TrS-RAM:Leveraging Novel MCDM Techniques for Evaluating Sustainability of Fuel Cell Vehicles Based on Tree Soft Technique

Abstract

A smart city is an urban area that leverages technology, data, and innovation to improve the quality of life for its residents, enhance sustainability, and optimize urban services and infrastructure. Smart cities use a variety of digital technologies and solutions to address urban challenges and create more efficient, resilient, and livable communities. Smart cities employ innovative waste management solutions, such as IoT-enabled waste bins, route optimization algorithms, and recycling initiatives, to minimize waste generation, improve collection efficiency, and promote recycling and composting. A fuel cell vehicle (FCV) is a type of electric vehicle (EV) that uses a fuel cell to generate electricity on board, which powers an electric motor to propel the vehicle. FCVs generate electricity through an electrochemical reaction between hydrogen and oxygen. Integrating fuel cell vehicles (FCVs) into waste management systems can offer several benefits, particularly in terms of enhancing environmental sustainability and operational efficiency. FCVs can be used for collecting and transporting waste from various collection points to treatment facilities or disposal sites. Their long driving ranges and rapid refueling capabilities make them suitable for covering large distances efficiently. Overall, integrating FCVs into waste management systems can contribute to achieving environmental sustainability goals, reducing emissions, and improving the efficiency and effectiveness of waste collection and transportation operations. The Root Assessment Method (RAM) is a systematic approach used to analyze and evaluate problems or issues by identifying and addressing their root causes. The method is particularly useful in problem-solving scenarios where understanding the underlying causes is essential for developing effective solutions. This paper proposes the RAM method under Tree-soft set approach using Entropy weight method.