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

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 mode

SUBMITTER: Ochoa-Barragan R 

PROVIDER: S-EPMC10181925 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature