JPH06348769A - Device or method for deciding machine configuration/ machine arrangement - Google Patents

Abstract

PURPOSE:To simply and easily decide the configuration and arrangement of machines used inside a plant. CONSTITUTION:When present system configuration, present problem point, typical process and restraint conditions are inputted, an improved system plan retrieval part 52 performs the fuzzy retrieval of an improving plan DB 46 by using a machine kind dictionary 41, problem kind dictionary 42, process kind dictionary 43 and machine DB 48. Concerning respective machines described in the improved contents of the remaining improving plan, a similar system plan retrieval part 54 prepares newly a improving plan by adding machines having a high degree of affinity and not presented as machine configuration as the machine configuration by using a system DB 47 as well. Concerning the improving plan outputted from the similar system plan retrieval part 54, a system plan evaluation/comparison part 56 performs fuzzy inference to the evaluated value of this improving plan.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a machine configuration / machine location determining apparatus for determining the configuration or location of a machine in a factory, and more particularly to an appropriate configuration / location.

[0002]

2. Description of the Related Art When selecting and arranging machines to be used in a factory, production efficiency varies depending on the selection / arrangement method. As described above, although the machine layout in the factory is directly connected to the production efficiency, the machine layout is normally performed by human judgment. For example, in a factory with fixed entrances and exits, what kind of machine should be arranged and what kind of machine should be arranged when carrying in raw materials from the entrance, performing predetermined processing, and carrying out processed products from the exit The person arranging is to judge.

However, experience is necessary for efficient placement, and it takes a lot of time and labor to train such experienced persons. In addition, if an inconvenience occurs after the actual placement, it is necessary to consider the placement again and relocate it.

In particular, a conversion from mass production to small lot multi-production is proposed today. For this reason, the contents of processing change one after another, and accordingly, it has become necessary to change machines to be arranged and their arrangements one after another.

An object of the present invention is to solve the above-mentioned problems and to provide a machine structure / machine layout determining method or apparatus capable of easily and easily carrying out the machines used in a factory and their layout.

[0006]

A machine configuration determining method or apparatus according to claims 1 and 2 is at least the following 1) to 1).
Machine configuration improvement plan data including 4) is stored, 1) machine configuration data before improvement, 2) problem data before improvement, 3) machine configuration data after improvement, 4) improvement degree data after improvement, The similarity between machines is stored as similarity data between machines, the similarity between problems is stored as similarity data between problems, and the current placement machine data and current problem data are input. Then, for the input data, the similarity between the machine configuration improvement plan data is obtained using the inter-machine similarity data and the problem similarity data, and based on this, at least one machine configuration is obtained. Outputting the improvement plan data.

The machine configuration determining method or apparatus according to claims 2 and 12 is characterized in that the degree of similarity is calculated using fuzzy inference.

In the machine configuration determination method or apparatus according to claim 3 or 13, the similarity between each typical process is stored as typical process similarity data, and a typical structure is prepared for each machine configuration improvement plan data. When the type of process is stored and the typical process data according to the current configuration is input, the similarity is calculated using the typical process similarity data by considering the inputted typical process data. Characterize.

In the machine configuration determining method or apparatus according to claim 4 or 14, when the machine requirement data required for the configured machine is input, this machine requirement data is also taken into consideration. , Outputting at least one machine configuration improvement plan data.

In the machine configuration determination method or apparatus according to claim 5 or 15, further, the shape of the factory is divided into a predetermined number of virtual blocks based on the given shape data of the factory, and the machine configuration improvement plan is provided. Assigning a machine determined based on the data to each of the divided virtual blocks, computing the placement evaluation, computing the placement evaluation when the assignment of the machine is changed, and determining the placement information data based on the result of these computations. , Is characterized.

In the machine configuration determining method or apparatus according to claim 6 or 16, when at least the following 1) and 2) are input, 1) shape data of the factory, 2) identifying the machine to be arranged. An identifier for dividing the shape of the factory into a predetermined number of virtual blocks based on the shape data of the factory, assigning the machine represented by the identifier to each of the divided virtual blocks, and changing the allocation of the machines. , The arrangement evaluation is calculated based on the allocation state of each machine, and the arrangement information data is determined.

In the machine configuration determining method or apparatus according to claim 7 or 17, when the shape data of the factory including the fault part data indicating the fault part in which the machine cannot be arranged is given, the fault is generated. A machine is assigned to each virtual block obtained by dividing the machine in consideration of partial data.

