Paper Info
Title
Particle Swarm Optimization for Dynamic Sectoring Control During Peak Traffic Pattern
Abstract
Elevator traffic scheduling is crucial module within an elevator group control system. An excellent scheduling approach is dedicated to both maximizing the system's handling capacity and minimizing the passenger's waiting time, journey time and the system's energy consumption, especially in peak traffic pattern which usually includes up-peak, down-peak and lunch-time peak. To keep the load of elevator cars balanced in the system is one of good choices for any peak traffic. This paper proposed a novel PSO-based dynamic sectoring algorithm for elevator traffic in buildings. The service sectors corresponding to elevator cars are determined with their expected round-trip time. Our simulation results demonstrate that the proposed algorithm is an effective approach to elevator systems, which can improve the service quality of elevator system in buildings as we expect.
Year
DOI
Venue
2007
10.1007/978-3-540-74282-1_73
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES
Keywords
Field
DocType
particle swarm optimization,elevator traffic scheduling,dynamic sectoring,peak traffic pattern
Particle swarm optimization,Elevator system,Airfield traffic pattern,Service quality,Computer science,Scheduling (computing),Real-time computing,Multi-swarm optimization,Elevator,Artificial intelligence,Energy consumption,Machine learning
Conference
Volume
ISSN
Citations 
2
1865-0929
3
PageRank 
References 
Authors
0.40
7
3
Name
Order
Citations
PageRank
Zhonghua Li1344.59
Yunong Zhang22344162.43
Hong-Zhou Tan319633.88