基于改进樽海鞘群算法的卸车调度优化

Optimization of Discharging Scheduling Based on Improved Salp Swarm Algorithm

针对港口载煤列车的卸车调度流程主要依靠工人经验进行调度作业,存在决策时间长、作业冲突和列车在港时间过长等问题。以列车在港时间最少为总优化目标,在已知列车到港时间及堆垛与煤种对应关系的前提下,考虑工作机械可用性、作业流程可达性及其相互约束关系等因素,构建了卸车调度数学模型。提出了一种基于改进樽海鞘优化算法的优化调度方法。引入自适应惯性权重,可有效地提高算法收敛速度;引入随机柯西变异策略,可有效地提高算法寻优能力。5个测试函数的测试结果表明:相比于樽海鞘优化算法、自适应樽海鞘优化算法、粒子群算法与鲸鱼优化算法,改进樽海鞘优化算法收敛速度更快,精度更高。港口堆场作业实际数据的仿真实验表明:改进樽海鞘优化算法可优化出满意的卸车调度任务,减少了火车总在港时间,提高了港口总体的工作效率。

In the past,port coal discharge process mainly relied on the experience of workers for discharging operations,and there were problems such as long decision-making time,frequent operation conflicts,and excessive stagnation of train in the port.Taking the minimum time for trains in port as the overall optimization objective,on the premise of knowing the train arrival time and the corresponding relationship between stacking and coal types,a mathematical model of discharge scheduling was constructed,which considered the availability of working machinery,accessibility of operation process and their mutual constraints.Finally,the optimal scheduling method based on the improved salp swarm optimization algorithm was proposed.The adaptive inertia weight was introduced to effectively improve the convergence speed of the algorithm.Random Cauchy mutation strategy was introduced to effectively improve the optimization ability of the algorithm.The test results of the five test functions show that compared with the salp optimization algorithm,the adaptive salp optimization algorithm,particle swarm optimization algorithm and whale optimization algorithm,the improved salp optimization algorithm has faster convergence speed and higher accuracy.The simulation experiment of the actual data of port yard operation shows that the improved salp optimization algorithm can optimize and obtain the satisfactory discharge scheduling task,reduce the total time of the train in port and improve the overall work efficiency of the port.