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

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel-Alsina Function

The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brčko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ( C 4 ) received the highest weight, while the criterion Automation Level ( C 8 ) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel-Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel-Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process.

Assessment of healthcare waste treatment methods using an interval-valued intuitionistic fuzzy double normalization-based multiple aggregation approach

Healthcare waste management has been an extensively attractive topic recently since it is one of the key concerns regarding both environment and public health, predominantly in developing nations. The optimization of the treatment procedure for healthcare waste is indeed a complex "multi-criteria decision-making (MCDM)" problem that involves contradictory and interweaved critical criteria. To successfully handle this issue, this study extends the original method, named the "double normalization-based multi-aggregation (DNMA)" approach, with "interval-valued intuitionistic fuzzy sets (IVIFSs)" for decision-making problems taking criteria in terms of benefit or cost types. This method involves two target-based normalizations and three subordinate utility models. To estimate the criteria weights, we propose a new parametric divergence measure and discuss the feasibility of the developed divergence measure based on existing divergence measures for IVIFSs. Further, the developed framework is implemented to elucidate the "healthcare waste treatment (HCWT)" problem. The comparative and sensitivity analyses of the outcomes indicate that the proposed approach efficiently tackles the problem of HCWT selection. The outcomes show that steam sterilization (0.462) is the optimal one for HCWT. The prioritization options, obtained by presented approach, are dependable and suitable, which are steam sterilization ≻ microwave ≻ incineration ≻ landfilling.

A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels

The COVID-19 pandemic has led to exponential growth in COVID-19 medical waste (CMW) generation worldwide. This tremendous growth in CMW is a major transmission medium for COVID-19 virus and thus brings serious challenges to medical waste (MW) management. Designing an efficient and reliable CMW reverse supply chain in this situation can help to prevent epidemic spread. Nowadays, the assessment of CMW recycling channels has become a challenging mission for health-care institutions, especially in developing countries. It can be seen as a complex multi-criteria group decision-making (MCGDM) problem that requires the consideration of multiple conflicting tangible and intangible criteria. Nevertheless, few academics have been concerned about this issue. Moreover, current MCGDM methods have limited support for CMW recycling channel evaluation and they do not consider hospitals' reverse supply chain strategy when evaluating. Thus, this study presents a novel MCGDM approach based on intuitionistic fuzzy sets (IFSs) and the VIKOR method for assessing the capacity of CWM recycling channels. According to the characteristics of CMW, processing flow and the TOE (Technology, Organization and Environment) theoretical framework, we established a new CMW recycling channel capacity evaluation index system which makes our proposed method more targeted and efficient. In the decision-making process, we integrate the best-worst method (BWM) and entropy to determine the decision makers (DMs) weighting in a more comprehensive way, considering both subjective and objective criteria, which was ignored by many MCGDM methods. A new aggregation operator called IFWA is proposed by us, considering the priority of DMs. Based on both the ranking of capacity and disposal charges, we then position the alternatives in the recycling channel priority index (RCPI) matrix constructed by us. According to this PCPI matrix and the reverse supply chain strategy of hospitals, a more reasonable CMW allocation strategy is determined and a more efficient CMW reverse supply chain is designed. Finally, a real case study from Wuhan was examined to illustrate the validation of our approach.

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.

Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods

Disposal of healthcare waste is a key issue of environmental sustainability in the world. The amount of healthcare waste is increasing every day, and it is necessary to adequately dispose of this kind of waste. There are various treatments for healthcare waste disposal, of which incineration of healthcare waste is one of the solutions. This paper suggests a model for selection of the type of incinerators that best solve the problem of healthcare waste in secondary healthcare institutions in Bosnia and Herzegovina. In the selection of incinerators, extended sustainability criteria were applied. Basic sustainability criteria: environmental, economic, and social criteria, were extended with the technical criterion. To assess which of the incinerators best meets the needs for healthcare waste collection, multi-criteria decision-making was used. For this purpose, a combination of two MCDA methods was applied in this paper, namely full consistency method (FUCOM) and compromise ranking of alternatives from distance to ideal solution (CRADIS). The FUCOM method was applied to determine the weights of the criteria, while the CRADIS method was applied to rank the alternatives. The best alternative of the six alternatives used is A2 (I8-M50), followed by alternative A1 (I8-M40), while the worst ranked alternative is A5 (I8-M100). These results were confirmed by applying the other six methods of multi-criteria analysis and the performed sensitivity analysis. The contribution of this paper is reflected through a new method of multi-criteria analysis that was used to solve decision-making problems. This method has shown simplicity and flexibility in operation and can be used in all problems when it is necessary to make a multi-criteria selection of alternatives.

