Capacitated waste collection problem solution using an open-source tool

• Autor(a) principal: Silva, Adriano S.
• Data de Publicação: 2023
• Outros Autores: Alves, Filipe, Díaz de Tuesta, Jose Luis, Rocha, Ana Maria A.C., Pereira, Ana I., Silva, Adrián, Gomes, Helder
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10198/27261
• topic: Waste collection; Municipal solid waste; Waste management; Vehicle routing problem; Route optimization; Google OR-Tool

Population in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems.