Multi-agent Collaborative Optimization Of Automated Container Ports Considering Carbon Emissions

With the development of shipping market,international port competition is increasingly fierce.Under the condition of limited port resources,it is particularly critical to improve the operation efficiency of container terminals.Container terminal operations mainly include berth assignment,quay and AGV assignment and yard operation.This paper will combine the actual operation scenarios of container ports to analyze the relevant factors affecting the operation efficiency of container ports.In order to reduce port operation cost and improve port operation efficiency,collaborative optimization is mainly carried out among berth,quay and AGV in the operation process.The research object of this paper is automatic container terminal,and the research focus is collaborative optimization among facilities and equipment.Combined with the actual operation scenario,the dual optimization objective is to minimize the total operation cost and the total operation time.By means of weighting,the multi-objective problem is transformed into a single objective problem.The main research contents of this paper are as follows:(1)By reading a large number of literature and aiming at the deficiencies existing in the current research,this paper focuses on the multi-agent collaborative optimization of ports in the context of low-carbon,mainly carrying out analysis and research on berth assignment,quay and AGV allocation of container terminals.(2)From the perspective of the system,the berth,quayside and AGV are regarded as a unified operating system,and a multi-objective optimization model is established to minimize the total operating cost and total operating time of ships arriving at the port.It provides the basis for port resource scheduling and helps to optimize port operation.(3)According to the model characteristics and algorithm performance,the gray Wolf algorithm is selected for solving,and the algorithm design of the port multi-agent cooperative optimization is carried out.Finally,the performance of each algorithm is tested by benchmark test function to verify the validity,rationality and scientificity of gray Wolf algorithm.(4)Taking the new Qianwan automated container terminal as the case study object,combined with the operation data of the container port,the collaborative optimization model of berth assignment,quay and AGV allocation was applied to the actual case,and the algorithm was solved to discuss the collaborative optimization of port equipment in different scenarios.