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Abstract
The following work addresses the optimization of solid waste collection systems, a crucial aspect of waste management. Inefficient collection processes lead to increased operational costs and reduced profitability. The focus of this work is on developing a replicable, data-driven methodology that adapts to the specific requirements of different locations, waste types, and collection needs. Given the non-standard nature of waste collection, which must vary according to local conditions, the objective is to design solutions that are both efficient and intuitive, improving the overall waste management process. The results demonstrate that the adoption of optimized waste collection approach can lead to significant operational benefits, both in terms of cost reduction and service effectiveness. This work lays the groundwork for a scalable, adaptable system that integrates data analytics and field experience, providing beneficial outcomes for both waste management operators and local communities.
Abstract
The following work addresses the optimization of solid waste collection systems, a crucial aspect of waste management. Inefficient collection processes lead to increased operational costs and reduced profitability. The focus of this work is on developing a replicable, data-driven methodology that adapts to the specific requirements of different locations, waste types, and collection needs. Given the non-standard nature of waste collection, which must vary according to local conditions, the objective is to design solutions that are both efficient and intuitive, improving the overall waste management process. The results demonstrate that the adoption of optimized waste collection approach can lead to significant operational benefits, both in terms of cost reduction and service effectiveness. This work lays the groundwork for a scalable, adaptable system that integrates data analytics and field experience, providing beneficial outcomes for both waste management operators and local communities.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Arcuri, Alessandra
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Waste,management,collection,Optimization,Vehicle,Routing,
Problem,Data,analysis
Data di discussione della Tesi
28 Ottobre 2024
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Arcuri, Alessandra
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Waste,management,collection,Optimization,Vehicle,Routing,
Problem,Data,analysis
Data di discussione della Tesi
28 Ottobre 2024
URI
Gestione del documento: