scholarly journals Spanning Cover Inequalities for the Capacitated Vehicle Routing Problem

2021 ◽  
Author(s):  
Guilherme G. Arcencio ◽  
Matheus T. Mattioli ◽  
Pedro H. D. B. Hokama ◽  
Mário César San Felice
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Minimum Cost ◽  
Connected Subgraph ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Spanning Subgraph ◽  
Cover Inequalities ◽  
Capacitated Vehicle

In the k-Minimum Spanning Subgraph problem with d-Degree Center we want to find a minimum cost spanning connected subgraph with n - 1 + k edges and at least degree d in the center vertex, with n being the number of vertices. In this paper we describe an algorithm for this problem and present correctness demonstrations which we believe are simpler than those found in the literature. A solution for the k-Minimum Spanning Subgraph problem with d-Degree can be used to formulate spanning cover inequalities for the capacitated vehicle routing problem.

scholarly journals Hybrid Heuristic Algorithm for solving Capacitated Vehicle Routing problem

2014 ◽  
Vol 12 (9) ◽  
pp. 3844-3851 ◽  
Author(s):  
Moh. M. AbdElAziz ◽  
Haitham A. El-Ghareeb ◽  
M. S. M. Ksasy
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Minimum Cost ◽  
Second Phase ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Vehicle Capacity ◽  
Capacitated Vehicle

The Capacitated Vehicle Routing Problem is the most common and basic variant of the vehicle routing problem, where it represents an important problem in the fields of transportation, distribution and logistics. It involves finding a set of optimal routes that achieve the minimum cost and serve scattered customer locations under several constraints such as the distance between customers’ locations, available vehicles, vehicle capacity and customer demands. The Cluster first – Route second is the proposed approach used to solve capacitated vehicle routing problem which applied in a real case study used in that research, it consists of two main phases. In the first phase, the objective is to group the closest geographical customer locations together into clusters based on their locations, vehicle capacity and demands by using Sweep algorithm. In the second phase, the objective is to generate the minimum cost route for each cluster by using the Nearest Neighbor algorithm. The hybrid approach is evaluated by Augerat’s Euclidean benchmark datasets.

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