An Overview of Simulation and Optimization Models for Urban Solid Waste Management Systems

Background: Considering the increasing production of waste and the challenges associated with traditional waste management, this research examines the integrated application of artificial intelligence and the Internet of Things in improving the waste management process.The main objective of this study is to provide innovative and efficient solutions for the collection, sorting, and recycling of waste using advanced technologies.Materials and Methods: This study was a descriptive review conducted in 2024, aimed at reporting the search process, documentation, and screening using the PRISMA checklist for systematic reviews.The methodology involved searching for articles from 2000 to 2024 with the English keywords waste management, Internet of Things in waste management, artificial intelligence in waste management, new methods of waste management, and their corresponding Persian equivalents on reputable sites such as PubMed, Scopus, Web of Science, Science Direct, Google Scholar, Magiran, and SID.Results: After searching with relevant keywords, a total of 1021 studies were found, and based on the inclusion and exclusion criteria, 41 articles were utilized.According to the results, the combination of the Internet of Things and artificial intelligence in waste management leads to the optimization of waste collection, prediction of waste volume, and reduction of costs.Machine learning algorithms are also widely used in municipal solid waste management, from waste generation to collection and transportation.Smart sensors and devices are capable of providing real-time data, which can lead to better decision-making and ultimately enhance MSWM, environmental sustainability, and the protection of natural resources.Conclusion: Waste management in contemporary societies faces challenges such as increased waste production and a lack of infrastructure.The utilization of modern technologies such as the Internet of Things and artificial intelligence can significantly aid in improving the waste management process by analyzing data, predicting waste volume, and optimizing collection routes.This approach not only reduces costs but also contributes to environmental protection and enhances urban quality of life.