In a machine configuration determining method or apparatus according to claim 8 or 18, the factory shape is divided into a predetermined number of virtual blocks, and allocation standard information data in which a machine is allocated to each of the divided virtual blocks, It is stored for each shape data of the factory, the similarity between each machine is stored as the similarity data between machines, the similarity between the shape data of each factory is stored as the shape similarity data, and the arrangement is determined. When the shape data of a machine and a factory desiring to be input is input as data to be calculated, the similarity to the assigned standard information data is calculated by using the similarity between machines and the shape data. Obtaining, identifying the machine layout improvement plan candidate data, determining the layout evaluation for this machine layout improvement plan candidate data, and determining the machine layout based on this layout evaluation. The features.

In the machine configuration determining method or apparatus according to claim 9 or 19, the machine layout improvement plan candidate data further meets a constraint condition determined by the operated data before obtaining a layout evaluation. Thus, the machine layout improvement plan candidate data is corrected.

In the machine configuration determining method or apparatus according to claim 10 or 20, the machine allocation improvement plan candidate data after correction is further perturbed by a machine assigned to the virtual block to obtain post-perturbation candidate data. Characterized by seeking.

[0016]

(Function) Claim 1, Claim 2, Claim 11, Claim 12
In the machine configuration determining method or apparatus of No. 1, when the current arrangement machine data and the current problem data are input, the inter-machine similarity data and the inter-problem similarity data are used for the input data. , And obtains at least one machine configuration improvement plan data based on the degree of similarity with the machine configuration improvement plan data.

Therefore, it is possible to output the machine configuration improvement plan data based on the current arrangement machine data and the current problem data.

In the machine configuration determination method or apparatus according to claim 3 or 13, when the typical process data according to the current configuration is further input, the typical process data input using the typical process similarity data is used. The degree of similarity is calculated in consideration of the process data. Therefore, it is possible to output the machine configuration improvement plan data in consideration of the typical process data.

In the machine configuration determining method or apparatus according to claim 4 or 14, when the machine requirement data required for the configured machine is input, the machine requirement data is also taken into consideration. Therefore, at least one machine configuration improvement plan data is output. Therefore, the machine configuration improvement plan data in consideration of the machine requirement data can be output.

In the machine configuration determining method or apparatus according to claim 5 or 15, further, the factory configuration is divided into a predetermined number of virtual blocks based on the given factory configuration data, and the machine configuration improvement plan is provided. The machine determined based on the data is assigned to each of the divided virtual blocks, and the placement evaluation is calculated. Also, when the allocation of the machine is changed, the placement evaluation is calculated, and the placement information data is determined based on the results of these calculations. Therefore, the arrangement information data can be obtained based on the machine configuration improvement plan data.

In the machine configuration determining method or apparatus according to claim 6 or 16, the shape of the factory is divided into a predetermined number of virtual blocks based on the shape data of the factory, and the identifier is assigned to each of the divided virtual blocks. While assigning the machine represented by, the assignment of the machine is changed, the placement evaluation is calculated based on the assignment state of each machine, and the placement information data is determined. Therefore, the layout information data can be obtained when the factory shape data and the identifier are input.

In the machine configuration determining method or apparatus according to claim 7 or 17, when the shape data of the factory including the fault part data indicating the fault part where the machine cannot be arranged is given, the fault is given. A machine is assigned to each virtual block obtained by dividing the machine in consideration of partial data. Therefore, it is possible to obtain the arrangement information data even with the shape data of the factory having the obstacle portion.

In the machine configuration determining method or apparatus according to claim 8 or 18, when the shape data of the machine and the factory for which layout determination is desired is input as the operated data,
For the operated data, the similarity between the assigned standard information data and the allocation standard information data is obtained using the similarity between machines and the similarity between shape data, and the machine layout improvement plan candidate data is specified. Then, a layout evaluation is obtained for this machine layout improvement plan candidate data, and the machine layout is determined based on this layout evaluation.

As described above, the allocation standard information data is stored in advance, and the arrangement of the machines is determined based on the similarity between the input data to be processed and the allocation standard information data. You can get the placement data.

In the machine configuration determining method or apparatus according to claim 9 or 19, the machine layout improvement plan candidate data further matches a constraint condition determined by the operated data before obtaining a layout evaluation. The machine layout improvement plan candidate data is corrected so as to do so. Therefore, it is possible to obtain the machine layout data that better matches the operated data.

In the machine configuration determination method or apparatus according to claim 10 or 20, further, with respect to the corrected machine placement improvement plan candidate data, the machine assigned to the virtual block is perturbed to obtain post-perturbation candidate data. Ask. Therefore, it is possible to obtain the machine layout data that better matches the operated data.

