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

An extension of the best-worst method based on the spherical fuzzy sets for multi-criteria decision-making

The ambiguous information in multi-criteria decision-making (MCDM) and the vagueness of decision-makers for qualitative judgments necessitate accurate tools to overcome uncertainties and generate reliable solutions. As one of the latest and most powerful MCDM methods for obtaining criteria weight, the best-worst method (BWM) has been developed. Compared to other MCDM methods, such as the analytic hierarchy process, the BWM requires fewer pairwise comparisons and produces more consistent results. Consequently, the main objective of this study is to develop an extension of BWM using spherical fuzzy sets (SFS) to address MCDM problems under uncertain conditions. Hesitancy, non-membership, and membership degrees are three-dimensional functions included in the SFS. The presence of three defined degrees allows decision-makers to express their judgments more accurately. An optimization model based on nonlinear constraints is used to determine optimal spherical fuzzy weight coefficients (SF-BWM). Additionally, a consistency ratio is proposed for the SF-BWM to assess the reliability of the proposed method in comparison to other versions of BWM. SF-BWM is examined using two numerical decision-making problems. The results show that the proposed method based on the SF-BWM provided the criteria weights with the same priority as the BWM and fuzzy BWM. However, there are differences in the criteria weight values based on the SF-BWM that indicate the accuracy and reliability of the obtained results. The main advantage of using SF-BWM is providing a better consistency ratio. Based on the comparative analysis, the consistency ratio obtained for SF-BWM is threefold better than the BWM and fuzzy BWM methods, which leads to more accurate results than BWM and fuzzy BWM.

Optimal selection of healthcare waste treatment devices using fuzzy-rough approach

The escalating volume of healthcare waste (HCW) generated by healthcare facilities poses a pressing challenge for all nations. Adequate management and disposal of this waste are imperative to mitigate its adverse impact on human lives, wildlife, and the environment. Addressing this issue in Bosnia and Herzegovina involves the establishment of a regional center dedicated to HCW management. In practice, there are various treatments available for HCW management. Therefore, it is necessary to determine the priority for procuring different treatments during the formation of this center. To assess these treatment devices, expert decision-making employed the fuzzy-rough approach. By leveraging extended sustainability criteria, experts initially evaluated the significance of these criteria and subsequently assessed the devices for HCW treatment. Employing the fuzzy-rough LMAW (Logarithm Methodology of Additive Weights), the study determined the importance of criteria, highlighting "Air emissions" and "Annual usage costs" as the most critical factors. Utilizing the fuzzy-rough CoCoSo (the Combined Compromise Solution) method, six devices employing incineration or sterilization for HCW treatment were ranked. The findings indicated that the "Rotary kiln" and "Steam disinfection" emerged as the most favorable devices for HCW treatment based on this research. This conclusion was validated through comparative and sensitivity analyses. This research contributes by proposing a solution to address Bosnia and Herzegovina's HCW challenge through the establishment of a regional center dedicated to HCW management.

Geographic information system-based multi-criteria decision-making analysis for investment assessment of wind-photovoltaic-shared energy storage power stations: a case study of Shanxi Province

As the center of the development of power industry, wind-photovoltaic (PV)-shared energy storage project is the key tool for achieving energy transformation. This research seeks to construct a feasible model for investment appraisal of wind-PV-shared energy storage power stations by combining geographic information system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a comprehensive criteria system is established from the perspectives of orography, economy, resources, climate, and society, and the evaluation data is described using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight deviation produced by the single weighting approach, a comprehensive weighting model including the best-worst method (BWM) and entropy weight method is provided to calculate the weights of criteria. Next, expert weights are calculated based on trust analysis. Finally, alternatives are ranked by the improved gained and lost dominance score (GLDS) method. To verify the validity of the model, an empirical investigation is carried out in Shanxi Province. The results show that the economy is the primary factor influencing the investment decision. Among all the projects approved by the government, alternative F4 located in Yanzhuang Town, Yuanping City is the best investment object. Furthermore, to illustrate the stability of the result, triple sensitivity analysis and comparative analysis are conducted in Shanxi Province. This study expands the application scope of GIS and MCDM method by first providing support for government and investors to identify optimal investment targets.

