基于遗传算法的多目标电梯紧急疏散问题

Multi-Objective Elevator Evacuation Problem Based on Genetic Algorithm

(超)高层建筑高度的增加,使得人们仅通过楼梯间疏散的时间显著增加.电梯技术的提高使得在发生突发事件时使用电梯辅助疏散技术成为可能,这样可以极大提高建筑中人员疏散效率和安全性.仅考虑疏散时间最短的单电梯紧急疏散调度问题(Single Elevator Scheduling for Emergency Evacuation,S-ESEE)已经被证明是NP难问题,但模型中未考虑电梯数量的限制.本文提出一种最小化疏散时间和往返次数的多目标模型,并采用遗传算法计算避免陷入局部最优解,并且为节省运算时间将人群数量、电梯停靠损失等固定值单独计算,通过增加电梯停靠约束降低算法时间复杂度.通过数值分析结果表明:在楼层数较少时,两种算法差别不大;但随着疏散楼层数量的增加,本文算法可以获得更优解.

With the height of(super)high-rise buildings increasing,the crowd evacuation time in the building only through the stairwell will be incresed significantly.With the improvement of elevator safety technology,elevator-assisted evacuation technology in emergency is able to greatly raise the efficiency and safety for human.The Single Elevator Scheduling for Emergency Evacuation(S-ESEE)that considers the shortest evacuation time has been proved to be an NPhard problem,but the limit on the number of elevators is not considered in the model.This study proposes a multiobjective model that minimizes the evacuation time and the number of round trips.The genetic algorithm is used to solve the model to avoid falling into the local optimal solution.To save the calculation time,some fixed values such as the number of people and the loss of elevator stop are calculated separately,and the time complexity of the algorithm is reduced by increasing the elevator stopping constraints.Numerical analysis results show that the two algorithms have little difference when the number of floors is small.However,as the number of evacuation floors increases,the algorithm in this study can obtain a better solution.