JPH0476913B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Application of the Invention] The present invention relates to an elevator group management control device.

[Prior art]

Recently, microcomputers have been used in devices that efficiently manage multiple elevators. Therefore, it has become possible to monitor the service status of individual hall calls in real time and manage elevator groups in consideration of the service status of the entire building.

For example, in order to select and assign the most suitable elevator to a hall call that has occurred, an evaluation function that takes into account the service status of the entire building is calculated, and the call that is assigned to the hall call that has occurred is assigned to the elevator with the minimum (or maximum) evaluation function value. This has made possible an allocation method and has made a major contribution to improving performance.

Incidentally, as the evaluation function, in general, in order to improve passenger performance, a function in which predicted waiting time is used as an evaluation value is often used. For example, (1) A method in which the estimated arrival waiting time required to arrive at the floor where a hall call occurs is used as the evaluation value.

(2) A method in which the evaluation value is the maximum value of the predicted arrival times of already allocated hall calls on the floor ahead of the floor where the hall call occurs.

(3) A method in which the evaluation value is the sum of the squares of the predicted arrival waiting times of already allocated hall calls on the floor ahead of the floor where the hall call occurs.

etc. can be mentioned.

On the other hand, due to social demands, energy-saving operation is strongly desired for elevators, and in addition to the above evaluation values, evaluation values that conflict with the above evaluation values are set.
A method has been devised in which the above two evaluation values are relatively weighted to obtain an assigned evaluation value. (For example, Japanese Patent Application No. 56-109216) In the present application, the service performance index for passengers such as the predicted arrival waiting time is allocated and controlled using a first evaluation value and a second evaluation value related to power consumption such as energy saving. This will be explained using an example.

An example of the above-mentioned second evaluation value is to make it easier to allocate a hall call that has occurred to an elevator that has a large number of car calls, to concentrate the load on that elevator, and to increase the number of times the entire elevator is started, the running time, and mechanical wear. There is a function that attempts to obtain an energy saving effect by reducing .

Based on the first evaluation value and the second evaluation value,
For example, the following formula is used to obtain the hall call assignment evaluation value.

φ=Tw−aP (1) Min{φ ₁ , φ ₂ , ..., φ _k } (2) Here, φ: Allocation evaluation value Tw: 1st evaluation value P: 2nd evaluation value α: Evaluation value Weighting coefficient K: Number of elevators, that is, assigned evaluation value φ for each elevator
is calculated, and the hall call that occurs is assigned to the elevator with the lowest assignment evaluation value.

In addition, in equation (1), when the evaluation value weighting coefficient α is set to zero, the first evaluation value Tw is emphasized, and as α becomes larger, the element of the second evaluation value P becomes larger, and the power While consumption will be reduced, service to passengers will be reduced.

These relationships are shown in FIG. The horizontal axis shows the operation control parameter (for example, evaluation value weighting coefficient α), and the vertical axis shows the average waiting time f ₁ representing the first evaluation value and the power consumption f _p corresponding to the second evaluation value. .
When the operation control parameter α is set to a large value, the load is concentrated on a specific elevator, and the power saving curve f _p decreases. Similarly, the first evaluation value such as the average passenger waiting time f _t increases as the control parameter α increases, and the waiting time becomes longer as shown by the average waiting time curve f _T .

As described above, serviceability and energy saving are in a contradictory relationship, and various proposals have been made to efficiently control them simultaneously, but no satisfactory results have always been obtained.

[Purpose of the invention]

An object of the present invention is to select a service elevator for a hall call using a plurality of relatively weighted evaluation factors, and to improve the target function while maintaining a desired waiting time. To provide elevator group management control equipment.

[Summary of the invention]

A feature of the present invention is that an elevator is assigned to a hall call based on a comprehensive evaluation of a first evaluation value of each elevator including the waiting time for the hall call and a second evaluation value of other factors such as power saving. The second evaluation value is evaluated within a range that does not exceed a predetermined allowable value.

