Landfill site selection using a novel FUCOM-CODAS model: A case study in Libya

Assessment of inorganic solid waste management techniques using full consistency and extended MABAC method

Population and industrial growth have spiked product consumption, which in turn have caused an abrupt rise in municipal solid waste (MSW) production. Due to the lack of resources allocated to waste management, municipal inorganic solid waste (ISW) has increased exponentially, posing a significant strain on the environment and health. To mitigate these issues, sustainable waste management strategies need to be implemented to reduce environmental impacts and improve waste collection and disposal efficiency. The objective of our work was to analyse and identify the most effective techniques for disposing of ISW in India by employing multi-criteria decision-making (MCDM). This technique entails selecting the most suitable alternative based on a variety of competing and interactive criteria. A fusion decision model named the FULL COnsistency Method (FUCOM) and Multi-Attributive Border Approximation area Comparison (MABAC) based on the interval-valued q-rung orthopair fuzzy (IV q-ROF) was developed. Finally, a comparative analysis was performed to demonstrate the system's robustness.

Suitable site selection by using full consistency method (FUCOM): a case study for maize cultivation in northwest Turkey

The agricultural land evaluation procedure is a valuable guide for growing plants where they are best suitable, and it has a critical role in actualizing sustainable plans for providing food security for the growing population. In agricultural land suitability analysis, different multi-criteria decision-making methods are applied. The main objective of this study is to introduce the potential usage of a new multi-criteria decision-making method the Full Consistency Method (FUCOM) in agricultural land suitability analysis. The study was carried out in the northern part of the Karamenderes plain in NW Turkey. Nine land characteristics (soil texture, soil depth, organic matter content, electrical conductivity, pH, slope, drainage, CaCO3%, and cation exchange capacity) were used for the land evaluation study. The weighting values of the land characteristics were calculated by the FUCOM. According to the results, 223 ha (6.26%) were highly suitable, 2650 ha (74.40%) were moderately suitable, 508 ha (14.26%) were marginally suitable, and 181 ha (5.08%) were not suitable for maize cultivation. The weighted values of the parameters were also tested with Analytic Hierarchy Process (AHP) and the Best-Worst Method (BWM). There is a general compatibility between the methodologies. The data obtained from these methods showed that analysis consists of a very positive relationship with each other. The comparisons of these methodologies showed that FUCOM's prioritization order simplicity in parameter weighting and ability to reduce the processing intensity would provide a significant contribution and advantage to the land evaluation experts and planners. It is recommended that the Full Consistent Method could be reliably used in agricultural land suitability analysis.

A novel risk assessment model for work-related musculoskeletal disorders in tea harvesting workers

Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched.

Healthcare performance management using integrated FUCOM-MARCOS approach: The case of India

AIMS

The goal of this research is to propose a simpler and more efficient model for evaluating healthcare establishments (HCEs). With this motivation, this study aims to discover key performance indicators (KPIs) that affect HCE performance, present a ranking model for KPIs in Indian HCEs, and evaluate Indian HCEs using the identified and prioritised KPIs.

MATERIAL AND METHODS

Through extensive literature review and expert opinions, this research identifies the various KPIs in HCEs, classifies them into six main categories, and prioritises them using the full consistency method (FUCOM). Further, well-known HCEs across northern India were evaluated and ranked using Measurement Alternatives and Ranking according to Compromise Solution.

RESULTS

The 'technology adoption related indicators' is found as the most important main KPIs, whereas 'adequate number of hospital beds and bathrooms (IE5)' as the most dominating sub-category KPIs. Also, amongst the 20 evaluated Indian HCEs 'healthcare establishment-1 (HCE1)' was found to be the best performing HCE while 'healthcare establishment-12 (HCE12)' was found to be the worst-performing HCE. The stability and consistency of the results are ascertained by performing sensitivity analysis and comparing the results with other existing methodologies.

