Abstract
The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two cages are connected with each other, are expected to be the next generation elevator systems. Meanwhile, Destination Floor Guidance Systems (DFGS) are also expected in Double-Deck Elevator Systems (DDES). With these, the passengers could be served at two consecutive floors and could input their destinations at elevator halls instead of conventional systems without DFGS. Such systems become more complex than the traditional systems and require new control methods Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some previous papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator because it performs crossover and mutation operations to judgment nodes and processing nodes. In studies so far, the passenger's arrival has been assumed to take Exponential distribution for many years. In this paper, we have applied Erlang distribution and Binomial distribution in order to study how the passenger's arrival distribution affects EGSCS. We have found that the passenger's arrival distribution has great influence on EGSCS. It has been also clarified that GNP makes good performances under different conditions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference |
| Pages | 1476-1483 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London Duration: 2007 Jul 7 → 2007 Jul 11 |
Other
| Other | 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 |
|---|---|
| City | London |
| Period | 07/7/7 → 07/7/11 |
Fingerprint
Keywords
- Elevator group supervisory control system
- Erlang distribution
- Genetic network programming
- Passengers' arrival
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Theoretical Computer Science
Cite this
Effects of passenger's arrival distribution to double-deck elevator group supervisory control systems using genetic network programming. / Yu, Lu; Zhou, Jin; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki; Markon, Sandor.
Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. 2007. p. 1476-1483.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Effects of passenger's arrival distribution to double-deck elevator group supervisory control systems using genetic network programming
AU - Yu, Lu
AU - Zhou, Jin
AU - Mabu, Shingo
AU - Hirasawa, Kotaro
AU - Furuzuki, Takayuki
AU - Markon, Sandor
PY - 2007
Y1 - 2007
N2 - The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two cages are connected with each other, are expected to be the next generation elevator systems. Meanwhile, Destination Floor Guidance Systems (DFGS) are also expected in Double-Deck Elevator Systems (DDES). With these, the passengers could be served at two consecutive floors and could input their destinations at elevator halls instead of conventional systems without DFGS. Such systems become more complex than the traditional systems and require new control methods Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some previous papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator because it performs crossover and mutation operations to judgment nodes and processing nodes. In studies so far, the passenger's arrival has been assumed to take Exponential distribution for many years. In this paper, we have applied Erlang distribution and Binomial distribution in order to study how the passenger's arrival distribution affects EGSCS. We have found that the passenger's arrival distribution has great influence on EGSCS. It has been also clarified that GNP makes good performances under different conditions.
AB - The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two cages are connected with each other, are expected to be the next generation elevator systems. Meanwhile, Destination Floor Guidance Systems (DFGS) are also expected in Double-Deck Elevator Systems (DDES). With these, the passengers could be served at two consecutive floors and could input their destinations at elevator halls instead of conventional systems without DFGS. Such systems become more complex than the traditional systems and require new control methods Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some previous papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator because it performs crossover and mutation operations to judgment nodes and processing nodes. In studies so far, the passenger's arrival has been assumed to take Exponential distribution for many years. In this paper, we have applied Erlang distribution and Binomial distribution in order to study how the passenger's arrival distribution affects EGSCS. We have found that the passenger's arrival distribution has great influence on EGSCS. It has been also clarified that GNP makes good performances under different conditions.
KW - Elevator group supervisory control system
KW - Erlang distribution
KW - Genetic network programming
KW - Passengers' arrival
UR - http://www.scopus.com/inward/record.url?scp=34548063208&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548063208&partnerID=8YFLogxK
U2 - 10.1145/1276958.1277227
DO - 10.1145/1276958.1277227
M3 - Conference contribution
AN - SCOPUS:34548063208
SN - 1595936971
SN - 9781595936974
SP - 1476
EP - 1483
BT - Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
ER -