File Download
Supplementary
-
Citations:
- Appears in Collections:
postgraduate thesis: Improving the performance of lifts using artificial intelligence techniques
Title | Improving the performance of lifts using artificial intelligence techniques |
---|---|
Authors | |
Advisors | Advisor(s):Cheung, KC |
Issue Date | 2003 |
Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
Citation | Wong, K. [黃敬修]. (2003). Improving the performance of lifts using artificial intelligence techniques. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. |
Abstract | (Uncorrected OCR)
Abstract of thesis entitled
Improving the Performance of Lifts Using Artificial Intelligence Techniques
submitted by
Wong King Sau
for the degree of Doctor of Philosophy
at the University of Hong Kong
in August 2003
An elevator group control system manages multiple elevators to serve hall
calls in a building. Most elevator group control systems need to recognize the traffic
pattern of the building and then change their control algorithms to improve the
efficiency of the elevator system. However, the traffic flow in a building is very
difficult to be classified into distinct patterns. Traffic recognition systems can
recognize certain traffic patterns, but mixed traffic patterns are difficult to be
recognized.
The aim of this study was therefore to develop improved duplex elevator
group control systems that do not need to recognize the traffic pattern. A fuzzy logic.
control unit and genetic algorithms control unit were used. A fuzzy logic control unit
integrates with the conventional duplex elevator group control system to improve
performance especially in mixed traffic patterns with intermittent heavy traffic
demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building.
The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator.
Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system.
(An abstract of 297 words.)
~
Signed _
Wong King Sau |
Degree | Doctor of Philosophy |
Subject | Fuzzy logic. Genetic algorithms. Elevators, Automatic. |
Dept/Program | Mechanical Engineering |
Persistent Identifier | http://hdl.handle.net/10722/31202 |
HKU Library Item ID | b2768295 |
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Cheung, KC | - |
dc.contributor.author | Wong, King-sau | - |
dc.contributor.author | 黃敬修 | zh_HK |
dc.date.issued | 2003 | - |
dc.identifier.citation | Wong, K. [黃敬修]. (2003). Improving the performance of lifts using artificial intelligence techniques. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. | - |
dc.identifier.uri | http://hdl.handle.net/10722/31202 | - |
dc.description.abstract | (Uncorrected OCR) Abstract of thesis entitled Improving the Performance of Lifts Using Artificial Intelligence Techniques submitted by Wong King Sau for the degree of Doctor of Philosophy at the University of Hong Kong in August 2003 An elevator group control system manages multiple elevators to serve hall calls in a building. Most elevator group control systems need to recognize the traffic pattern of the building and then change their control algorithms to improve the efficiency of the elevator system. However, the traffic flow in a building is very difficult to be classified into distinct patterns. Traffic recognition systems can recognize certain traffic patterns, but mixed traffic patterns are difficult to be recognized. The aim of this study was therefore to develop improved duplex elevator group control systems that do not need to recognize the traffic pattern. A fuzzy logic. control unit and genetic algorithms control unit were used. A fuzzy logic control unit integrates with the conventional duplex elevator group control system to improve performance especially in mixed traffic patterns with intermittent heavy traffic demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building. The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator. Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system. (An abstract of 297 words.) ~ Signed _ Wong King Sau | - |
dc.language | eng | - |
dc.publisher | The University of Hong Kong (Pokfulam, Hong Kong) | - |
dc.relation.ispartof | HKU Theses Online (HKUTO) | - |
dc.rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. | - |
dc.source.uri | http://hub.hku.hk/bib/B2768295X | - |
dc.subject.lcsh | Fuzzy logic. | - |
dc.subject.lcsh | Genetic algorithms. | - |
dc.subject.lcsh | Elevators, Automatic. | - |
dc.title | Improving the performance of lifts using artificial intelligence techniques | - |
dc.type | PG_Thesis | - |
dc.identifier.hkul | b2768295 | - |
dc.description.thesisname | Doctor of Philosophy | - |
dc.description.thesislevel | Doctoral | - |
dc.description.thesisdiscipline | Mechanical Engineering | - |
dc.description.nature | abstract | - |
dc.description.nature | toc | - |
dc.identifier.mmsid | 991021776519703414 | - |