Research On Elevator Group Control Scheduling Algorithm Based On Multi-objective Optimization

With the rapid development of science and technology and social economy,the number of high-rise buildings is increasing,and the demand for elevators is also increasing greatly.In recent years,in order to continuously improve the performance of elevator group control system,the optimal scheduling problem has been paid more and more attention.Aiming at the problems of passengers waiting time and riding time for the elevator for too long and elevator energy consumption performance is not ideal,this dissertation selects several performance evaluation indexes to establish a multi-objective optimization model of elevator group control system,and studies the optimal scheduling of group control system by particle swarm optimization algorithm and particle swarm-simulated annealing algorithm.The main research contents are as follows:Firstly,a passenger flow simulation model is established to obtain information such as the arrival time,starting floor,and target floor of each passenger,which is essential for studying elevator group control scheduling and conducting simulations;Select the average waiting time,average riding time,and elevator energy loss as the performance evaluation indicators of the group control system,and establish a multi-objective evaluation function for the elevator group control system using a linear weighting method for three evaluation indicators,which is the basis for dispatching elevators in the group control system.Then,use the particle swarm optimization algorithm to optimize the scheduling of the elevator group control system,and design simulation experiments.Under different traffic modes,compare the average waiting time and average riding time of passengers,and the number of starts and stops of the elevator with the scheduling without using the particle swarm optimization algorithm,that is,the minimum waiting time is the scheduling principle.The experimental results show that the particle swarm optimization can reduce the number of elevator stops and passengers’ waiting time and riding time to a certain extent.Finally,in order to overcome the defect that particle swarm optimization is not easy to jump out of local optimum,simulated annealing algorithm is introduced to form particle swarmsimulated annealing fusion algorithm.The particle swarm-simulated annealing algorithm is applied to optimize the scheduling of elevator group control systems,and simulation experiments are designed,which are compared with the particle swarm algorithm in different passenger flow modes.The simulation experiments show that the control effect of this algorithm on elevator group control systems is better than that of the particle swarm algorithm.In summary,the algorithm studied in this dissertation has shown good performance in reducing the average waiting time and average riding time of passengers,and the number of elevator stops,and can optimize the scheduling of elevator group control systems to a certain extent.