Modelo matemático para otimização de roteamento dos veículos na coleta de resíduos de saúde: estudo de caso em Ponta Grossa-PR

Abstract

Global economic growth, population increase, rapid urbanization, and improved living standards have led to a substantial increase in the waste that cities need to manage. Within this context, the growing generation of hazardous and infectious health waste stands out, making it an additional challenge to efficiently manage this waste, which encompasses processes such as separation, collection, transportation, and disposal, aiming to reduce volume and quantity without harming the environment and public health. Within these procedures, collection, and transportation emerge as crucial elements, given the relevance of effective logistics to ensure regular and safe collection of this waste, covering multiple collection points. Given the peculiar nature of these wastes, their inadequate management represents an environmental and public safety risk, reinforcing the need for efficient logistics to address such challenges. In this context, this study aims to propose a mathematical model to optimize the total distance traveled through the vehicle routing technique for the collection of healthcare waste. The approach is based on the characteristics of the Periodic Vehicle Routing Problem (PVRP). Achieving this objective involved conducting systematic literature reviews on variants of the routing problem and relevant solution methods. The mathematical model was solved using two heuristic algorithms: the Clarke and Wright (CW) construction algorithm and the Particle Swarm Optimization (PSO) algorithm. To validate the model, data from a company located in the city of Ponta Grossa, Paraná, which specializes in waste collection, and categorizes its customers into two groups (large and small), according to the demand and frequency of collection, were used. The test results revealed significant achievements. For small customers, using the CW algorithm reduced the total monthly distance by 31%, while the PSO algorithm achieved an even greater reduction of 41%. The reductions were 17% with CW and 22% with PSO for large clients. In addition, both algorithms effectively distributed the total distance over the month. Furthermore, the reduction of spaces contributed to lower operating costs and the reduction of pollutant emissions, improving the effectiveness of collection, and benefiting both the company and the community. In summary, this work achieved its proposed objective by solving the mathematical model using distinctive features of heuristic algorithms, combining them with company data, and reviewing specialized literature.