Berth And Yard Integrated Scheduling Problem In Multiple Bulk Ports

Bulk cargo trade plays a more and more important role in international trade. At the same time, the operational efficiency of bulk cargo port gains increasingly attention. As the scarce resources, berth and yard resources play a very important role in bulk port. Scientific and reasonable schedule of berth and yard can greatly improve the efficiency of resource utilization, thus improve the operational efficiency and service level of bulk port. Against this background, this paper studies the berth and yard integrated scheduling problem in multiple bulk ports.First of all, based on a large number of related literatures at home and abroad, combining the actual situation of the bulk port operation, a multi-objective model of berth and yard integrated scheduling problem in multiple bulk ports is proposed. The model contains two levels, scheduling optimization between different bulk ports and berth and yard integrated scheduling optimization inside the bulk port. The objective function consists of three parts:the cost of vessels berthing at the port, the transportation cost between berth side and yard side, and the workload imbalance of the berth over the planning horizon. The three components of the objective function are weighted by the parameter λ, and carry on sensitivity analysis to design the parameter.Aiming at the above problem model, a hybrid algorithm with particle swarm optimization algorithm and simulated annealing algorithm is proposed to solve the model. The model consists of two parts:the berth scheduling problem and the yard allocation problem, thus a method of segmented collaborative encoding is presented. In terms of population initialization, the chaos theory is adopted to give the initial population characteristics of diversity and ergodicity. To update particle status, this paper used the crossover and mutation methods of genetic algorithm, and the simulated annealing operation is introduced in order to enhance the global search ability of the algorithm.Finally, this paper set the iron ore ports of ZhouShan islands as an example for empirical study. The basic data such as berth and yard are epurated from the actual operating situation of the iron ore ports. In terms of algorithm parameter setting, the parameters are determined by a large number of experiments using the method of comparative analysis. To verify the validity of the model and algorithm, this paper designed three sets of experiments. In the first experiment, comparing the performance of hybrid particle swarm optimization algorithm and particle swarm algorithm, the result shows that the hybrid particle swarm algorithm has a distinct advantage in the quality and stability. In the second experiment, using the models and algorithms proposed by this paper to calculate the example of iron ore ports. Experimental results show that under different case size, the scheduling scheme calculated by this method is better than the original scheduling scheme. In the third experiment, comparing the performance of integrated scheduling with independent scheduling, the experiment shows that the integrated scheduling is better than independent scheduling. Through the above three experiments, indicating that the integrated scheduling results in better, more efficient resource allocations, and verify the scientificity of integrated scheduling model and the applicability of hybrid particle swarm algorithm.