Data-Driven Intelligent Clustering-Based Optimization for Enhancing Urban Logistics Delivery Systems: A Case Study in Casablanca, Morocco
Keywords:
DDS, Supply chain, Stock delivery, Unsupervised learning, ClusteringAbstract
These In the field of supply chain management, making sure products get to people efficiently is not easy. There are issues like customers being unhappy, figuring out the optimizing itinerary of trucks, how much they can carry, and optimizing the delivery time. In this paper, we are introducing a smart system that makes the whole delivery process smoother, starting from managing inventory to reaching the customers. Our system leverages clustering techniques to automate and simplify this complex process. We collected data and tested our approach on a mass retail company in Casablanca, Morocco. This data includes information about customer locations, order details, and the available delivery trucks with their capacities. At the core of our solution lies a unique clustering algorithm, custom-made to handle our specific challenges. The approach starts by defining how far apart cus-tomers' locations can be, ensuring we don't group locations that are too distant from each other. Then, we use a straightforward method to create these groups based on proximity between locations and order details. This ensures the efficient allocation of customer orders to clusters, maximizing truck fill rates. In short, our innovative approach streamlines delivery operations, reduces cus-tomer complaints, optimizes fleet management and guarantees on-time, cost-effective deliveries with a highly satisfactory service rate.
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