Hospital food management: a multi-objective approach to reduce waste and costs

Food waste contributes significantly to greenhouse emissions and represents a substantial portion of overall waste within hospital facilities. Furthermore, uneaten food leads to a diminished nutritional intake for patients, that typically are vulnerable and ill. Therefore, this study developed mathematical models for constructing patient meals in a 1000-bed hospital located in Florida. The objective is to minimize food waste and meal-building costs while ensuring that the prepared meals meet the required nutrients and caloric content for patients. To accomplish these objectives, four mixed-integer programming models were employed, incorporating binary and continuous variables. The first model establishes a baseline for how the system currently works. This model generates the meals without minimizing waste or cost. The second model minimizes food waste, reducing waste up to 22.53 % compared to the baseline. The third model focuses on minimizing meal-building costs and achieves a substantial reduction of 37 %. Finally, a multi-objective optimization model was employed to simultaneously reduce both food waste and cost, resulting in reductions of 19.70 % in food waste and 32.66 % in meal-building costs. The results demonstrate the effectiveness of multi-objective optimization in reducing waste and costs within large-scale food service operations.

Evaluating sustainable municipal solid waste management scenarios: A multicriteria decision making approach

Due to increasing urbanization and population growth, municipal solid waste management (MSWM) is a significant environmental concern in developing countries. Inadequate waste management systems lead to environmental pollution, health hazards, and economic losses. While considering the challenges and limitations, policymakers and authorities need to opt for such waste management scenarios that are environmentally friendly and resolve energy issues. Ten MSWM scenarios were developed and evaluated using seven different criteria. Four multi-criteria decision-making (MCDM) techniques, namely fuzzy logic, AHP, TOPSIS, and PROMETHEE II, were employed to rank the scenarios and identify the most appropriate option for solid waste management in Lahore. This study highlights that the optimal waste management approach comprises a composition of 54% anaerobic digestion, 37% gasification, and 9% landfill technologies. These percentages collectively represent the most suitable and effective strategies for the city's waste management needs. All the MCDM techniques consistently produce similar results. These scenarios have broader applicability across cities in Central Asia and beyond. The study's findings are aligned to promote sustainable and environmentally friendly MSWM practices. These findings endorse implementing strategies and measures aimed at fostering environmental sustainability and the responsible handling of waste, serving as a valuable reference for various regions.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

Municipal solid waste management: Identification and analysis of technology selection criteria using Fuzzy Delphi and Fuzzy DEMATEL technique

Municipal solid waste management (MSWM) poses a considerable challenge to developing countries like Bangladesh because of the rising waste generation rates and lack of effective management practices such as illegal open dumping and informal waste collection. One of the crucial factors in the successful management of MSW is to select the appropriate technology which is a complex multi-criteria and laborious process. Despite the global emphasis on the importance of MSWM in the literature, there is a lack of studies conducted in developing countries that effectively identify and analyze the critical performance criteria for appropriate technology selection. This research aims to address this shortcoming by identifying, and prioritizing the selection criteria and finally investigating the inter-relationship between them and the degree to which they affect or are affected by one another. First, a thorough literature review and expert consultation were employed to determine a set of 21 key criteria using the Fuzzy Delphi method (FDM). Later, taking into account the imprecise and subjective nature of the DEMATEL method on human judgements, the Fuzzy DEMATEL technique was employed to investigate the cause-effect relationships among the identified criteria. The findings of the study demonstrated that 14 criteria were categorized as causal elements that have the most significant influence on the MSWM technology selection process and 7 criteria were categorized as effect. The selection of MSWM technology demands greater consideration of the top three ranked criteria, namely T4- Access to Technology (AT), T8- Feasibility (F), and the Ec6-Infrastructure requirements (IR). By identifying the pertinent criteria, structures and interrelationships, the outcome of the study can facilitate a better understanding of causal relationships among the criteria that require specific consideration from the decision-makers and allow them to select appropriate MSW management technology.

Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model's applicability, a case study was conducted for the Erzurum region, one of Turkey's landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study's robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.

Integrated evaluation of the performance of phosphogypsum recycling technologies in China

The Chinese government is implementing policies, such as the "Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period", to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings.

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method

Municipal solid waste (MSW) management is vital in achieving sustainable development goals. It is a complex activity embracing collection, transport, recycling, and disposal; and whose management depends on proper strategic decision-making. The use of decision support methods such as multi-criteria decision-making (MCDM) is widespread in MSW management. However, their application mainly focuses on selecting plant locations and the best technologies for waste treatment. Despite the critical role played by transport in promoting sustainability, MCDM has seldom been applied for the selection of sustainable transport alternatives in the field of MSW management. There are a few MCDM studies about choosing waste collection vehicles, but none that include the most recent green vehicles among the options or consider feasible future scenarios. In this article, different engine technologies for collection trucks (diesel, compressed natural gas (CNG), hybrid CNG-electric, electric, and hydrogen) are evaluated under sustainability criteria in a Spanish city by applying the stratified best and worst method (SBWM). This method enables considering the uncertainty associated with future events to establish various feasible scenarios. The results show that the best-valued options are electric and diesel trucks, in that order, followed by CNG and hybrid CNG-electric, and with hydrogen-powered trucks coming last. The SBWM has proven helpful in defining a comprehensive framework for selecting the most suitable engine technology to support long-term MSW collection. Considering sustainability among the criteria and feasible future scenarios in waste management collection decision-making provides more comprehensive and conclusive results that help managers and policymakers make better informed and more reliable decisions.

