Abstract:
An elevator system employing a micro-processor-based group controller (FIG. 2) communicating with the cars (3, 4) to assign cars to hall calls based on a Relative System Response (RSR) approach. However, rather than using unvarying bonuses and penalties, the assigned bonuses and penalties are varied using "artificial intellience" techniques based on combined historic and real time traffic predictions to predict the number of people behind a hall call, and, calculating and using the average boarding and de-boarding rates at "en route" stops, and the expected car load at the hall call floor. Prediction of the number of people waiting behind hall calls for a few minute intervals are made using traffic levels measured during the past few time intervals on that day as real time predictors, using a linear exponential smoothing model, and traffic levels measured during similar time intervals on previous similar days as historic traffic predictors, using a single exponential smoothing model. The remaining capacity in the car at the hall call floor is matched to the waiting queue using a hall call mismatch penalty. The car stop and hall stop penalties are varied based on the number of people behind the hall call and the variable dwell times at "en route" stops. The stopping of a heavily loaded car to pick up a few people is penalized using a car load penalty. These enhancements to RSR result in equitable distribution of car stops and car loads, thus improving handling capacity and reducing waiting and service times.
Abstract:
An elevator control system employing a micro-processor-based group controller (FIG. 2), which communicates with the cars (3, 4) of the system to determine the conditions of the cars, and responds to hall calls registered at a plurality of landings in the building serviced by the cars under control of the group controller, assigning hall calls to cars based on the summation for each car, relative to each call, a weighted summation of a plurality of system response factors, some indicative, and some not, of conditions of the car irrespective of the call being assigned, assigning varying "bonuses" and "penalties" to them in the weighted summation. "Artificial intelligence" techniques are used to predict traffic levels and any crowd build up at various floors to better assign one or more cars to the "crowd" predicted floors, either parking them there, if they were empty, or more appropriately assigning car(s) to the hall calls. Traffic levels at various floors are predicted by collecting passengers and car stop counts in real time and using real time and historic prediction for the traffic levels, with single exponential smoothing and/or linear exponential smoothing. Predicted passenger arrival counts are used to predict any crowd at fifteen second intervals at floors where significant traffic is predicted. Crowd prediction is then adjusted for any hall call stops made and the number of passengers picked up by the cars. The crowd dynamics are matched to car assignment, with one or more cars being sent to crowded floor(s).
Abstract:
The present invention discloses a method for controlling an elevator system. In the method an elevator is allocated for the use of a passenger in a first optimization phase in such a way that a first cost function is minimized, a second optimization phase is performed, in which the route of the allocated elevator is optimized in such a way that a second cost function is minimized.
Abstract:
An exemplary elevator input device includes a passenger interface configured to allow a passenger to place a call to indicate a desired elevator service. The elevator input device includes a controller configured to interpret any passenger input regarding desired elevator service. The controller identifies which of a plurality of elevator cars will be able to provide the desired elevator service according to a predetermined criterion. The plurality of elevator cars considered by the controller includes every elevator car that is capable of serving the call. The controller is also configured to assign the call to the identified elevator car.
Abstract:
Die Erfindung betrifft ein Verfahren zur Steuerung einer Aufzugsanlage (10) mit einer Doppel- bzw. Mehrfachaufzugskabine pro Aufzugsschacht (S0, S01''); wobei auf mindestens einem Rufeingabestockwerk mindestens ein Zielruf (T1) eingegeben wird bzw. mindestens ein Identifikationscode (T1') empfangen wird; welcher Zielruf (T1) bzw. Identifikationscode (T1') ein Ankunftsstockwerk bezeichnet; wobei für den Zielruf (T1) bzw. Identifikationscode (T1') mindestens eine Fahrt mit mindestens einer Aufzugskabine (1, 1', 1'') der Doppel- bzw. Mehrfachaufzugskabine von einem Abfahrtstockwerk auf ein Ankunftsstockwerk ermittelt wird; wobei vor Ermittlung einer Fahrt überprüft wird, ob mindestens ein situationsspezifischer Parameter (T2) erfüllt ist; und falls ein situationsspezifischer Parameter (T2) erfüllt ist, mindestens eine situationsgerechte Rufzuteilung (T6) für eine Fahrt mit einer Stockwerkdifferenz von Null zwischen dem Rufeingabestockwerk und dem Abfahrtstockwerk bzw. mit einer Stockwerkdifferenz von Null zwischen dem Zielstockwerk und dem Ankunftsstockwerk ermittelt wird.
Abstract:
One version of this disclosure includes a system for assigning an elevator car to respond to a call signal wherein a controller is responsible for determining which elevator car will respond to a call signal. This version includes the controller receiving a hall call signal, receiving information regarding the elevator system, determining whether the call assignment can be made in view of a first rule associated with a banned call assignment, and eliminating the rule against banned call assignments when necessary to avoid saturation of the elevator system.
Abstract:
Elevator group control method for the allocation of landing calls, in which method a target value is assigned to a given service time of the elevator group and landing calls are so allocated to elevators that the assigned target value of the service time is realized on the average, the energy consumption of the elevator group being thereby reduced.
Abstract:
A rule base in which control rule sets are stored is created. By applying a rule set in the rule base to the current traffic, the elevator cars are operated, and the behavior of each car is simulated in real time by scan assignment till reverse. Thus, a group control performance when the rule set is applied is predicted. According to the results of the performance prediction, an optimum rule set is selected. In such a way, real time simulation is made during group control, and accordingly group control of elevator cars is effected by applying an optimum rule set at all times, thus providing favorable service.