The transition from Internal Combustion Engine vehicles to Electric Vehicles presents challenges for fleet managers in terms of adapting operational processes and systems. One prominent challenge is the Electric Vehicle Routing Problem, which requires careful consideration of battery monitoring, efficient route planning, charging infrastructure availability, and vehicle performance management. The goal is to alleviate range anxiety and ensure effective fleet management. In this context, we propose a Reinforcement Learning approach in conjunction with graph-based modeling to solve Electric Vehicle Routing Problem with Time Window. This paper provides a novel approach addressing the need for efficient and sustainable electric vehicle fleet management. Our aim is to minimize the distance traveled while serving customers within their time windows using the combination of Structure2vect and Double Deep Q-Network. Real-world data from a public utility fleet company in Tunisia is utilized to evaluate the proposed model, and comparisons are made with conventional benchmarking strategies and other Reinforcement Learning approaches. The results highlight the effectiveness of the proposed model achieving a reduction up to 50% in traveled distance while demonstrating enhanced computational efficiency through reduced complexity and optimized runtime. Moreover, the obtained model can be directly applied to treat the large-scale adoption of electric vehicles in fleets.

In this study, we present an indoor localization system leveraging Zigbee-based hardware for real-time positioning. We conduct in-house measurements in a controlled environment to generate a robust dataset for the system. A six-step methodology is employed, encompassing received signal strength indicator (RSSI)-to-image transformation, feature extraction using a pre-trained convolutional neural network (CNN) model, dimensionality reduction, clustering, and classification. By converting RSSI values into images, we capture spatial patterns more effectively. A multi-power level fusion strategy combines RSSI data from three distinct power levels into RGB images, improving localization accuracy and robustness against interference. K-means clustering segments the environment into distinct zones, streamlining classification. Experiments demonstrate the significant contributions of power level fusion, clustering, and classification, with the multi-power level fusion yielding improved performance compared to single power levels. Our approach, based on a custom CNN architecture, offers enhanced precision and efficiency for indoor localization tasks, providing valuable insights into the integration of advanced signal processing and deep learning.