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

Entropy and discrimination measures based q-rung orthopair fuzzy MULTIMOORA framework for selecting solid waste disposal method

Fastest growing population, rapid urbanization, and growth in the disciplines of science and technology cause continually development in the amount and diversity of solid waste. In modern world, evaluation of an appropriate solid waste disposal method (SWDM) can be referred as multi-criteria decision-making (MCDM) problem due to involvement of several conflicting quantitative and qualitative sustainability indicators. The imprecision and ambiguity are usually arisen in SWDM assessment problem, and the q-rung orthopair fuzzy set (q-ROFS) has been recognized as one of the adaptable and valuable ways to tackle the complex uncertain information arisen in realistic problems. In the context of q-ROFSs, entropy is a significant measure for depicting fuzziness and uncertain information of q-ROFS and the discrimination measure is generally used to quantify the distance between two q-ROFSs by evaluating the amount of their discrimination. Thus, the aim of this study is to propose a novel integrated framework based on multi-attribute multi-objective optimization with the ratio analysis (MULTIMOORA) method with q-rung orthopair fuzzy information (q-ROFI). In this approach, an integrated weighting process is presented by combining objective and subjective weights of criteria with q-ROFI. Inspired by the q-rung orthopair fuzzy entropy and discrimination measure, objective weights of criteria are estimated by entropy and discrimination measure-based model. Whereas, the subjective weights are derived based on aggregation operator and the score function under q-ROFS environment. In this respect, novel entropy and discrimination measure are proposed for q-ROFSs. Furthermore, to display the feasibility and usefulness of the introduced approach, a case study related to SWD method selection is presented under q-ROFS perspective. Finally, comparison and sensitivity investigation are presented to confirm the robustness and solidity of the introduced approach.

Waste management and green technology: future trends in circular economy leading towards environmental sustainability

The effective treatment of waste to be used as a resource in future has a major role in achieving environmental sustainability and moving towards circular economy. The current research is aimed to provide in-depth detail regarding prominent trends and research themes, evolution, future research orientation, main characteristics, and mapping of research publications on waste management, technological innovation in circular economy domain from the year 2000 to 2021. Different analyses including text mining and bibliometric and content analyses were applied to answer the research question and provide the details on aforementioned variables. From the bibliometric analyses, a total of 1118 articles were drawn out from the Scopus database to conceptualize the core body of research. As a result, the following themes were identified: electronic waste, circular economy transition, plastic waste, bio-based waste management, lifecycle assessment, and ecological impacts, and construction and demolition waste management. The highlighted features, future research orientation, and prominent research perspective can provide guideline for future research to enrich the literature through conducting studies on provided research directions and help lead waste management and technological innovation policymakers, professionals, and practitioners in moving towards circular transition.

Analyzing failures in adoption of smart technologies for medical waste management systems: a type-2 neutrosophic-based approach

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.

Evidence-Based User Interface Sanitation Technology Selection for Urban Slums: A Multi-Criteria Analysis; The Case of Jimma Town, Ethiopia

Slums are urban areas with insufficient public services and access to sanitation. Evidence-based selection of sustainable sanitation options is critical for addressing the sanitation crisis in slums. This mixed methods study was conducted in Jimma Town, southwest Ethiopia, to assess sanitation status and prioritize sustainable sanitation options for slums. The study was done in 2 phases: quantitative and qualitative. The quantitative cross-sectional household survey aimed to assess sanitation status and the qualitative exploratory method to explore alternative sanitation options and prioritize sustainable alternatives. A total of 310 households were chosen using systematic random sampling methods, of which 302 participated. Data was gathered through interviews, which were supplemented with questionnaires and observation checklists, and 2 focus group discussions (FGD) were held. First, FGD was with expertise in the sanitation sector, and the second was with community members. The state of sanitation was summarized, and multi-criteria analysis (MCA) was used to prioritize sustainable sanitation options. According to our findings, 68% of households had access to improved facilities, and 22.5% didn't have any form of toilet facility. About 7 off-site and on-site user interface sanitation technology options were considered in the selection of alternative sanitation technologies, and each option was evaluated against 17 health, economic, social, technical, and environmental criteria. In the final analysis, the options with the highest scores for the setting were flush to septic tanks, compost toilets, and biogas toilets. Mobilizing such a promising sanitation option is recommended for future interventions.

Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields.

Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10479-022-04822-0.

