This paper evaluates a physical model for estimating the energy consumption of Battery Electric Vehicles (BEVs) in urban deliveries, as well as proposing an analysis of the impact of speed, road profile, load weight, and number of stops on the range of these vehicles. The proposed microscopic physical model is based on vehicle dynamics equations, considering exponential battery regeneration during braking events. Physical parameters and auxiliary system consumption of the BEV are taken from the literature, and a standard driving cycle is proposed, with a speed and acceleration profile based on real-world data. A total of 160 distinct scenarios were simulated, varying the vehicle’s maximum speed, road profile, load weight and number of stops. The results demonstrate that these factors have a significant and quantifiable effect on BEV range. Uphill road grades can reduce range by up to 63% compared to flat terrain, while higher speeds substantially increase energy consumption – range is up to 88% higher at 10 km/h and 54% lower at 80 km/h, compared to a baseline of 40 km/h.

This study focuses on developing a heuristic for Decision Support Systems (DSS) in e-commerce logistics education, specifically addressing the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). The 2E-CVRP involves using Urban Transshipment Points (UTPs) to optimize deliveries. To tackle the complexity of the 2E-CVRP, DSS can employ fast and effective techniques for visual problem-solving. Therefore, the objective of this work is to develop a local search heuristic to solve the 2E-CVRP quickly and efficiently for implementation in DSS. The efficiency of the heuristic is assessed through benchmarks from the literature and applied to real-world problems from a Brazilian e-commerce retailer, contributing to advancements in the 2E-CVRP approach and promoting operational efficiency in e-commerce logistics education. The heuristic yielded promising results, solving problems almost instantly, for instances in the literature on average in 1.06 s, with average gaps of 6.3% in relation to the best-known solutions and, for real problems with hundreds of customers, in 1.4 s, with gaps of 8.3%, demonstrating its effectiveness in achieving the study’s objectives.

Na literatura encontram-se muitos métodos para resolver problemas de roteirização de veículos. Tais métodos buscam otimizar um parâmetro específico, como a distância total percorrida ou o tempo de processamento computacional. Porém observa-se que alguns métodos apresentam ou ressaltam a importância de buscar resultados em outros parâmetros, como número de veículos no plano de rotas. Deste modo, o presente estudo objetivou utilizar os referidos indicadores para comparar planos de rotas gerados por três algoritmos bem conhecidos. Para tanto, em primeiro lugar foi necessário buscar na avaliação de desempenho logístico quais indicadores poderiam ser usados nessa comparação para posterior realização de testes nas instâncias usadas por Solomon (1987). Assim, foram propostos 7 indicadores que não apresentaram correlação, ou seja, não houve evidência de relação de “trade on” ou “trade off” entre os indicadores. Além disso, dos métodos testados, a inserção apresentou de forma geral o melhor desempenho segundo os indicadores propostos.