<![CDATA[Distribution lies at the heart of logistics, as it directly dictates both operational costs and how fast customers receive their items. The Sidoarjo Branch Post Office, which manages a massive delivery network, currently struggles with inefficient routing and an uneven workload among its couriers. To tackle this, our study focuses on optimizing these delivery paths by applying the Capacitated Vehicle Routing Problem (CVRP) framework, powered by a Genetic Algorithm. We chose the Genetic Algorithm for its superior ability to navigate complex solution spaces without getting trapped in "local optima" or dead-end results. The process involves several key stages: initializing the population, evaluating fitness, selecting individuals, and performing crossover and mutation to refine the results. By using the CVRP model, we aim to slash travel distances while staying within strict limits like vehicle capacity and fair workload distribution. Our findings reveal that this algorithm-driven approach consistently outperforms manual methods by finding shorter, more logical routes. Furthermore, the web-based system we developed does more than just calculate; it provides a clear "before-and-after" comparison, balances courier tasks, and offers a precise sequence of visits all while ensuring no vehicle is overloaded. Ultimately, this method has proven to be a game-changer for the Sidoarjo Branch Post Office in boosting its overall operational performance.]]>
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