International Journal of Green Management and Business Studies
Vol.5 No.1 June 2025
DOI https://www.doi.org/10.56830/IJGMBS06202503
Authors
Ana Brasão
Mohamed A. E. Khalefa
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.
Keywords: Green Algorithms, Sustainable Waste Management, Urban Supply Chains,
Decision Sciences, Machine Learning, Smart Waste Management Systems
(SWMS)
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