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The capacitated vehicle routing problem revisited: using fuzzy c-means clustering

Author

Listed:
  • Henrique Ewbank
  • Peter Wanke
  • Henrique L. Correa
  • Otávio Figueiredo

Abstract

This paper proposes to simplify complex distribution scenarios and find near-optimal solutions by applying a heuristic approach for solving the capacitated vehicle routing problem with a homogeneous fleet using fuzzy c-means as the clustering technique. A memetic algorithm determines the number of clusters and an improved fuzzy c-means algorithm allocates customers to routes. When benchmarked with other methods and compared with 50 known instances from the literature, it indicated an error average of less than 3%. Due to the nature of the errors studied, a tobit regression has been applied to predict the average percent error in terms of the characteristics of the demand and the distance of each customer. Results also suggest that kurtosis and skewness of the distances among all customers, capacity of the vehicles and standard deviation of the demand could be used to predict the average percent error.

Suggested Citation

  • Henrique Ewbank & Peter Wanke & Henrique L. Correa & Otávio Figueiredo, 2019. "The capacitated vehicle routing problem revisited: using fuzzy c-means clustering," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 34(4), pages 411-430.
    • Handle: RePEc:ids:ijlsma:v:34:y:2019:i:4:p:411-430
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