[0027]

[Examples] ----------------------------------- [Table of Contents] 1. Machine configuration determination device 1 1-1. Overall configuration 1-2. Hardware configuration configured by using CPU 1-3. Improvement plan creation device 2. Machine placement determining device 61 2-1. Overall configuration 2-2. Layout operation 3. Machine placement determination device 71 3-1. Configuration 3-2. Operation 4. Machine placement determination device 91 4-1. Standard pattern selection 4-2. Modification of standard pattern 4-3. Perturbation 5. Other application examples -------------------------------------------------------------------------------------------------------------------- 1. Machine configuration determination device 1 1-1. Overall Configuration One embodiment of the present invention will be described with reference to the drawings. FIG. 1 shows a functional block diagram of the machine configuration determination device 1. The system configuration display device 1 includes a current state data input unit 2, a similarity calculation unit 4, a machine configuration determination unit 18, a machine type data storage unit 6, a problem data storage unit 8, and a typical process storage unit 1.
2. A machine configuration plan data storage means 10, a machine data storage means 14, and a machine-related data storage means 16 are provided.

The machine type data storage means 6 stores the similarity between the machines as the machine similarity data, and the problem data storage means 8 stores the similarity between the problems as the problem similarity data. The typical process storage unit 12 stores the similarity between the typical processes as typical process similarity data.

The current data input means 2 inputs the current layout machine data, the current problem data, the typical process data according to the current configuration, and the machine requirement data required for the configured machine.

The machine configuration data storage means 10 includes at least a machine configuration data before improvement, problem data before improvement, machine configuration data after improvement, improvement degree data after improvement, and a typical process type. The configuration improvement plan data is stored.

The similarity calculation means 4 calculates the machine-to-machine similarity data, the problem-to-problem similarity data, the typical process data, with respect to the data input to the current data input means 2.
The degree of similarity with the machine configuration improvement plan data is obtained using the machine requirement data. Machine-related data storage means 1
6 stores the affinity between the combined machine and the combined machine.

The machine configuration determining means 18 is the similarity calculating means 4
At least one machine configuration improvement plan data is output based on the calculated similarity. At that time, the machine data storage means 1
4 and the machine-related data storage means 16, and outputs the machine configuration improvement plan data.

1-2. Hardware Configuration Configured Using CPU FIG. 2 shows a system configuration display device 21. The system configuration display device 21 is an example of a hardware configuration in which each function of FIG.
An M25, a RAM 27, a hard disk 26, a keyboard 28, a CRT 29, and a bus line 30 are provided.

The ROM 25 stores a control program for the CPU 23 and the like, and the CPU 23 controls each unit via the bus line 30 in accordance with this control program. The keyboard 28 is an input means for inputting current data.

1-3. Machine Configuration Determining Device 40 FIG. 3 shows a system conceptual diagram of the machine configuration determining device 40.
The machine configuration determination device 40 includes an improvement system plan search unit 52,
Similar system plan search unit 54, system plan evaluation / comparison unit 5
6, machine type dictionary 41, problem type dictionary 42, process type dictionary 43, improvement plan DB 46, system DB 47, machine D
It is equipped with B48.

Improvement plan D, which is means for storing machine configuration plan data
In B46, as shown in FIG. 4, a plurality of improvement plans which are machine configuration improvement plan data are stored. Each improvement plan includes a current configuration that is the machine configuration data before the improvement, a problem that is the problem data before the improvement, a process type that is a typical process in each process, an improvement content that is the machine configuration data after the improvement, and an improvement. It includes the degree of improvement effect expectation, which is the degree of improvement data later.

For example, for "improvement plan 1", the current configuration is machines MA1, MB2, MC1, and the problem is problem Q.
1 and the process type is process K2. In this case, it is shown that the improvement effect of 0.3 was obtained when the machines MA1, MB3, and MC1 were the contents of improvement.

Machine DB 4 which is the machine data storage means 14.
In FIG. 8, as shown in FIG. 5, the standard (including size and performance) of each machine, the production efficiency in each typical process, and the investment cost are stored for each machine.

In the system DB 47, which is the machine-related data storage means 14, the affinity between the combined machine and the machine to be combined, that is, when a certain machine is used, if a specific machine is combined, the production efficiency is improved. I remember the relationship. The affinity is represented by a value of 0 to 1, and when a corresponding machine is a key machine and a corresponding machine is indispensable in the configuration, the affinity is "1".

For example, in FIG. 6, the affinity between the machine MA1 and the machine MD1 is "0.3", and the relationship between the two is not so deep, but the affinity between the machine MA2 and the machine MD3 is "1". And both have an indispensable relationship. That is, when the machine MA2 is used as the machine configuration, the machine MD is used.
3 will be required.

