Journal of System Simulation
Abstract
Abstract: In the case of dynamic demand, considering the order due date, vehicle transportation time window, and other factors, this paper developed an optimization model of periodic product oil distribution with multiple trips and multiple due dates to maximize the distribution revenue. The paper also designed a reinforcement learning-based large neighborhood search algorithm to solve the problem. The initial solution was constructed based on the forward insertion heuristic algorithm. Then, a deep reinforcement learning model for neighborhood operator selection was designed. By fitting the action value function through the double deep Q network, the optimal neighborhood operator was selected, and the optimal distribution scheme was obtained. The experimental results show that the large neighborhood search algorithm based on reinforcement learning proposed in this paper can effectively improve the solving speed while ensuring the solution's quality.
Recommended Citation
Xie, Yong; Gao, Hailong; Chen, Yutao; and Wang, Huanjiang
(2025)
"Optimization of Product Oil Distribution with Multiple Trips and Multiple Due Dates under Dynamic Demand,"
Journal of System Simulation: Vol. 37:
Iss.
8, Article 10.
DOI: 10.16182/j.issn1004731x.joss.01 24-0243
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/10
First Page
2016
Last Page
2029
CLC
TP18；F253.4
Recommended Citation
Xie Yong, Gao Hailong, Chen Yutao, et al. Optimization of Product Oil Distribution with Multiple Trips and Multiple Due Dates under Dynamic Demand[J]. Journal of System Simulation, 2025, 37(8): 2016-2029.
DOI
10.16182/j.issn1004731x.joss.01 24-0243
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