Research On Dynamic Pricing Of Container Liner Shipping Space Based On Improved SARSA Algorithm

Economic globalization has become an important feature of today’s international community and the mainstream trend in the future.The economic development of all countries in the world is inseparable.It is the so-called "pull one hair and move the whole body".As an important means of connection,container liner transportation has already become the main and effective mode of transportation in the shipping industry and even international trade.At the same time,the state of container liner transportation market is closely related to the development level of international trade,and any fluctuation of global economic development may have a great impact on the transportation market.In addition to the global economic situation,transportation costs,market demand and political factors will also have a significant impact on it.With the complex and changeable market environment,the competition among liner companies is becoming increasingly fierce.How to use the dynamic pricing strategy instead of the traditional pricing method to deal with the rapidly changing market demand and enhance the market share of liner companies has become the focus of research.Therefore,in order to help improve the revenue level of liner companies,in the era of artificial intelligence and big data,this paper studies the dynamic pricing of container liner shipping space in non-stationary environment with the help of reinforcement learning.First of all,this paper analyzes and defines the dynamic pricing problem of container liner shipping space under the condition of unknown demand form.On this basis,a dynamic pricing model of container liner shipping space is established to maximize the revenue of liner shipping companies.Secondly,the real-time dynamic pricing problem is modeled as a discrete finite period Markov decision-making process,and its specific details are described.Based on reinforcement learning theory,the process of using sarsa algorithm to solve the real-time dynamic pricing problem is analyzed and discussed.Thirdly,in order to improve the convergence speed and solution performance of the traditional sarsa algorithm,according to the characteristics of the problem studied,the improved SARSA(λ)dynamic pricing algorithm based on memory matrix,HA-SARSA(λ)and SA-SARSA(λ)dynamic pricing algorithm are designed respectively.Finally,simulation experiments are designed to compare the performance of the four dynamic pricing algorithms in linear and exponential demand environments,and the market demand environments with time-varying and volatility are constructed to further verify the practical value of the proposed algorithm.Through the analysis of the experimental results,we can see that:(1)it is of practical significance to model the pre-sale process of container liner shipping space as a discrete Markov decision process with finite period and fixed quantity.(2)the feasibility of real-time dynamic pricing sarsa algorithm is verified.(3)the improved scheme of dynamic pricing algorithm based on reinforcement learning designed in this paper is feasible and can be further improved.(4)the improved dynamic pricing algorithm not only has good revenue performance,but also can help liner companies deal with the complex and changeable market demand environment.