In other words, if a hall call is assigned to an elevator based on the comprehensive evaluation of the schedule, if the waiting time exceeds the allowable value, the second evaluation value including the above power saving is limited, and the power saving is set to the target value. They try to keep the waiting time within the expected value even if it falls below the expected value.

With this configuration, power consumption, which is a control objective that is contrary to waiting time, can also be reduced as much as possible within the range that satisfies the planned conditions for "waiting time," which is the most important control objective. Control objectives other than waiting time can also be achieved within the allowable range of waiting time.

The present invention may be executed all the time, but according to a preferred embodiment of the present invention, it is applied when setting the allocation algorithm and parameters by simulation using input of the group management control target. .

[Embodiments of the invention]

Embodiments of the present invention will be described below with reference to FIGS. 2 to 17. In the description of the embodiment, first, the hardware configuration for realizing the present invention will be described, then the overall software and its control concept will be described, and finally the explanation will be given using a flowchart for realizing the control concept.

FIG. 2 shows the overall hardware configuration of one embodiment of the present invention.

The elevator group management control device MA includes a microcomputer M ₁ that controls the elevator operation control described above and a microcomputer M ₂ that controls the simulation rayon described above.
A serial communication processor SDA _c is used between microcontrollers _M1 and _M2 .
Data communication is performed via the communication line _CMc .

The detailed structure and operation of SDA are disclosed in Japanese Patent Laid-Open No. 56-37972 and No. 56-37973.

The microcomputer _M1 that controls elevator operation has
The call signal HC from the hall call device HD is connected via the parallel input/output circuit PIA, and the machine control microcontrollers E ₁ to _{E o} ( Here, it is assumed that there is an n-th elevator) which is connected to the same serial communication processors SDA ₁ to SDA _o as described above via communication lines CM ₁ to _{CM o} .

On the other hand, the microcomputer _M2 has a parallel input/output circuit that includes signals PM, T _LMT , and X LMT from the setting device PD that provide the information necessary for determining the optimal driving control parameters for the simulation, and a device _SX that displays each traffic condition.
Input via PIA.

In addition, the machine control microcontrollers E ₁ to _{E o} have car call information necessary for control, various elevator safety limit switches, relays, and control input/output elements EIO ₁ to EIO _o , which are comprised of response lamps, and parallel input/output circuits. Connected to PIA via signal lines SIO ₁ to SIO _o .

Figure 3 shows the overall configuration of the software, which can be roughly divided into operation control system software.
It consists of SF1 and simulation software SF2. The former is processed by microcomputer _M1 in FIG. 1, and the latter by microcomputer _M2 .

The operation control system software SF1 includes an operation control program SF14 that directly commands and controls elevator group management control such as call assignment processing and elevator distributed standby processing. As input information for this program, elevator control data table SF11 including elevator position, direction, car call, etc., which is transmitted from the machine control program (built into microcomputers E ₁ to E _o in Figure 2), hall call table SF12, Calculate the number of managed elevators using the elevator specification table SF13 and the simulation software SF2,
The output optimal operation control parameters, etc. are used as input data.

On the other hand, simulation software SF2
consists of the following processing programs.

(1) Data collection program SF20...A program that samples the contents of hall call and elevator control data tables online at regular intervals and collects data for simulation. ) are mainly collected.

(2) Simulation data calculation program
SF22...This is a program that calculates simulation data by taking into account the contents of the online sampling data table collected by the data collection program and the contents of the table for past time periods.

(3) Various curve calculation programs using simulation SF23...Simulation data table SF24 and elevator specification table SF
25 is input, simulation is performed for each of a plurality of predetermined parameters, and various curve data tables SF26 are calculated and output. Examples of the various curve data tables SF26 include a waiting time curve table, a power consumption curve table, and the like.

