<![CDATA[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.]]>
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