Green Algorithms: Leveraging Decision Sciences and Machine Learning for Optimized Waste Management in Urban Supply Chains
DOI:
https://doi.org/10.56830/IJGMBS06202503Keywords:
Green Algorithms, Sustainable Waste Management, , Urban Supply Chains, Decision Sciences, Machine Learning, Smart Waste Management Systems (SWMS)Abstract
This research examines the application of decision science techniques and machine learning for algorithmic solutions to urban solid waste management (SWM systems). The rapid rate of urbanization paired with heightened environmental scrutiny necessitates improved waste management operations in modern cities. This paper proposes a hybrid approach using green algorithms for optimization within bin packing, route scheduling, and vehicle rental allocation complexities in waste disposal operations. Incorporation of spatial two-dimensional optimization, two-valued focus signatures, and knapsack problem constraints enhance logistics efficiency relative to operational cost and emission reduction. A C-MACRO prototype was designed for ultra-wide-distance routing which permits dynamic behavioral and operational feedback customization from designers of the waste management system. The study applies GIS-enhanced simulations for spatial geography analysis, systemic bottleneck identification, context-relevant policy formulation, as well as interventional suggestions and evaluation of polices. Operators gain higher revenues while achieving better resource efficiency alongside smarter integration of waste systems into sustainable urban supply chains. This novel eco-algorithmic model integrates environmental policies with operational logistics on frameworks of data-driven urban planning reflects the increasing necessity towards cross-disciplinary innovation enabling sustainable urban growth.
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