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Algoritmo de criação de rotas para entrega de mercadorias: Estudo de caso
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Orientador(es)
Resumo(s)
A melhoria do planeamento das rotas de entrega é de grande importância na atividade de
empresas de logística, como a TNB, para aumentar a eficiência operacional e a satisfação dos clientes.
Atualmente, o processo de criação de rotas é ineficiente, pois consome tempo excessivo e está sujeito
a erros que impactam negativamente a produtividade e os custos operacionais.
Este estudo propõe o desenvolvimento de um algoritmo para a criação de rotas de entrega, e
para implementar numa base diária. Utiliza dados como o peso, o volume, as coordenadas geográficas
e as janelas horárias das entregas, respeitando as restrições de capacidade das carrinhas e os horários
pré-estabelecidos pelos clientes.
A abordagem adotada envolve a análise de dados históricos de entregas realizadas para mo-
delar os tempos de entrega através de regressão linear múltipla, incluindo variáveis como o tempo de
serviço, o peso, o volume e o número de caixas. Com base nesses dados, o algoritmo desenvolvido
organiza as rotas de forma a reduzir a distância total percorrida, garantindo o cumprimento das janelas
horárias e resultando numa utilização da frota mais eficiente, sejam carrinhas elétricas ou a gasolina.
Os resultados obtidos na aplicação do algoritmo a um mês de planeamento de rotas mostram
uma redução no tempo consumido na criação das rotas e um aumento na eficiência das entregas,
refletindo-se em menores custos operacionais e maior satisfação dos clientes. A implementação do
algoritmo permite que a empresa realize uma melhor utilização da sua frota, alinhando-se com os
objetivos de sustentabilidade da empresa. Além disso, contribui para a diminuição do consumo de
combustível, promovendo benefícios económicos e ambientais. Este estudo evidencia a importância
da automatização e da análise de dados na gestão de operações logísticas, propondo uma solução
eficaz para a criação de rotas de entrega que pode ser adaptada a diferentes contextos empresariais.
As implicações deste trabalho sugerem que a adoção de algoritmos pode transformar a eficiência ope-
racional de empresas de transporte, proporcionando vantagens competitivas no mercado.
The improvement of delivery routes is of great importance for logistics companies, such as TNB, to improve operational efficiency and customer satisfaction. Currently, the process of route planning is inefficient, as it is very time-consuming, and prone to errors, which negatively impact productivity and operational costs. This study proposes the development of an algorithm for creating delivery routes and imple- menting daily. Utilizing data such as weight, volume, geographic coordinates, and delivery time win- dows, while respecting the trucks' capacity constraints and customer pre-specified delivery times. The approach involves analyzing historical delivery data to model delivery times through multi- ple linear regression, including variables such as service time, weight, volume, and number of pack- ages. Based on this data, the developed algorithm organizes routes to reduce total distance travelled, ensuring compliance with delivery time windows, and a better use of the companies' fleet, whether they are electric or gasoline-powered trucks. The results obtained after applying the algorithm to a full month of route planning show a re- duction in the time spent on route planning and an increase in delivery efficiency, resulting in lower operational costs and higher customer satisfaction. The implementation of the algorithm enables the company to improve the use of its fleet, aligning with the company's sustainability goals. Furthermore, it contributes to reduce fuel consumption, promoting both economic and environmental benefits. This study highlights the importance of automation and data analysis in managing logistics operations, pro- posing an effective solution for delivery route planning that can be adapted to different business con- texts. The implications of this work suggest that the adoption of algorithms can transform the opera- tional efficiency of transportation companies, providing competitive advantages in the market.
The improvement of delivery routes is of great importance for logistics companies, such as TNB, to improve operational efficiency and customer satisfaction. Currently, the process of route planning is inefficient, as it is very time-consuming, and prone to errors, which negatively impact productivity and operational costs. This study proposes the development of an algorithm for creating delivery routes and imple- menting daily. Utilizing data such as weight, volume, geographic coordinates, and delivery time win- dows, while respecting the trucks' capacity constraints and customer pre-specified delivery times. The approach involves analyzing historical delivery data to model delivery times through multi- ple linear regression, including variables such as service time, weight, volume, and number of pack- ages. Based on this data, the developed algorithm organizes routes to reduce total distance travelled, ensuring compliance with delivery time windows, and a better use of the companies' fleet, whether they are electric or gasoline-powered trucks. The results obtained after applying the algorithm to a full month of route planning show a re- duction in the time spent on route planning and an increase in delivery efficiency, resulting in lower operational costs and higher customer satisfaction. The implementation of the algorithm enables the company to improve the use of its fleet, aligning with the company's sustainability goals. Furthermore, it contributes to reduce fuel consumption, promoting both economic and environmental benefits. This study highlights the importance of automation and data analysis in managing logistics operations, pro- posing an effective solution for delivery route planning that can be adapted to different business con- texts. The implications of this work suggest that the adoption of algorithms can transform the opera- tional efficiency of transportation companies, providing competitive advantages in the market.
Descrição
Palavras-chave
Planeamento de rotas Algoritmo de criação de rotas Logística Eficiência operacional Análise de dados