A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic

Rogelio Ochoa-Barragán, Aurora del Carmen Munguía-López () and José María Ponce-Ortega
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Rogelio Ochoa-Barragán: Universidad Michoacana de San Nicolás de Hidalgo
Aurora del Carmen Munguía-López: Universidad Michoacana de San Nicolás de Hidalgo
José María Ponce-Ortega: Universidad Michoacana de San Nicolás de Hidalgo

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 7, No 51, 17653-17672

Abstract: Abstract This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years.

Keywords: Optimization; Machine learning; Taxation; COVID-19; Municipal solid waste management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03354-2

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