Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800257-9.doi: 10.11896/jsjkx.210800257

• Artificial Intelligence • Previous Articles     Next Articles

Study on Dual Sequence Decision-making for Trucks and Cargo Matching Based on Dual Pointer Network

CAI Yue, WANG En-liang, SUN Zhe, SUN Zhi-xin   

  1. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    Post Industry Technology Research and Development Center of the State Posts Bureau(Internet of Things Technology),Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:CAI Yue,born in 1997,postgraduate.His main research interests include information networks,reinforcement learning,and deep neural networks.
    SUN Zhi-xin,born in 1964,doctor,professor,doctoral supervisor.His main research interests include the theory and technology of network communication,computer network and security.
  • Supported by:
    National Natural Science Foundation of China(61972208).

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Abstract: Due to the uneven utilization of road transportation resources in my country,the supply and demand of trucks and cargo become a hot issue today.In order to maximize the utilization of overall transportation resources,the truck and cargo supply-demand matching platform needs to integrate transportation demand and capacity,reduce costs and improve efficiency.The algorithms used by most platforms are usually heuristic algorithms to solve the problem of trucks-cargo matching.Such algorithms have a bottleneck in optimizing when faced with large-scale problems.In response to the above-mentioned problems,this paper transforms the supply-demand matching problem of vehicles and goods into a double sequence decision-making problem for the first time.Based on this,we study an efficient algorithm that is suitable for today’s vehicle and goods supply-demand matching links.First,a mathematical model of trucks-cargo matching is proposed and the model is abstracted as a double sequence decision problem,and then a dual-pointer-network algorithm is innovatively proposed to solve this problem.The experiment uses the Actor-Critic algorithm as the model training framework to train the dual-pointer-network and evaluates the model.Experiments show that the dual-pointer-network’s vehicle-to-cargo matching solution method is equivalent to traditional heuristic algorithms in small problem scales,and surpasses heuristic algorithms in large problem scales.At the same time,the time consumption is greatlyreduced.

Key words: Dual pointer network, Double sequence decision-making problem, Deep reinforcement learning, Combinatorial optimization, Trucks and cargo matching, Critic network

CLC Number: 

  • TP311
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