遗传算法在企业铁路取送调车作业优化中的应用

如何合理安排企业铁路取送车作业是企业铁路调车作业中的一个重要环节.本文针对调车作业方式＂送取分离＂的企业铁路货运站,参照3车列的取送车仿真时序,建立了取送车作业优化问题的数学模型.同时,提出一种带启发式知识的进化增强型遗传算法用以求解这类大规模组合优化问题.算法一方面通过启发式知识产生优良个体并有效保存,加速算法寻优;另一方面通过有效的交叉和变异操作保证算法的多样性,避免算法早熟;从而最终有效提高算法的寻优效率.实例计算结果验证了模型和算法的有效性和工程实用性.

Application of Genetic Algorithm to Optimal Operation for Placing-in and Taking-out of Wagons at Enterprise Railway

How to rationally arrange operation for placing-in and taking-out wagons is one of important part of shunting operation at enterprise railway. Aiming at enterprise railway freight station with the separating operation mode of placing-in and taking-out of wagons, and referring to the time-sequence simulation of placing-in and taking-out of wagons operation of three train stocks, a mathematical model of optimal operation for placing-in and taking-out of wagons is built. And a heuristic evolution-enhanced genetic algorithm is presented to resolve the large-scale combinatorial optimization problem. On one hand, better individuals are generated by adopting heuristic knowledge and conserved effectively to accelerate optimization of the algorithm; on the other hand, variety of algorithm is guaranteed by the effective crossover and mutation operation to avoid premature convergence. Through them, the searching efficiency of the algorithm is greatly improved. The simulation results have verified the validity and practicability of the model and algorithm.