(4) Calculation program for optimal operation control parameters
SF27...Target value table SF28 set from the above various curve tables SF26 and setting device PD
is input, and the optimal operation control parameter SF29 adapted to the environmental conditions of the building is calculated and output.

In addition, the optimal operation control parameter SF29 includes:
Simulation data table GF24 calculated by the simulation data calculation program
A part of is also added. This can be said to be a learning function because the simulation software SF2 evaluates the actual operation results and controls the elevator based on the results.

Here, the second evaluation value P of the equation (1) will be briefly explained. This P is expressed as P=ΣβS.

β is a weighting coefficient, for example, from 0 to 20, for the generated hall call and the stop call (referring to the call to be serviced) on the adjacent floor. In addition, S indicates the outage probability, and if there is a call to be serviced, it will be 1.0, and if there is a predicted call, it will be an appropriate value found from the prediction (0S
1). FIG. 4 shows values that ignore predicted calls.

By using the evaluation function of formula (1), it is possible to concentrate adjacent stop calls of generated hall calls to one machine, reduce the overall number of starts and stops, and save energy.
Dangerous driving is prevented.

The second evaluation value P in the example of Fig. 4 takes into account the two floors before and after the occurrence call floor i, and is calculated as follows: P = ΣβS = 5 x 1.0 + 10 x 0 + 20 x 1.0 + 10 x 1.0 + 5 x 0 = 35 (seconds). Therefore, assuming that the first evaluation value T is the same for each elevator, the elevator with the larger P will be determined to be optimal, and the generated hall call will be assigned to that elevator.

Now, in equation (1), focusing on the weighting coefficient α between the first evaluation value T and the second evaluation value P, this α
There exists a value that is most effective in preventing driver-driving, and at that time the waiting time (average waiting time) for the entire building can be minimized.

On the other hand, when α is increased, elevators with many stop calls are selected preferentially, so the load is concentrated on a certain elevator and the average waiting time increases. Conversely, as mentioned above, since the load on other elevators becomes lighter, the number of times the elevator stops (starts) as a whole decreases, and the power consumption decreases.

Next, a table configuration used in an embodiment of the present invention will be explained with reference to FIGS. 5 and 6. FIG. 5 shows the table configuration of the operation control system software, which is roughly divided into blocks including an elevator control table SF11, a hall call table SF12, and an elevator specification table SF13. The tables within each block will be described each time the operation control program is explained below.

Figure 6 shows the table configuration of the simulation software, and shows the optimal operation control parameter SF2.
9. Various curve data table SF26, target value table SF28, sampling data table SF
21.Simulation data table SF2
4 and an elevator specification table SF25 (not shown because it is similar to FIG. 12).

Next, an embodiment of the software of the present invention will be described.

First, the operation control system program will be explained, and then the simulation system program will be explained. Note that the programs described below are managed under a system program that divides programs into multiple tasks and performs efficient control, that is, an operating system (OS). Therefore, programs can be started freely from the system timer or from other programs.

Now, FIGS. 7 to 10 show the flow of the operation control program. Two particularly important operation control programs, the elevator arrival prediction time table calculation program and the call assignment program, will be explained.

FIG. 7 is a flowchart of a program that calculates the predicted arrival time of an elevator to an arbitrary floor, which is the basic data for calculating the waiting time evaluation value. This program is activated periodically, for example, every second, and calculates the predicted arrival time from the current position of the elevator to an arbitrary floor for all floors and for all elevators.

In FIG. 7, steps E10 and E90 indicate loop processing for all elevator numbers. In step E20, an initial value is first set in the work time table T, and its contents are set in the predicted arrival time table shown in FIG. As an initial value, it is possible to consider how many seconds it will take to depart based on the open/closed state of the door, or the predetermined time until activation when the elevator is stopped.

