RESUMO

Waste disposal systems are crucial components of environmental management, and focusing on this sector can contribute to the development of various other sectors and improve social welfare. Urban waste is no longer solely an environmental issue; it now plays a significant role in the economy, energy, and value creation, with waste disposal centers (WDCs) being a key manifestation. The purpose of this study is to measure the performance of WDCs in the state of Nuevo León, Mexico, with the aim of developing environmental, social, and governance (ESG) strategies to strengthen and prepare the WDCs for the industrial developments in this state. By identifying environmental variables and undesirable factors, the efficiency and managerial capacity of 32 WDCs were assessed. The analysis revealed that 9 out of the 32 WDCs are technically efficient, while the remaining 23 require significant improvements. Using the Data Envelopment Analysis (DEA) technique, an average efficiency score of 0.91 was found, with a standard deviation of 0.08. The managerial capacity analysis indicated that the highest-ranked WDC achieved an efficiency score of 1, whereas the lowest-ranked WDC scored 0.67. Finally, an operational map of development strategies was developed using the Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) approach. The results indicate that four phases of development should be followed for real development and maturity of development in these WDCs, including Groundwork, Structuring, Development and Growth, and Smart Maturity.

Assuntos

Gerenciamento de Resíduos , México , Gerenciamento de Resíduos/métodos , Instalações de Eliminação de Resíduos , Eliminação de Resíduos/métodos , Cidades , Meio Ambiente , Conservação dos Recursos Naturais/métodos

RESUMO

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.

RESUMO

Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.

RESUMO

The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al2O3-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the thermophysical properties of the membrane. 5%-10% of Al2O3-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of Al2O3-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio. To determine the impact of Al2O3-NPs loading on the Response Surface Method (RSM), DOE, ANOVA, ANN, MLP, and NSGA-II, the number of aluminum oxide nanoparticles (Al2O3-NPs), temperature, and water/ethylene glycol (EG) on membranes in PC ultrafiltration are evaluated. Based on the Relative Thermal Conductivity Model (RSM), the regression coefficient of Al2O3 in water and EG was 0.9244 and 0.9170 with adjusted regression coefficients. A higher concentration of EG enhances the thermal conductivity of the membrane when the effective parameters are considered. The effect of temperature on the relative viscosity of the membrane led to the conclusion that Al2O3 water/EG can cool at high temperatures while providing no viscosity change. When Al2O3 is dissolved in water and EG, more EG is necessary to optimize the mode of reactivity. Using the MLP model, the calculated R-value is 0.9468, the MSE is 0.001752989 (mean square error), and the MAE is 0.01768558 (mean absolute error). RSM predicted the average thermal conductivity behavior of nanofluid better. The ANN model, however, has proven to be more effective than the RSM in simulating the relative viscosity of nanofluids. The NSGA-II optimized results showed that the minimum relative viscosity and maximum coefficient of thermal conductivity occurred at the lowest water ratio and maximum temperature.

Assuntos

Nanopartículas , Água , Temperatura , Ultrafiltração , Óxido de Alumínio , Etilenoglicóis

RESUMO

The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters' optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms' applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.

RESUMO

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient's condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.