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Communication Dans Un Congrès Année : 2017

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.
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Dates et versions

hal-01712413 , version 1 (19-02-2018)

Identifiants

  • HAL Id : hal-01712413 , version 1

Citer

Mathieu Coulama, Lei Wang, Franck Fontanili. Cluster of demand for a Chinese logistics company using K-Mean and genetic algorithm. IFAC 2017 - 20th World congress of the International Federation of Automatic Control, IFAC, Jul 2017, Toulouse, France. p.9575-9579. ⟨hal-01712413⟩
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