Application of interval-valued Pythagorean fuzzy VIKOR approach for petroleum sludge treatment technology evaluation and selection

Petroleum sludge is produced during oilfield development and production and can negatively impact the production area and surrounding environment. With increasing attention to the environmental protection of oilfields, finding an energy-efficient, environmentally sound, cost-effective and socially acceptable sludge treatment method is crucial to the sustainable development of oil companies. However, there are several problems in the selection process: ① there is no effective index system for the evaluation of treatment technologies; ② there is data uncertainty and loss of information; ③ experts in the field often make one-sided decisions; and ④ the common decision models fail to balance the general effect and local dominance of a treatment technology. This study is innovative in the following aspects: ① a decision index system of petroleum sludge treatment technology is established; ② the interval-valued Pythagorean fuzzy set effectively managed data uncertainty and loss of information; ③ the redundancy-based expert weighting method is used to avoid one-sided decisions; and ④ using the basic ideas of the VIKOR model to balance the general effect and local dominance of a technology. Example verification proved the effectiveness of this method and a sensitivity analysis showed the results were reliable. Finally, this study compared the results obtained by three other similar methods, and comparative analysis demonstrated that this approach effectively evaluated and selected petroleum sludge treatment technologies. This study improves the rationality of petroleum sludge treatment technology selection and provides a necessary reference for the selection of treatment technology for other petroleum pollutants.

Review of Current Healthcare Waste Management Methods and Their Effect on Global Health

Healthcare is a rapidly growing industry as medical treatments become more sophisticated, more in demand due to increasing incidence of chronic disease and more widely available worldwide. This booming industry is also creating more waste than ever before and, as such, there is a growing need to treat and dispose of this waste. Healthcare waste (HCW) disposal includes a multitude of disposal methods, including incineration, landfilling and chemical treatments. These rudimentary methods and their growing use present their own problems that negatively impact both the environment and, in turn, damage public health, thus contributing to a global healthcare crisis. The aim of this review was to examine the current HCW disposal methods in place and the harmful effects they have on the environment and on public health. The findings accumulated in this review demonstrate a heavy reliance on basic, low tech HCW disposal techniques and uncovered the negative impacts of these methods. There is a notable lack of employment of "greener" HCW disposal methods on a largescale due to cost, access and feasibility. Despite innovations in HCW disposal, there is no scalable, global green solution at present. Further, the review highlights that global health consequences of HCW disposal methods often differ depending on how developed the country is.

Sustainable waste disposal technology selection: The stratified best-worst multi-criteria decision-making method

Waste disposal technology selection is a key problem in the field of municipal solid waste (MSW). This decision may have long-term impacts on environmental development and economic growth. The literature suggests using multi-criteria decision-making (MCDM) methods to address this problem. MCDM techniques commonly require decision makers to assign weightings of importance to the decision criteria based on which, the available technologies are ranked. However, this technology selection problem is concerned with selecting a technology to be used for a relatively long period of time. It is important to take into consideration any uncertainty the decision maker may have with regard to the weightings of the criteria in the future. To take this uncertainty into consideration, this study suggests utilizing a recently developed MCDM technique, namely the stratified MCDM. This technique is designed to help decision makers structure the uncertain future through the consideration of a set of states, which are placed in different strata. The paper shows how the stratified MCDM technique in combination with the best-worst method (labelled stratified BWM) can be employed to compute the ranking of the available technologies. This research is expected to stimulate future applications of the stratified BWM to facilitate long-term decision making.

