Cluster of demand for a Chinese logistics company using K-Mean and genetic algorithm
Résumé
This paper is related to the use of the K-mean (KM) clustering method combined with Genetic Algorithm (GA) to solve a multi-objective problem in a supply chain field. A simplified use case based on a real Chinese logistics company is proposed as a proof of concept. This logistics company wants to find an efficient way to cluster the demand of destination cities in order to maximize truck load for delivery. To do so, we used KM to cluster destination cities by demand and GA to minimize the distance between cities and their centroid.