity linguistic term sets because of different backgrounds and knowledge, some of which may be imprecise, uncertain and incomplete. Therefore, the main objective of this study is to propose a new hybrid decision making approach combining interval 2-tuple induced distance operators with the technique for order preference by similarity to an ideal solution (TOPSIS) for tackling HCW treatment technology selection problems with linguistic information. The proposed interval 2-tuple induced TOPSIS (ITI-TOPSIS) can not only model the uncertainty and diversity of the assessment information given by decision makers, but also reflect the complex attitudinal characters of decision makers and provide much more complete information for the selection of the optimum disposal alternative. Finally, an empirical example in Shanghai, China is provided to illustrate the proposed decision making method, and results show that the ITI-TOPSIS proposed in this paper can solve the problem of HCW treatment technology selection effectively.

Healthcare waste treatment technologies and health impacts of waste management

Healthcare waste includes all types of waste generated from human and veterinary medical activities (public or private), and encompasses diagnosis, prevention and curative treatments, research, and laboratory activities. The risks associated with healthcare waste and its management have gained attention across the world over last decades, and this has resulted in the increased recognition of the need for proper healthcare waste management. One of the most important factors in any type of waste management is undoubtedly safety and hence the basic safety for waste treatment workers on the site has to be guaranteed first. However, some waste treatment technologies, such as landfills and incinerators, release toxic substances which could cause significant health effects. There are two purposes of this paper: (1) to review the technologies available for the treatment of healthcare wastes (HCW) by indicating the major merits and drawbacks of them, and (2) to highlight adverse health implications of these technologies reported in the epidemiology to provide data and information regarding excess health risk of these technologies for further studies. If there are not a significant number of health effect investigations specifically related to HCW treatment and disposal, municipal waste will be used as a surrogate for healthcare waste.

Application of interval 2-tuple linguistic MULTIMOORA method for health-care waste treatment technology evaluation and selection

The management of health-care waste (HCW) is a major challenge for municipalities, particularly in the cities of developing countries. Selection of the best treatment technology for HCW can be viewed as a complicated multi-criteria decision making (MCDM) problem which requires consideration of a number of alternatives and conflicting evaluation criteria. Additionally, decision makers often use different linguistic term sets to express their assessments because of their different backgrounds and preferences, some of which may be imprecise, uncertain and incomplete. In response, this paper proposes a modified MULTIMOORA method based on interval 2-tuple linguistic variables (named ITL-MULTIMOORA) for evaluating and selecting HCW treatment technologies. In particular, both subjective and objective importance coefficients of criteria are taken into consideration in the developed approach in order to conduct a more effective analysis. Finally, an empirical case study in Shanghai, the most crowded metropolis of China, is presented to demonstrate the proposed method, and results show that the proposed ITL-MULTIMOORA can solve the HCW treatment technology selection problem effectively under uncertain and incomplete information environment.