<![CDATA[Purpose – This study aims to optimize delivery operations by implementing a flexible clustering method to handle demand uncertainty and improve logistics efficiency. Methodology – This study develops a clustering algorithm using a two-dimensional time-warping approach to group demand points based on spatial proximity and demand characteristics. The methodology consists of three stages: 1) processing data on point distances, 2) clustering using two-dimensional time warping, and 3) validating through silhouette analysis. Findings – This study resulted in optimal and efficient demand clustering through location clustering with a Silhouette coefficient value of 0.7 or an accuracy and feasibility level of 70%. The algorithm also shows improved computational efficiency compared to traditional approaches, making it suitable for practical applications in uncertain and dynamic environments. Practical implications – This study holds significant importance for businesses in the logistics and retail sectors. Through demand clustering, businesses can effectively group customer demands and utilize this information to optimize inventory management and delivery solutions.]]>
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