Research Of Berths Scheduling Of Iron Ore Ports Based On Multi-objective Genetic Algorithm

Since2003, China has been the world's largest importer of iron ore. The requirements of long-term and high-volume transportation promote the development of China's iron ore specialized logistics chain. Coastal ports has been speeding up the construction of specialized iron ore terminal, but still can not meet the needs of economic development and the steel industry. It's very important to optimize resources, to speed up the loading and unloading ships, to increase port utilization and to reduce the cost of port operations by scheduling with the port's existing resources, not by increasing the facilities of the port.The examination is on the berths scheduling to iron ore ports. First of all, China is the world's largest importer of iron ore, the significance of the thesis is based on the analysis of the gap between the capacity and the total imports of China's iron ore ports. The content and methods of the paper are proposed on the research at home and abroad.Second, the model of the paper is simplified on the analysis of iron ore berths scheduling problems. The two optimal objective functions are determined on the analysis of iron ore ship unloading processes. The parked expenses are different from ship to ship because of the scales, so it's not enough to minimize the time of ships stayed in port, what was often mentioned by many past studies. The second objective function is to minimize the cost of port operations, as the main cost of port operations are ship unloading costs and the transmission costs.Third, the relevant knowledge of genetic algorithm is introduced. Relevant overview of the principles and processes of the basic genetic algorithm is summarized. The common methods which are used to realize the multi-objective optimization based on genetic algorithm is introduced.At last, the specific realization process of berth allocation genetic algorithm of ironstone wharf is designed. Based on the sequencing problem this paper is mainly studied, the natural number encode is used; the weight coefficient method is used to realize the multi-objective optimization and its fitness function is obtained by the conversion of the objective function; the arithmetic operators is chosen by using no playback remainder randomly selected method. Two points crossover method is used, and the transposition mutation is taken as its mutation method; the relevant control parameters are chosen and the constraint condition is processed. Finally, a specific instance is presented to verify the genetic algorithm by running the program.