Next, the program advances one floor (step E30) and compares whether the floor is the same as the elevator position (step E40). If they are the same,
Since the predicted arrival time table for one elevator has been calculated, the process jumps to step E90 and repeats the same process for the other elevators. On the other hand, in step E40, “No”
If so, the first floor running time Tr is added to the time table T.
is added (step E50). This time table T is then set as a predicted arrival time table (step E60). Next, it is determined whether there is a car call or an assigned hall call, that is, a call that should be serviced by the elevator of interest, and if so, one stop time Ts is added to the time table in order to stop the elevator (step E80). ). Next, the process jumps to step E30, and the above process is repeated for all floors.

The first floor running time Tr and one stop time Ts in step E50 and step E80 are given as one of the optimum operation control parameters by the simulation software.

Figure 8 shows the flow of the call allocation program.
This program is activated when a hall call occurs.
In this program, there are two call allocation algorithms; one is a long-waiting call minimization call allocation algorithm (described later in FIG. 9) as shown in step A60, and the other is a call allocation algorithm as shown in step A70. This is a minimum predicted arrival time call allocation algorithm (described later in FIG. 10). Selection of these algorithms is switched by the algorithm selection parameter As in the optimum operation control parameter SF29 shown in FIG.

Returning to FIG. 8, first, in step A10, the generated hall call is read from outside. Then, the following processing is performed in a loop at steps A20 and A100, and steps A30 and A90. That is, if there is a generated hall call, calculation is performed using one of the call allocation algorithms, and this call is allocated to the selected optimal elevator (step A80).

FIG. 9 is a processing flow of the long-waiting call minimization call allocation algorithm. Steps A60-1 and A are used to determine which elevator is optimal.
60-6 performs loop processing based on the number of elevators. The processing within the loop begins with step A60-2.
Then, the maximum predicted waiting time (first evaluation value Tw) of the allocated hall call on the front floor including the generated hall call is calculated. Note that the predicted waiting time is the sum of the hall call elapsed time (see FIG. 5), which indicates the elapsed time from the occurrence of the hall call to the present, and the predicted arrival time (see FIG. 5). Next step A60
-3, the second evaluation value P is calculated from the stop calls on the predetermined floors before and after the generated hall call, as described above in FIG.
Tw is used to calculate the evaluation function φ of equation (1) (step A60-4). Then, select the smallest elevator in this evaluation function φ (step A60-
5). If the above process is executed for all elevators, the elevator with the optimal evaluation value will be selected by the calculation in step A60-5.

As another call allocation algorithm, FIG. 10 shows the flow of the minimum predicted arrival time call allocation algorithm. The flow in FIG. 1 is almost the same as that in FIG. 9, except for the processing at step A70-2. In this algorithm, in order to select the elevator with the minimum evaluation value of the predicted arrival time up to the generated hall call, the predicted arrival time Ti of the generated hall call floor i is loaded from the table shown in FIG.

The above elevator running time t _r and number of stops t _s are calculated by
By dividing the travel time by the number of floors traveled, the travel time for the first floor can be calculated, and from the number of times the elevator stops and the time the elevator is open (stop time), the standard stop time for one time can be calculated. When the sampling time ends, the collected data is subjected to the above-mentioned calculation and stored in the online measurement table and time slot table of the sampling data table SF21 in FIG. 6, respectively. Note that the online measurement data table has item names such as C _opw , t _ropw , and t _sopw .
Add the new subscript, C _pld to the time zone table,
They are written with the subscript "old" added, such as t _rpld and t _spld .

FIG. 11 shows the flow of a simulation data calculation program, which is activated periodically (for example, every 10 minutes). The simulation data is calculated by predicting online measured data and past data by adding an appropriate coupling variable γ. For example, the destination traffic volume is calculated as C _prp = γC _opw + (1 - γ) C _pld (3) as shown in step SB20. Therefore, the weight of destination traffic volume data that is almost online measured and has a large coupling variable γ becomes large. Note that the subscript "pre" is added to the predicted data.

