关键词: 水路运输, 泊位调度方案优化, 动态学习, 船舶在泊时间, 并行算法

Abstract:

Berth scheduling scheme for the dwelling vessels in the port is thoroughly determined by the present berths occupied and the estimated handling time of those mooring vessels. However, as the estimation on berth handling time for each vessel is heavily affected by weather and tide etc. and all the influences are fluctuated dynamically by time. Thus the berth scheduling scheme seldom meets the requirements in practice. In this paper, the dynamic learning method is proposed to update the calculation function of handing time, and then the berth scheduling scheme is optimized based on the updated function. A parallel algorithm is designed where the dynamic learning for handling time and optimization for berth scheduling scheme are included. The former provides the updated calculation function for the latter, and the actual execution results of the latter provide learning samples for the former. The effectiveness of the model is verified by examples. The results represent that the calculation deviation of handling time can be reduced by adding the dynamic learning process; and in the optimization scheme, the mean value of handling time is reduced by 2.4 hours and the total cost is reduced by 11.1%.

Key words: water transportation, berth scheduling scheme optimization, dynamic learning, vessel handling time, parallel algorithm