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Routing for solid waste collection in the Limeira city on the smart cities approach

Grant number: 17/12249-4
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): October 01, 2017
Effective date (End): September 30, 2018
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal Investigator:Washington Alves de Oliveira
Grantee:Giovani Canesin
Home Institution: Faculdade de Ciências Aplicadas (FCA). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil

Abstract

The concept of smart cities and their specific demands have become research targets in recent years. The technological tool, which we can also call another concept, the Internet of Things (IoT), has facilitated the implementation of the concept previously mentioned. In addition, the development of products and services that assist cities with high infrastructure has opened up several technology business opportunities and new types of demands for solutions to urban problems. For example, the Brazilian population has been producing more and more municipal solid waste, mainly in the last 15 years, which generates high costs for the government in the public management of this waste. An alternative to reducing such costs and already used positively in some cities is the use of automated (intelligent) bins, which provide accurate information on the amount of waste that must be collected, thus helping to improve the collect. In this project, we propose the development of a methodology that is capable of automating the design of the routes to be used by solid waste collection vehicles in the city of Limeira, SP. This methodology consists of three decision levels. At the first level, we will use a mathematical model of set coverage to decide the best places to locate each smart bin. Then we will apply a designation template to assign to which transfer terminal (before the landfill) each bin will be taken. Finally, we are going to develop a vehicle routing model that will provide the best routes for collecting and transporting all bins. In the third level, the information provided by intelligent bins is crucial in feeding information that will be used as parameters in the mathematical models, helping in the management of resource allocation and in the capacity constraints of the collection system. We intend to use in a combined way the use of solvers and the genetic algorithm method in solving the various subproblems present in the decision levels. (AU)