Similarly to the above, predicted data t _rpre and t _spre for the first floor running time and one stop time are also calculated (step SB30). Moreover, the data of t _rper and t _spre are the optimum operation control parameters SF29T _{r and} t spre shown in Fig. 6.
T _s table is set (step SB4)
0).

Then, in order to execute a simulation based on the predicted data calculated by this program, various curve calculation programs (tasks) using the simulation shown in Fig. 12 are started (step SB5).
0).

Figure 12 shows the flow of various curve calculation programs using simulation.
It is started from step SB50 in FIG.

The simulation parameters include an algorithm parameter As for selecting a call allocation algorithm, and a control parameter α which is a weighting coefficient as described above in equation (1), and the simulation is executed for each parameter case.

First, simulation data such as destination traffic volume is set (step SC10), and algorithm parameters are set (step SC3).
0). The algorithm parameters are As and As
When =1, the call allocation algorithm that minimizes long waiting calls is selected, and when As=2, the call allocation algorithm that minimizes the predicted arrival time is selected. Next, in step SC30, the control parameter α
and executes the simulation (step SC40). Note that the control parameter α is, for example, 0, 1, 2, 3, 4, as shown in FIG.
There are 5 cases.

The simulation results for each case are stored for each parameter (step
SC60).

Although the simulation results are stored in two types: average waiting time and power consumption, other evaluation items may be stored to create a curve table.

This program has three algorithms for optimal operation control: one is an algorithm that sets a target value for energy saving (step SC90), and one is an algorithm that sets a predetermined value for the average waiting time (step SC90).
SC100), one is an algorithm that provides a long waiting rate of 1 minute or more (step SC110), and the selection of these algorithms is determined by the algorithm selection parameter A _S2 in the optimal operation control parameter SF29 shown in FIG. can be switched.

After completing the simulation for all the above cases, the optimum operation control parameter calculation program (task) shown in Fig. 14 is started (step
SC120), this program ends.

A specific flow of the simulation execution program at step SC40 is shown in FIG. The simulation program consists of operation programs for the elevators themselves, such as running operations, door opening/closing operation programs, etc., and management function programs to efficiently manage these elevators, such as call assignment functions, elevator distributed standby function programs, etc. It is broadly divided into Whether simulation results can be obtained with high accuracy depends on the configuration of this simulation program, and it is desirable to configure the program to be as equivalent to an elevator system as possible.

Now, in Fig. 13, first, initial values for simulation are set (step SC40).
-1), and thereafter, loop processing is performed for a predetermined simulation time (e.g., one hour) (steps SC-40-2 to SC40-15). Next, passenger generation processing is performed (step 40-2). This passenger generation is calculated based on the predicted destination traffic volume C _pre data shown in FIG. When a passenger is generated by the above passenger generation process, steps SB40-3~
SC40-5 detects the generated hall call,
Call assignment processing is performed. This call assignment process is performed by a program similar to the call assignment program in the operation control program described above with reference to FIG.

When the call assignment process is completed, the process shifts to a simulation of car operation. First, the elevator running process is performed (step SC40-
6), it is determined whether the elevator has reached the stop position, and if the elevator is at the stop position, steps 40-8 to SC40-13 are executed.

If the elevator is at the stop position, it is determined whether there is a service call such as a car call or an assigned hall call (step SC40-8), and if that is the case, the service call is reset and the passenger boarding/alighting process is executed (step SC40-8). SC40-9). Then, in order to evaluate the simulation results, the number of elevator stops is collected (this data is collected because the number of stops is approximately proportional to power consumption), and the waiting time is collected (step SC4).
0-10, SC40-11). Next, door opening/closing processing (step SC40-12) is performed, and the processing for each elevator is completed. In addition, step SC40
-8, if there is no service call, elevator distributed standby processing is performed (step SC
40-13).

When the above processing is performed for a predetermined simulation time, the average waiting time and power consumption, which are evaluation data of the simulation results, are calculated in step 40-16.
The program is then completed.