Multi-Criteria Selection of Waste-to-Energy Technologies for Slum/Informal Settlements Using the PROMETHEE Technique: A Case Study of the Greater Karu Urban Area in Nigeria

Slum/informal settlements are an integral part of a city, with a population projected to reach 3 billion by 2030. It is also expected that the rate of waste generation will more than triple by 2050 in the cities of low-income countries of sub-Saharan Africa. At this rate, the risk to the environment and health of inhabitants are enormous, because the current waste management practices are not guided by legislation on proper use and disposal. This paper proposes the conversion of waste to energy as a solution to this problem. The aim of this study is to apply the PROMETHEE technique with a combination weighting method to obtain the most appropriate waste-to-energy technology for the slum/informal settlements of the Greater Karu Urban area in Nigeria. The findings reveal that the gasification technology outperformed the other technologies, and the affordability of electricity supply from this technology was determined by a general survey conducted on the slum/informal settlements.

A facilitating framework for a developing country to adopt smart waste management in the context of circular economy

To achieve higher standards of sustainability, the waste management sector now requires the incorporation of circular economy (CE) principles. However, an easy transition toward the particular goal would require the use of smart waste technologies. To achieve the aforementioned goal, this study aims to provide a facilitating framework for the adoption of smart waste management in the context of CE for Pakistan. To help Pakistan transition toward the new paradigm, a total of 16 critical facilitators are evaluated based on five distinctive criteria using a novel fuzzy hybrid multi-criteria decision-making (MCDM) approach. The hybrid MCDM approach includes fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) for allocating weights to the determined criteria; whereas, the fuzzy VIšekriterijumsko kompromisno rangiranje (VIKOR) approach is used to rank the critical facilitators adopted from the secondary literature. The fuzzy approach in both cases is to deal with any kind of uncertainty during the data collection process. Based on the achieved results, the study suggests that before the application of smart waste technologies in the country, Pakistan should first focus on devising regulations that effectively address the mismanagement of waste produced in the country. Also, the industries in the country need to become more responsible and should adopt environmental management systems that foster waste minimization. Lastly, the country in the third phase should focus on the wide application of digitalization both in the streams of ICT and IoT, for collecting, sharing, and receiving waste data. The study further provides policy recommendations to the respective stakeholders that will help the country achieve zero-waste CE.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. "Non-motorized travel" is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts' preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.

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.

Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach

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.

Proposed Methodology for End-of-Life Option using Multi Criteria Decision Analysis: A Study for General Paper Product

Over the years, the world population has been growing exponentially. This population growth affects the number of waste products due to the increased production, which leads to greater environmental impact and other problems. There are different numbers of product end-of-life (EOL) options to handle waste based on product characteristics. This research is designed to develop a methodology to determine the best EOL option for a paper product using the analytical hierarchy process (AHP). AHP is one of the multi-criteria decision analysis (MCDA) methods employed to select the best option by considering the user’s preferences and output of competing EOL options related to different product criteria. A graphical user interface (GUI) called AHP-based software was developed using Microsoft Excel through the programming function of Visual Basic for Applications as a user facilitating tool when conducting the analysis. The case study technique is applied to five different types of paper products to assess the capability of the proposed AHP-based software. Results from the AHP-based software reveal that recycling is the most suitable EOL technique for most paper products compared to other techniques. However, polluted products with ink or food waste and coating may not be suitable for this method. The research assists the users to identify the most sustainable ways to handle paper product waste based on the product condition.

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.

Determining the critical factors of air-conditioning innovation using an integrated model of fuzzy Kano-QFD during the COVID-19 pandemic: The perspective of air purification

At present, people are demanding better indoor air quality during the COVID-19 pandemic. In addition to maintaining the basic functions, new air-conditioning should also add air purification functions to improve indoor air quality and reduce the possibility of virus transmission. Nowadays, there is lack of research results on the innovation of air-conditioning. The aim of this study is to present a two-stage mathematical model for identifying critical manufacturing factors in the innovation process of air conditioning. In this paper, Kano and quality function deployment (QFD) are used to analyze the critical factors affecting air-conditioning innovation. Some studies have proposed using Kano-QFD model to analyze product innovation, but the study only studies one stage, which loses the analysis of the subsequent stages of product innovation. Based on this, this paper studies the priority method of two-stage critical factors for air-conditioning innovation. Firstly, the questionnaire survey and fuzzy sets are used to collect demand information of multi-agent (customers and professional technicians). Secondly, the Kano model is used to classify and calculate satisfaction of multi-agent. Then, QFD is used to transform multi-agent demands into engineering property indexes (first stage) and technical property indexes (second stage) and calculate the weight of each index. Finally, the applicability and superiority of this method is illustrated by taking the central air-conditioning as an example.

Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective

Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then compared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.