Dynamic planning of office building elevator scheduling

Abstract

Every morning rush hour is the most crowded time for elevators in office buildings, and many office workers will be late because they can't wait for the elevator. How to reasonably call the existing elevator resources has become a problem we need to pay attention to. In this paper, we will discuss the modeling of elevator scheduling optimization problem for office buildings. In this paper, the background and significance of the research is firstly explained, and then the elevator group linkage control system is briefly understood and analyzed, and the evaluation indexes are determined according to its multi-objective and diversified characteristics, and then the group control method focusing on making the elevator run time the shortest is adopted, and the principle of the ratio of elevator running period/total running time equal to the number of passengers transported/total passengers transported by the elevator in one cycle is followed to compare several common elevator running methods, and the results are summarized in the following table. Common elevator operation methods are compared, the most effective way to arrive at, and finally use dynamic programming to solve the partition point, to establish the optimal scheme of elevator scheduling for the office building division. Since it is not possible to expand the elevator in the office building, reducing the number of stops of each elevator to shorten the time required for the elevator to carry passengers becomes the most feasible method. After comparing the common elevator operation methods, it can be found that zoning can shorten the final average arrival time of passengers more than no zoning. That is, the elevators in an office building are divided into different zones, and each zone has a designated elevator serving a designated floor segment. In order to solve the floor segment allocation problem, the shortest path algorithm is used in this paper. The paper concludes with a summary and future outlook of the resulting optimization model.