Figure 14 shows the flow of the calculation program for the energy saving setting algorithm of the optimal operation control algorithm.
This program is executed at step SC120 in Figure 12.
It is activated by

This program uses the waiting time curve data calculated in Figure 1 and the power consumption curve (or long waiting rate).
It learns and calculates the optimal operation control parameters for elevator group management operation using the data and the energy-saving target value input from the setting device PD.

First, input the energy saving target value P _M and display it on a display device that can be installed in the building's central monitoring panel or electrical machinery room.
Displayed on SX (Figure 16). (Step SD10,
20)). Then, based on the contents of the curve data table SF26 obtained by simulation,
By applying a predetermined interpolation method, a waiting time curve f _T and a power consumption curve f _P as shown in FIG. 15 are calculated. Here, the predetermined interpolation method is, for example, surrounding data 3
This refers to a well-known method of approximating a quadratic curve by a number of individuals (SD30).

Since the curves f _T and f _P have been calculated in the above process, using this curve f _T , as shown in FIG. 15, the operation control parameter α ₀ at the minimum point and the minimum waiting time f _T (α ₀ ) are calculated.
is calculated (step SD40).

Next, in step SD10, it is determined whether or not the input energy saving target value P _M is 0. If it is 0, the process jumps to step SD80, and this α ₀ becomes a candidate for the optimum operation control parameter α. On the other hand, if the energy saving target value P _M is not 0, using the power consumption curve f _P , operation is performed such that f _P (α ₂ )=f _P (α ₀ )×(1−P _M ) ……(4) A control parameter α ₂ is calculated (step SD60). This α ₂ provides an operation control parameter that satisfies the energy saving target value P _M , for example, 10%. Next, other algorithms (A _s
If A s = 1, then A _s = 2) is calculated (step SD70), and parameters suitable for each traffic situation are set. Display device SX with step SD80
Then, the power consumption f _p (α ₂ ), the average waiting time f _T (α ₂ ), and the parameter α ₂ are displayed, and this algorithm ends.

Next, using FIG. 17, calculate SC10 in FIG. 12.
An algorithm for setting a predetermined value of the average waiting time of 0 will be described. (See FIG. 18) First, a predetermined value T _LMT for limiting the upper limit of the average waiting time is inputted from the setting device PD (FIG. 2) and displayed on the display device SX. (Step SE-10, 2
0), then calculate the average waiting time curve f _T and the power saving curve f _p as shown in FIG. 18, and calculate the parameter α _LMT such that T _LMT =f _T (α _LMT ) (5). (Step SE
30, 40) In step SE-50, the control parameter α ₀ that minimizes the average waiting time is calculated.

At step SE-60, the minimum waiting time value f _T (α ₀ ) is compared with a predetermined waiting time value T _LMT . If f _T (α ₀ ) is large, the parameter α ₀ that minimizes the average waiting time is used as the control parameter α in step SE-70. If the predetermined value T _LMT is large, α _LMT determined in step SE-40 is used as the control parameter (step SE-80).

Next, the above parameters _{α 0 ,}
Given α _LMT , the energy saving value P _n P _n =f _p (α _LMT )/f _p (α ₀ )×100(%) (6) is calculated. (Step SE-90) Calculate all algorithms (Step SE-100)
Then, the energy saving value P _n and the power consumption f _p are displayed on the display device SX.
(α _LMT ), the control parameter α is displayed, and the process ends.

The algorithm for setting the predetermined value (L _LMT ) for the long waiting rate of 1 minute or more in step SC110 in FIG. 12 is the same as the algorithm for determining the predetermined value ( _TLMT ) for the average waiting time in step SC100, so a description thereof will be omitted.

One embodiment of the present invention has been described above, but below:
The effects of one embodiment of the present invention will be described.

