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The Mixed Fleet Vehicle Routing Problem Optimization of Multi-depot
Distribution with Time Windows
ZHANG Jiarui
2022, 47(3):
55-63.
DOI: 10.16638/j.cnki.1671-7988.2022.003.012
With the continuous development of logistics industry, the number of logistics transportation
vehicles is increasing, and the use of traditional fuel vehicle caused a certain pressure to the environment.
In recent years, because of the characteristics of energy conservation and environmental protection,
logistics electric vehicles have been widely used. However, due to the electric vehicle has to charging for
a long time and the development present situation of transportation industry, the electric vehicle is
currently not completely replace the traditional fuel vehicle, the two kinds of vehicles will exist in the field
of logistics distribution. The open multi-depot distribution vehicle routing problem with fuel vehicle and electric vehicle as distribution vehicles is research topic of this paper. The goal is to minimize total
distribution costs, which including carbon emission cost, transportation cost and time window penalty cost.
A linear programming mathematical model is established. For the NP-hard characteristic, an improved
particle swarm optimization algorithm is designed. The theory of good-point set is employed in the
particle swarm optimization algorithm for generating initial population to increase the diversity of particle
swarm optimization algorithm. In iteration phase of the particle swarm optimization, in order to achieve
the global optimal, the random neighborhood strategy is provided to avoid the particle swarm into local
optimum. Finally, a numerical example is designed to test. The results show that the improved particle
swarm optimization algorithm is superior to the standard particle swarm optimization algorithm. By
analyzing the costs obtained by the two algorithms, it is concluded that the ratio of the total cost obtained
by the improved particle swarm optimization algorithm is 5.69% lower than that obtained by the standard
particle swarm optimization algorithm, which proves the effectiveness of the improved particle swarm
optimization algorithm designed in this paper. The number of traditional fuel vehicle used considering the
carbon cost is declined compared to without considering the cost of carbon emissions, and the total cost of
open hybrid VRP is less than the cost of closed-loop hybrid VRP.
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