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Abstract
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The study titled "Vehicle Routing with Multiple Deliverymen: Modeling Using Fuzzy C-Means Clustering" addresses the complexities of logistics and transportation management, particularly in scenarios involving multiple delivery personnel. The research proposes a novel model that integrates Fuzzy C-Means (FCM) clustering to optimize vehicle routing by efficiently grouping delivery points based on proximity and demand characteristics. This approach not only enhances route planning but also accommodates uncertainties in customer demands, making it particularly suitable for dynamic environments. The job outlines the necessity of advanced methodologies like FCM in tackling the vehicle routing problem (VRP), which is a well-known combinatorial optimization challenge. By employing a Cluster-First Route-Second (CFRS) strategy, the model simplifies the routing problem and improves computational efficiency. Furthermore, it explores hybrid algorithms that combine FCM with other optimization techniques, demonstrating significant improvements in performance metrics such as travel time and operational costs. In addition to traditional delivery scenarios, the research highlights the adaptability of FCM in emerging fields like drone delivery and last-mile logistics, emphasizing its relevance in modern logistics operations. The study aims to develop a comprehensive framework for vehicle routing that not only minimizes costs but also enhances customer satisfaction through timely deliveries. Ultimately, this research contributes valuable insights into optimizing logistics operations by leveraging advanced clustering techniques to address contemporary challenges in resource allocation and route planning.
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