First, not only the energy saving target value but also the average waiting time, 1
Since the control parameters are limited by setting a predetermined value for the long waiting rate of 1 minute or more, the accuracy of energy saving control that prevents long waiting times for passengers is improved.

Next, by displaying each traffic situation, services can be improved, especially for building managers.

Furthermore, since it has not only a call allocation algorithm but also a plurality of optimal operation control algorithms, it is possible to further improve performance in terms of average waiting time and long waiting rate.

Next, other embodiments of the present invention will be described.
As the first evaluation value, although the average waiting time was mainly used in the explanation, the service completion time (until the passenger presses the hall call and gets off at the destination floor) may also be used. Moreover, as the second evaluation value, although the power consumption curve has been mainly used in the explanation, an elevator running curve, a machine wear curve, or the like may be used.

As for the display device, not only a digital display as shown in FIG. 16 but also an analog type may be used.
Displaying it in the elevator hall will improve service to passengers.

Further, although predetermined values are provided for the control parameters, they can be varied depending on each traffic situation, and as described above, not only program control but also an external setting device or clock means may be used.

Furthermore, it can also be used as a so-called port-type elevator in which a destination call is provided at the landing.

〔Effect of the invention〕

As described above, according to the group management control of the present invention, it is possible to fully demonstrate the target function without impairing the desired waiting time.

[Brief explanation of drawings]

The figure shows an embodiment for explaining the elevator group management and control device according to the present invention, in which FIG. 1 shows the relationship between average waiting time and power consumption, and FIG. 2 shows the overall configuration of the group management and control device. 3 is a diagram for explaining the overall configuration of the software, FIG. 4 is a diagram for explaining the evaluation function, FIGS. 15 and 18 are diagrams for explaining the relationship between parameters, waiting time, and power consumption curves, Figure 5 is a table configuration diagram of the group management operation control system, Figure 6 is a table configuration diagram of the simulator system,
Figure 7 is a flowchart for calculating the predicted arrival waiting time table, Figure 8 is a flowchart for calculating call allocation, Figure 9 is a flowchart for calculating call allocation to minimize long waiting times, and Figure 10 is the minimum predicted arrival waiting time. Flowchart for call assignment calculation, Figure 11 is a flowchart for data calculation for simulation, Part 1
Figure 2 is a flowchart for creating various curves by simulation, Figure 13 is a flowchart for executing simulation, Figure 14 is a flowchart for energy saving settings for optimal operation control, Figure 15 is an explanatory diagram of the operation of the embodiment, and Figure 16 is a flowchart for creating various curves by simulation. A diagram showing an example of a display device,
FIG. 17 is a flowchart when the average waiting time is set, and FIG. 18 is an explanatory diagram of another operation of the embodiment. MA...Elevator group management control device, HC...Hall call signal, M ₁ ...Microcomputer for elevator group management operation control, M2 _... Microcomputer for simulation, SDA...Processor dedicated to serial communication between microcomputers, _E1 to E _o ...No. machine control microcontroller, P _M ...target setter output signal, SX...display device.

Claims (1)

[Scope of Claims] 1. A plurality of elevators operating on multiple floors, a hall call means provided on the floor, and a first evaluation value and waiting time of each elevator including the waiting time for the hall call. A system comprising means for calculating a second evaluation value consisting of a control target other than time, and means for selecting a service elevator for a hall call using both of the above evaluation values, wherein the waiting time satisfies the scheduled conditions. An elevator group management control device comprising means for evaluating the second evaluation value within a range. 2. In claim 1, the means for evaluating the second evaluation value is an elevator provided with means for limiting the second evaluation value so that the waiting time satisfies a predetermined condition. Group management control device. 3. The elevator group management control device according to claim 2, wherein the means for imposing a limit on the second evaluation value includes means for lowering the weighting of the second evaluation value. 4. An elevator group management control device according to claim 1, wherein the control target other than the waiting time includes an element of power consumption.