Authors:
Giovani Farias
;
Timotio Cubaque
;
Eder Gonçalves
and
Diana Adamatti
Affiliation:
Federal University of Rio Grande, FURG, Center for Computational Sciences, C3, RS, Brazil
Keyword(s):
Vehicle Routing Problem, Capacitated Vehicle Routing Problem, Heuristic, Ensemble.
Abstract:
The vehicle routing problem presents an intricate challenge within logistics and cargo transport. The primary objective is to determine the most efficient vehicle routes to visit a designated set of clients while minimizing overall transportation costs. The capacitated vehicle routing problem represents a specific variation of this challenge, introducing constraints such as routes commencing and concluding at the same depot, assigning each client to a single vehicle, and ensuring that the total demand for a route does not exceed the vehicle’s capacity. This paper explores the hypothesis that optimal optimization strategy is contingent on spatial data density. Thereby, we evaluate various routing strategies using heuristic methods and ensemble techniques applied to spatial data. The goal is to identify the most effective strategy tailored to a specific spatial data pattern. To accomplish this, we employ two clustering methods – K-means and DBSCAN – to group clients based on their geog
raphical locations. Additionally, we utilize the nearest neighbor heuristic to generate initial solutions, which are subsequently refined through the implementation of the 2-Opt method. Through experiments, we demonstrate the impact of each approach on the resulting routes, taking into account the spatial data distribution.
(More)