This review paper examines recent advancements in vehicle routing optimization under time uncertainty, focusing on the vehicle routing problem (VRP). It sys-tematically analyzes research papers to identify strategies for optimizing routes despite temporal uncertainties, covering key areas such as optimization algo-rithms, uncertainty modeling techniques, and simulation methods. The study investigates dynamic dispatching models, reliability considerations, and multi-objective optimization approaches. By synthesizing existing literature, this pa-per presents the current state of research in vehicle routing under time uncer-tainty and suggests potential future research directions. Our findings indicate that integrating robust optimization techniques with advanced simulation meth-ods could significantly enhance decision-making processes in uncertain envi-ronments. Additionally, the paper highlights the role of machine learning and artificial intelligence in developing adaptive algorithms that respond to dynamic changes in real-time. As the need for efficient logistics solutions grows, this comprehensive review underscores the importance of addressing uncertainties in vehicle routing to improve operational efficiency, reduce costs, and enhance customer satisfaction.

Autonomous semantic indoor navigation requires robust low-level control and high-level understanding of objects and spatial context in cluttered and partially occluded environments. While deep reinforcement learning (DRL) methods such as twin delayed deep deterministic policy gradient (TD3) enable reactive obstacle avoidance, they typically struggle with long-horizon semantic navigation, where object-location memory and language-level reasoning are required. We present a lightweight hierarchical two-stage framework that, to the best of our knowledge, is introduced for the first time to integrate a locally deployed vision–language model (VLM), semantic object coordinate memory, and a TD3-based DRL controller for language-conditioned indoor navigation. In Stage 1, the robot performs semantic exploration using odometry, 2D LiDAR, and VLM-based object recognition to build a geometric map and store detected object categories with their estimated world coordinates in a structured javaScript object notation (JSON) semantic memory. In Stage 2, a natural language query is used to retrieve the target object coordinates from memory and pass them to a TD3 target point navigation policy, which performs mapless navigation using odometry and RealSense RGB-D perception. The proposed framework combines open-vocabulary VLM-based object coordinate estimation, LiDAR mapping, RGB-D perception, and language grounding within a unified semantic memory representation. Experiments in a ROS-integrated realistic simulation demonstrate consistent goal-reaching performance and improved navigation efficiency compared with an Artificial Potential Field baseline using the same VLM and a DRL + GPT-4o mini configuration. We also compare the proposed VLM-based recognition module with YOLO-World v2.6 and Grounding DINO, showing that the VLM-based approach provides more reliable semantic grounding and target-coordinate estimation in the tested indoor navigation scenarios, particularly for flexible natural language object queries.