| dc.description.abstract | The Capacitated Vehicle Routing Problem (CVRP) is a significant logistics and
management NP-hard problem in the industrial sector. The CVRP appears in more
large-scale and complicated scenarios as the urban planning sector develops. In CVRP,
the primary objective is to minimize the total cost or distance travelled to fulfill the
FXVWRPHUV¶ GHPDQG by the vehicles while accommodating each vehicle's capacity. The
cluster-first route-second method is one of the techniques utilized to solve the CVRP.
It involves arranging consumers into a few clusters, with each cluster being handled by
a single vehicle. Once clusters are assigned, a route identifying the optimal order to
visit customers inside each cluster is established. The main purpose of this study is to
propose two algorithms in the clustering phase, namely Modified Fuzzy C-means with
K-means for generating initial centroids and K-means MHHO, to large-scale problems
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FCM-based technique is given to solve the clustering phase at a lower cost through the
initialization of centroids. Also, we attempt to build the K-means MHHO by
combining the MHHO algorithm with the basic K-means clustering algorithm and
introducing a capacity limit. After the clustering phase, the routing phase of the TSP
issue is handled using a modified Ant Colony Optimization technique. Two proposed
algorithms are evaluated thorough comparisons and analysis on many benchmark
issues, particularly for large-scale datasets, then resolving the case study. | en_US |
