Research On Intelligent Scheduling Technology For Tugboat Operations In Dalian Port Based On Particle Swarm Optimization

A harbour tugboat is an important tool for ships entering and leaving the port,assisting large ships with berthing,unloading and movement.It has a direct impact on whether ships can enter berth operations or leave the port safely and on time.The improvement of harbour tugboat dispatching efficiency can reduce the waiting time of ships at anchor,thus indirectly improving the speed of ships berthing and leaving the port.At present,most ports give tugboat scheduling solutions based on the scheduling rules set by the port or based on historical experience,which makes it difficult to ensure that ships enter and leave the harbour efficiently.There are many complex factors that need to be considered by the port authorities to develop a reasonable tugboat scheduling plan,and this is a hot issue for ports.The tugboat scheduling problem is a non-linear problem,which is difficult to solve by traditional mathematical planning methods,so this thesis uses an intelligent optimisation algorithm-the particle swarm algorithm-to solve this problem.In this thesis,the particle swarm algorithm,which has the advantages of fast search for optimal solutions and simple parameter setting,is used.For the characteristics of port tugboat operation,a port tugboat scheduling model is established,a particle swarm algorithm is introduced,and the objective function to be achieved by the research is represented by the fitness function of the particle swarm algorithm.After several trials,the tugboat scheduling scheme with the optimal objective function value is selected.According to the above idea,the standard particle swarm algorithm,the particle swarm algorithm with mixed differential evolution and the quantum particle swarm algorithm are used in the tugboat scheduling model to perform experimental calculations on the actual cases in the thesis respectively,and the optimal tugboat scheduling scheme is screened out to verify the rationality of the algorithm research and the feasibility of the tugboat scheduling model in this thesis.The main work is as follows:(1)The current problems of tugboat scheduling are analysed and a mathematical model of tugboat scheduling in the study port area is established under the assumptions limited by the study,while the model parameters are set reasonably to reduce the waiting time of all incoming vessels and the operating costs of berth and tugboat scheduling.(2)Discusses the current commonly used intelligent algorithms,learns about particle swarm algorithms,analyses the advantages and disadvantages of particle swarm algorithms,leads to a hybrid differential evolutionary particle swarm algorithm and a novel particle swarm algorithm used in the study,and implements the algorithm using a real number coding strategy.Attempting to solve the defect that the particle swarm algorithm easily falls into local optimum,the quantum particle swarm algorithm is introduced for the first time in the study of tugboat scheduling,which effectively suppresses the local convergence of the algorithm in the early stage of iterative calculation and improves the accuracy of the experimental results.(3)The scheduling model is simulated using MATLAB software to validate the model and algorithm of this thesis based on examples,and the computational results of the standard particle swarm algorithm,the improved version of the particle swarm algorithm and the quantum particle swarm are compared and analysed.The experiments prove that the research uses the quantum particle swarm algorithm compared with the previous standard intelligent algorithm,not only the convergence speed is significantly improved,and the scientific tugboat scheduling scheme is obtained,but also provides a new research idea for the research of the tugboat scheduling problem.