Intuitionistic fuzzy multi-criteria decision making framework based on life cycle environmental, economic and social impacts: The case of U.S. wind energy

2016 ◽  
Vol 8 ◽  
pp. 78-92 ◽  
Author(s):  
Serkan Gumus ◽  
Murat Kucukvar ◽  
Omer Tatari
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Environmental Economic ◽  
Wind Energy ◽  
Social Impacts ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Decision Making Framework ◽  
Economic And Social Impacts

Methodological analysis for decision-making regarding solid waste management in megacities

2020 ◽  
Author(s):  
Johanna Solano ◽  
David Orjuela Yepes ◽  
Javier Rodrigo-Ilarri
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Environmental Economic ◽  
Waste Management ◽  
Solid Waste ◽  
Life Cycle Analysis ◽  
Solid Waste Management ◽  
Social Impacts ◽  
Toma De Decisiones ◽  
Economic And Social Impacts

<p>Pronosticar la generación de residuos sólidos se ha convertido en un tema fundamental para dimensionar los elementos técnicos (generación, recolección, transporte, transferencia, uso y disposición final) y políticos (legislación, grupos de interés, sostenibilidad financiera) con respecto a la gestión integral de residuos sólidos en megaciudades. Para poder hacer este tipo de predicciones, es necesario diseñar modelos matemáticos que permitan el análisis de cada variable asociada con esta gestión, teniendo en cuenta las particularidades y necesidades locales de gestión de residuos.</p><p>Se pueden incluir varios modelos en cada etapa de la gestión integral de residuos sólidos urbanos. Actualmente, existen modelos que utilizan inteligencia artificial para pronosticar la generación de residuos sólidos urbanos, diseñar rutas de recolección y seleccionar el tipo de disposición final. Sin embargo, es necesario integrar estos modelos que respondan al contexto de cada población. Para lograr esto, es necesario conocer las características de cada ciudad, así como las diferentes variables implícitas dentro del proceso para desarrollar metodologías concretas, que se convierten en herramientas útiles para las administraciones municipales. Sin embargo, las metodologías existentes no incluyen un análisis de los impactos asociados con cada etapa del proceso de gestión de residuos, como criterio para seleccionar las mejores estrategias de gestión. </p><p>Therefore, this methodological proposal includes a stage to evaluate the possible impacts caused by the selected alternative, for which a life cycle analysis is proposed as a tool to determine possible environmental, economic and social impacts. This analysis will be carried out by gathering the corresponding information, as well as using specific software to obtain the data that feeds the model for subsequent decision-making.</p><p>Esta propuesta introduce diferentes tipos de modelos en cada etapa del proceso para obtener resultados integrales y más precisos con respecto a las necesidades de una megaciudad. La propuesta se basa en variables y datos reales de acuerdo con las particularidades de las ciudades, para minimizar los posibles errores en la toma de decisiones. Al introducir herramientas cuantitativas para analizar la gestión de residuos sólidos urbanos, la metodología propuesta omite posibles evaluaciones cualitativas o basadas en la percepción, lo que lleva a que los resultados obtenidos sean cada vez más realistas, ya que tienen en cuenta las necesidades reales de cada población.</p>

scholarly journals Fire Risk Assessment in Healthcare Settings: Application of FMEA Combined with Multi‐Criteria Decision Making Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fateme Omidvari ◽  
Mehdi Jahangiri ◽  
Reza Mehryar ◽  
Moslem Alimohammadlou ◽  
Mojtaba Kamalinia
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Fire Risk ◽  
Fire Detection ◽  
Healthcare Setting ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Healthcare Settings ◽  
Fire Risk Assessment ◽  
Risk Parameters

Fire is one of the most dangerous phenomena causing major casualties and financial losses in hospitals and healthcare settings. In order to prevent and control the fire sources, first risk assessment should be conducted. Failure Mode and Effect Analysis (FMEA) is one of the techniques widely used for risk assessment. However, Risk Priority Number (RPN) in this technique does not take into account the weight of the risk parameters. In addition, indirect relationships between risk parameters and expert opinions are not considered in decision making in this method. The aim is to conduct fire risk assessment of healthcare setting using the application of FMEA combined with Multi‐Criteria Decision Making (MCDM) methods. First, a review of previous studies on fire risk assessment was conducted and existing rules were identified. Then, the factors influencing fire risk were classified according to FMEA criteria. In the next step, weights of fire risk criteria and subcriteria were determined using Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) and different wards of the hospital were ranked using Interval-Valued Intuitionistic Fuzzy Combinative Distance-based Assessment (IVIFCODAS) method. Finally, a case study was performed in one of the hospitals of Shiraz University of Medical Sciences. In this study, fire alarm system (0.4995), electrical equipment and installations (0.277), and flammable materials (0.1065) had the highest weight, respectively. The hospital powerhouse also had the highest fire risk, due to the lack of fire extinguishers, alarms and fire detection, facilities located in the basement floor, boilers and explosive sensitivity, insufficient access, and housekeeping. The use of MCDM methods in combination with the FMEA method assesses the risk of fire in hospitals and health centers with great accuracy.

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