Sustainability assessment of healthcare waste treatment systems using surrogate weights and PROMETHEE method

This study aims to assess the sustainability of healthcare waste treatment systems using surrogate weights and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE). For this purpose, ten treatment systems, including land disposal, incineration and non-incineration systems, were evaluated in terms of environmental, financial, social, and technical criteria. Firstly, fifteen reputed experts assigned their preferred rankings for the groups of criteria and the sub-criteria. The conversion of these rankings into numerical weights was performed using the SR function, which is an additive combination of Sum and Reciprocal weight functions. Secondly, the alternatives' performance with regards to each criterion allowed PROMETHEE to generate the outranking flows for each alternative. The complete ranking revealed that the rotary kiln (A4) is the most sustainable system followed by steam disinfection (A8), dry heat disinfection and microwave disinfection. However, the municipal landfill is the least sustainable system, while chemical disinfection is ranked in the penultimate position of sustainability. The partial ranking indicated that A4 and A8 are incomparable and both were ranked as most sustainable. Therefore, the sustainability of a system cannot be assessed properly without the exact specification of the system itself. In addition, it is preferable to act on the criteria that affect negatively the system to improve its performance.

Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose

To determine the critical component in an industry is one of the most important tasks performed by maintenance personnel to choose the best maintenance policy. Therefore, the purpose of the current paper is to develop a methodology based on integrated cloud model and extended preference ranking organization method for enrichment evaluation (PROMETHEE) method for finding the most critical component of the framework by ranking the failure causes of the system from multiple decision maker perspective. For this purpose, ranking of failure causes is performed by taking into account five factors namely chances of occurrence of failure (F0), non-detection probability (Nd), downtime duration (Dd), spare part criticality (Spc) and safety risk (Sr). In this paper, first the primary and secondary weight of decision makers are calculated based on the uncertainty degree and divergence degree, respectively, to determine overall weight using cloud model theory by converting the uncertain linguistic evaluation matrix into interval cloud matrix, and then ranking of the steam handling subunit of paper making unit in a paper mill using extended PROMETHEE. The effectiveness of the proposed methodology is explained by considering steam handling subunit of paper making unit to find the critical component.

Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment

With the increasing awareness of global environmental protection, green production has become a significant part for enterprises to remain in a competitive position. For a manufacturing company, selecting the most suitable green supplier plays an important role in enhancing its green production performance. In this paper, we develop a new green supplier evaluation and selection model through the combination of heterogeneous criteria information and an extended multi-attributive border approximation area comparison (MABAC) method. Considering the complexity of decision context, heterogeneous information, including real numbers, interval numbers, trapezoidal fuzzy numbers, and linguistic hesitant fuzzy sets, is utilized to evaluate alternative suppliers with respect to the selected criteria. A maximizing consensus approach is constructed to determine the weight of each decision-maker based on incomplete weighting information. Then, the classical MABAC method is modified for ranking candidate green suppliers under the heterogeneous information environment. Finally, the developed green supplier selection model is applied in a case study from the automobile industry to illustrate its practicability and efficiency.

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.

Medical waste management in three areas of rural China

Objective

The purpose of this paper is to describe current practices of medical waste management, including its generation, investments, collection, storage, segregation, and disposal, and to explore the level of support from upper tiers of the government and health care system for medical waste management in rural China.

Methods

The authors draw on a dataset comprised of 209 randomly selected rural township health centers (THCs) in 21 counties in three provinces of China: Anhui, Shaanxi and Sichuan. Surveys were administered to health center administrators in sample THCs in June 2015.

Results

The results show that the generation rate of medical waste was about 0.18 kg/bed, 0.15 kg/patient, or 0.13 kg/person per day on average. Such per capita levels are significant given China’s large rural population. Although investments of medical waste facilities and personnel in THCs have improved, results show that compliance with national regulations is low. For example, less than half of hazardous medical waste was packed in sealed containers or containers labeled with bio-hazard markings. None of the THCs segregated correctly according to the categories required by formal Chinese regulations. Many THCs reported improper disposal methods of medical waste. Our results also indicate low levels of staff training and low rates of centralized disposal in rural THCs.

Conclusions

Medical waste is a serious environmental issue that is rising on the agenda of policymakers. While a large share of THCs has invested in medical waste facilities and personnel, it appears that actual compliance remains low. Using evidence of low rates of training and centralized disposal, we surmise that a lack of support from upper tiers of management is one contributing factor. Given these findings, we recommend that China’s policymakers should enhance support from upper tiers and improve monitoring as well as incentives in order to improve medical waste management.