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一种混合机器学习-数学编程优化方法,用于大流行期间的城市固体废物管理

Rogelio Ochoa-Barragán¹, Aurora Del Carmen Munguía-López¹, José María Ponce-Ortega¹

¹Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Ciudad Universitaria, 58060 Morelia, Michoacán México.

Environment, development and sustainability

|June 26, 2023

概括

本研究介绍了一种优化和机器学习策略,用于在COVID-19流行期间管理城市固体废物. 它平衡经济利与环境目标,为未来的流行病废物管理提供了洞察力.

科学领域:

环境科学环境科学
运营研究运营研究
计算机科学计算机科学

背景情况:

在全球范围内,COVID-19大流行对城市固体废物管理系统产生了重大影响.
有效的废物管理策略对于流行病期间的公共卫生和环境保护至关重要.

研究的目的:

开发和评估数学优化和机器学习战略,以在COVID-19疫情期间为最佳的城市固体废物管理提供最佳的解决方案.
整合供应链确定优化模型与用于参数估计和未来预测的机器学习.

主要方法:

使用通用代数建模系统 (GAMS) 开发了一个优化模型,以确定最佳的废物管理供应链.
利用Python中的机器学习预测模型来估计时间依赖的参数并生成未来的废物管理预测.
将该策略应用于纽约市的一个案例研究中,使用广泛的社会经济数据来训练机器学习模型.

主要成果:

基于社会经济数据,成功预测了城市固体废物收集的趋势.
确定了经济目标 (利) 和环境目标 (垃圾填埋场垃圾减少) 之间的重大权衡.
证明了该战略在潜在的未来流行病情景中规划和决策的实用性.

结论:

综合优化和机器学习方法为在流行病期间适应性城市固体废物管理提供了强大的框架.
这些发现突显了废物管理中经济因素和环境因素之间的复杂相互作用,为决策者提供了宝贵的见解.
该研究提供了一个数据驱动的工具,用于优化废物管理策略,以应对公共卫生危机.

关键词:

在 COVID-19 疫情中,机器学习机器学习城市固体废物管理优化优化优化优化税收税收税收税收

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