Research On Port Emergency Dispatch Integrating Transport Tasks And Communication Network Repair

As the hub of modern transportation,plays a crucial role in international communication and national economic development.Within the construction of automated ports,unmanned container transporters serve as essential equipment connecting the quay crane and the yard,undertaking the vital tasks of enhancing the efficiency of transfer and ensuring the smooth operation of the terminal.The mobile logistics resources dominated by unmanned container transporters not only ensure reliable connection in container transfer but also possess the function of safeguarding the robustness of the industrial Internet of Things,thus ensuring the normal operation of both logistics and data networks.The conventional model for mobile resource scheduling at ports no longer meets the planning requirements of intelligent ports in the context of the industrial Internet of Things.Therefore,this thesis focuses on the emergency dispatch of unmanned container transporters that integrate transport tasks and local information network repair in intelligent ports.A dynamic programming model for the path planning of unmanned container transporters with consideration of endurance is constructed,and a high-performance swarm intelligence optimization algorithm is developed for solving the problem.Firstly,this study investigates the problem of path planning for multiple mobile resources that integrate transport tasks and local information network repair.In the context of the industrial Internet of Things in ports,unmanned container transporters not only act as executors of transport tasks but also serve as an important means to enhance network robustness.Drawing upon the context of port transportation scheduling,a topological model for the integrated industrial Internet of Things in ports,which includes unmanned container transporters,can be constructed.This model takes into account the unique characteristics of the unmanned container transporters and the transport tasks,while also considering the constraints of endurance.In doing so,a model for emergency dispatch of unmanned container transporters in ports,which integrates transport tasks and local information network repair,can be established.Furthermore,in order to achieve efficient and rapid resolution of the model,a dynamic multi-subgroup particle swarm optimization algorithm is constructed,driven by a composite of multiple behaviors.By constructing dynamic heterogeneous multi-subgroups and introducing differential learning strategies,particles select different subgroups based on their own information,endowing subgroup individuals with differentiation ability,enhancing the ability to break out of local extreme points and conduct in-depth searches,ultimately improving the performance of the algorithm.Finally,the improved swarm intelligence optimization algorithm is utilized to solve the dynamic dispatching model of unmanned container trucks in ports.For the case design,individual encoding and decoding methods are proposed,mapping individual expressions to adjacency matrix edges and path sequences,respectively.Evaluating connectivity as a measure and timely assessing robustness enhancement,the feasibility and effectiveness of the integrated transportation task and local information network repair of unmanned container truck emergency dispatching models in port are verified.