CONCLUSION

The findings of this study are quite important for HCEs management to fully comprehend the key areas to improve upon so that managers can improve medical standards in a targeted manner. The developed prioritisation model and methodology shown in this paper will help and motivate managers and intellectuals of HCEs to evaluate and improve the HCE's performance.

A Novel Hybrid Interval Rough SWARA–Interval Rough ARAS Model for Evaluation Strategies of Cleaner Production

Cleaner production is certainly a challenge of our everyday life, and a lot of effort and energy is required to achieve it. This paper has created a model of five strategies for cleaner production in Libyan industry, which have been evaluated on the basis of eight criteria. In order to determine the significance of the criteria, a novel interval rough SWARA (step-wise weight assessment ratio analysis) method has been developed, which takes into account the preferences of decision-makers (DMs) by applying interval rough numbers. A novel interval rough ARAS (additive ratio assessment) method has been developed for the evaluation and selection of the most favorable strategy for cleaner production. The integration of the developed methods has yielded results showing that the first strategy, launching awareness-raising campaigns to publicize these policies, represents the most realistic and best current solution to achieve cleaner production in Libyan industry. A comparative analysis with some existing interval rough methodologies has been presented to verify the superiority of the proposed model. In addition, in a sensitivity analysis, the weight of the most significant criterion has been changed.

A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection

Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers.

Implementation of the circular supply chain management in the pharmaceutical industry

The ever-increasing levels of pollution and waste creation have subjected industries around the world to incorporate the concept of circular economy (CE) in their supply chains. The amalgamation of the CE approach along with supply chain management is called circular supply chain management (CSCM). Among other industries, the pharmaceutical industry is also involved in damaging the ecosystem. Hence, an effective framework for the adoption of CSCM in a particular industry is very essential. Therefore, this paper aims to devise a model that will help the pharmaceutical industries to adopt CSCM in their organizations. For this purpose, the study in the first phase identifies ten barriers that are working as an impediment in the adoption of the CSCM approach. To counter those barriers, the study in the second phase identifies a set of twelve enablers. To analyse the barriers and enablers, the study uses a new hybrid methodology. For allocating weights and prioritizing the barriers, the fuzzy multi-criteria decision-making (MCDM) technique, i.e. fuzzy full consistency method (F-FUCOM) is used, whereas the total quality management tool, i.e. fuzzy quality function deployment (FQFD) is used to rank the enablers. The results from F-FUCOM suggest "lack of financial resources and funding", "market challenges", and "lack of coordination and collaboration among the entire supply chain network" to be the top-most barriers, respectively, whereas the results achieved from the FQFD suggest "industrial symbiosis", "Reverse Logistic (RL) infrastructure", and "block chain technology" to be the top-ranked enablers, respectively. The provision of a facilitating framework for the adoption of CSCM in the pharmaceutical industry and the newly developed hybrid methodology are both novelties of this study.

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

Smartphones have become an inevitable part of every facet of modern society. The selection of a particular smartphone brand from multiple options that are available is a complex and dynamic decision-making problem, involving multiple conflicting criteria that are associated with imprecise asymmetric information imposed by the uncertainty of the consumers. In this paper, we propose a novel hybrid full consistency method (FUCOM) and a combinative distance based assessment (CODAS) based on the multi-criteria group decision-making (MAGDM) framework in the Fermatean fuzzy (FF) domain for smartphone brand selection. We derive the criteria using the UTAUT2 (unified theory of acceptance and ese of technology) model. A group of 15 decision makers (DMs) participated in our study. We compare 14 leading smartphone brands in India and find that the brands having superior features of a good quality and selling a brand image at a affordable price outperform other smartphones. To check the validity of our framework, we compare the results using extant multi-criteria decision-making (MCDM) models. We observe our model provides a consistent solution. Furthermore, we carry out a sensitivity analysis for ascertaining the robustness and stability of the results generated by our model. The results of the sensitivity analysis show that our proposed framework delivers a stable and robust solution.

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.