基于遗传算法的电梯群控鲁棒优化模型

针对电梯群控调度过程中交通流不确定的问题,建立了鲁棒优化模型,利用遗传算法对所建模型进行求解.对于不确定线性优化问题,研究了不确定集的选择以及模型鲁棒对等式转化方法.仿真实验中,利用电梯群控虚拟仿真环境对鲁棒优化调度算法在不同交通流下进行了验证.以300人/15 min的混合交通流模式为例,鲁棒优化算法的平均候梯时间比静态分区算法降低12.77 s;平均乘梯时间比最小等待时间算法降低9.7 s;电梯启停次数比静态分区算法少8次.实验结果表明,鲁棒优化调度算法对不同交通模式具有更好的适应性,可以减小交通流不确定性的影响,提高电梯群控调度性能.

Robust Optimization Model of Elevator Group Scheduling Based on Genetic Algorithm

To investigate the problem of elevator group scheduling,a model was proposed based on robust optimization(RO)and solved by using genetic algorithm(GA).For uncertain linear optimization,the choice of uncertain set and transformation of robust counterpart were investigated.Virtual simulation environment of elevator group control system was adopted to validate the RO scheduling algorithm under different traffic flows.Numerical experiments showed that the average waiting time of RO was 12.77 s shorter than that of static zoning algorithm,the average journey time was 9.7 s shorter than that of minimum waiting time algorithm,and the elevator stop times was 8 times less than that of static zoning algorithm.The RO algorithm was more adaptable under different traffic modes.It can diminish the impact of traffic uncertainty and improve the performance of elevator group scheduling evidently.