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Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that "Pendik" is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other critical multi-dimensional problems related to COVID-19 and support sustainability and circular economy. [ABSTRACT FROM AUTHOR]

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

Medical waste management (MWM) systems are considered among the most important urban systems nowadays. Cities in different countries prefer to transform their infrastructure based on sustainability guidelines and practices. Meanwhile, smart technologies such as Internet of Things (IoT) and blockchain are being recently used in different urban systems of cities that aim to transform into smart cities. MWM systems are one of the main targets of integrating such smart technologies to maximize economic and social profits and minimize environmental issues. However, the transformation of traditional MWM systems into smart MWM systems and the adoption of such technologies can be a very resource-consuming task. One of the possible tasks in this process can be the identification of factors that cause failure in the adoption of smart technologies. Therefore, this study proposes a multi-criteria evaluation model based on type-2 neutrosophic numbers (T2NNs) to identify factors contributing to failure in the adoption of IoT and blockchain in smart MWM systems in Istanbul, Turkey. Results of the case study indicate that training for different stakeholders, market acceptance, transparency, and professional personnel are the main factors that lead to failure in the adoption of smart technologies. Training for different stakeholders, market acceptance, transparency, and professional personnel factors obtained distance values of 0.494, 0.381, 0.375, and 0.278, respectively, against the best factor which is security and privacy. In order to validate the results of the proposed approach, a sensitivity analysis test is performed. Results of this study can be useful for governmental and private MWM and green companies that are planning to adopt IoT and blockchain within their waste management (WM) system. [ABSTRACT FROM AUTHOR]

Developing sustainable municipal waste management systems requires an in-depth analysis and synthesis of economic, environmental, and social sustainable development indicators. However, despite its profound impact on organizational performance, social sustainability has received little attention in previous studies compared to economic and environmental sustainability. Although a few studies have been conducted to analyze and measure the impact of social sustainability indicators, most of these endeavors fail to consider many indicators that must be evaluated under uncertain and incomplete information. This study proposes a new decision model that implements Interval-Valued Neutrosophic Sets (IVNS) within a multi-distance measure defined with respect to an ideal reference solution. IVNS allows decision-makers to reliably express their opinions using truth, indeterminacy, and falsity membership functions. A linguistic framework is developed to categorize indicators based on their performance and suggest potential solutions when detecting indicators with relatively weak performances. Social sustainability failures in the municipal waste management system of Istanbul are investigated to show the applicability and efficacy of the proposed approach. We identify salary satisfaction and health insurance as the most significant social indicators determining the success of the system, while freedom of association and citizen participation are categorized as the worst-performing ones. The stability of the proposed methodology is illustrated by performing a comparative analysis with Single-Valued Neutrosophic Sets and Interval-Valued Fuzzy Pythagorean Sets. [Display omitted] • Municipal waste management systems build on economic, environmental, and social pillars. • Social sustainability (SS) indicators often rely on uncertain and incomplete information. • We define a model based on interval-valued neutrosophic sets and multi-distance measures. • A linguistic framework is developed to categorize indicators based on their performance. • We analyze the main SS failures in the municipal waste management system of Istanbul. [ABSTRACT FROM AUTHOR]

• Recycling facility location selection for urban healthcare plastic waste is addressed. • Extensions of CoCoSo and WASPAS methods are presented based on constrained attributes. • Impacts of potential uncertain future events and scenarios are included using stratified BWM model. • A novel hybrid model is developed based on Stratified BWM, CoCoSo, and WASPAS with constrained attributes. • A real-life case study of Istanbul is investigated to show applicability of the proposed model. For efficient management of plastic waste in urban healthcare environment, the development of in-depth solutions considering all possible effective factors is required. Recycling plastic waste of healthcare centers has become an essential process for urban regions considering environmental, economic, and social issues. In this regard, recycling facility location selection is a multi-dimensional decision-making problem that can be addressed with the support of MCDM. Although traditional MCDM techniques have usually focused on benefit and cost attributes, in real-life practices, decision-makers in environmental sectors have shown a high tendency to consider specific constraints on attributes according to real-life standards. To address the recycling location selection problem with a practical solution, this study proposed a novel hybrid constrained decision-making model that integrates the hierarchical stratified best-worst method (H-SBWM) with the constrained combined compromise solution (CoCoSo) and the constrained weighted aggregated sum product assessment (WASPAS) methods, called H-SBWM-C-CoCoPAS. To examine the proposed approach, a case study is investigated for six location candidates in Istanbul under technical and sustainability aspects. The research findings show that the most important criterion for recycling facility location selection is carbon footprint with the weight coefficient of 0.1757. Also, the Pendik district is the best location for opening a recycling center in the Istanbul. [ABSTRACT FROM AUTHOR]

• A novel MCDM approach is proposed to select the optimal BSS for electric scooters. • TRUST approach is based on a multi-normalization and multi-distance framework. • A four-dimensional BSS location selection framework is established. • Sensitivity analysis is conducted to show validity of the proposed approach. • Comparative analysis is conducted to show advantages of the proposed approach. Due to high popularity of electric scooters, cities with high population have faced challenges regarding establishment of battery swapping stations (BSS) along populated areas of the urban districts. However, locating a BSS in a big city is a multi-dimensional problem which require reliable tools to efficiently address. This paper proposes a novel robust decision-making tool, named mulTi-noRmalization mUlti-distance aSsessmenT (TRUST), to tackle location selection problem for BSS considering sustainability criteria. The proposed approach applies a multi-normalization procedure using three linear normalization techniques, logarithmic-normalization, and constraint-based normalization which are integrated through an aggregation operator. Then, Euclidean, Manhattan, Lorentzian, and Pearson distance measures are used to determine distance of alternatives from the negative ideal solution in order to calculate the final score. Advantages of the proposed approach are consideration of a multi-normalization algorithm to minimize subjectivity in normalized data, consideration of constraint-based normalization technique to ensure specific standards, and utilization of four distance measures through a two-stage process to determine a relative distance score. To show the feasibility and applicability of the new approach, a real-life case study is investigated to locate a BSS in Istanbul. Results show that the best alternative is Beyoğlu for locating a BSS for electric scooters. [ABSTRACT FROM AUTHOR]