The problem type dictionary 42, which is a problem data storage means, is a dictionary that stores a fuzzy relationship between two problem points as problem point similarity data. There is a strong relationship with similar problems, especially those of the kind that can be greatly improved by the same improvement method. For example, as shown in FIG. 7, the fuzzy relationship between the problems Q1 and Q3 is “0.8”, which has a strong relationship. That is, both are problems that are greatly improved by the same improvement method. On the other hand, the relationship between questions Q1 and Q4 is "0.
2 ”, and the relationship between the two is weak. That is, both are problems that are hardly improved by the same improvement method.

The machine type dictionary 41, which is a machine type data storage means, is a defined dictionary that stores fuzzy relationships between two machines as inter-machine similarity data. It will have a strong relationship with similar machines, especially machines with similar performance. For example, as shown in FIG.
The relationship between the machines MA1 and MA2 is “0.8”, which has a strong relationship. That is, both are machines that show similar performance. On the other hand, the fuzzy relationship between the machines MA1 and MA4 is "0.2", and the relationship between them is weak. That is, they are machines that exhibit different performances.

Process type dictionary 4 which is a typical process storage means
Reference numeral 3 is a dictionary that stores fuzzy relationships between two process types as typical process similarity data. There is a strong relationship with similar processes, especially those types of processes in which the same problem can be greatly improved by the same improvement method. For example, as shown in FIG. 9, the relationship between the processes K1 and K3 is “0.8”, which has a strong relationship. That is, both are steps in which the same problem can be greatly improved by the same improvement method. On the other hand, steps K1 and K
The fuzzy relationship of No. 4 is "0.2", and the relationship between the two is weak. That is, both are steps that cannot be improved by the same improvement method even if the problems are the same.

The current system configuration, current problems, typical process types, and machine requirements are input to the improvement system plan search unit 52 as input data.

The current system configuration means the machines that compose the current system, and the current arrangement machine data. The input machine needs to be registered in the machine type dictionary 41.

The current problem is a problem to be improved in the current system, which is current problem data. The input problem needs to be registered in the problem type dictionary 42.

The typical process type refers to the most typical process among the processes processed by this machine structure, for example, 10
φ pipe bending, 10 mm square punching, etc. The process type to be input is the process type dictionary 43.
Must be registered with.

The machine requirements are the conditions that the machines constituting the system must absolutely meet. For example, there are the maximum material size that can be machined, the required layout area, and the like.
This is because such a machine cannot be placed if the required placement area is 20 m ² even though the actual placement area is 18 m ² .

When these input data are input, the improvement system plan search unit 52 fuzzy searches the improvement plan DB 46 using the machine type dictionary 41, the problem type dictionary 42, the process type dictionary 43, and the machine DB 48. . That is, the condition similarity is calculated for each improvement plan stored in the improvement plan DB 46. Specifically, it is performed as follows.

For example, as input data, the current system configuration "MA2, MB1, MC3" and the current problem "Q
3 ”, a typical process type“ K3 ”, and a machine requirement“ required layout area 20 m ² ”are input, with reference to the flowchart of FIG. 10.

First, it is determined whether or not there is an unsearched improvement plan in the improvement plan DB 46 shown in FIG. 4 (step ST1).
When all the improvement plans have been searched, the process ends. When there is an unsearched improvement plan, the process proceeds to step ST2, the next improvement plan is searched, and in step ST3, it is determined whether or not the improvement plan satisfies the machine requirement. The machine DB 48 shown in FIG. 5 is referred to for this determination. Specifically, improvement plan 1
It is only necessary to judge whether or not the current configuration "MA1, MB2, MC1" satisfies the machine requirement "necessary area for arrangement is 20 m ² ". If the improvement plan does not satisfy the machine requirement, the step ST1 is performed without obtaining the condition similarity.
Returning to step ST2 and subsequent steps for other improvement plans are repeated.

If the improvement plan satisfies the machine requirement, the process proceeds to step ST4 to obtain the condition similarity between the improvement plan 1 and the input data.

First, the current system configuration "MA2, MB
1, MC3 "and current configuration of improvement plan 1" MA1, MB2, M
C1 ”(see FIG. 4) is calculated. The machine type dictionary shown in FIG. 8 is used for the similarity calculation. For example,
The degree of similarity between the machines MA2 and MA1 is “0.8”. Similarly, machine MB1, machine MB2, machine MC3
And the machine MC1 are calculated.

Next, the degree of similarity between the current problem "Q3" and the problem "Q1" of improvement plan 1 (see FIG. 4) is calculated. The problem dictionary shown in FIG. 7 is used to calculate the similarity. For example, the similarity between the problem Q3 and the problem Q1 is "0.
8 ".

Next, the degree of similarity between the typical process type “K3” and the process type “K2” of the improvement plan 1 (see FIG. 4) is obtained. The process type dictionary shown in FIG. 9 is used to calculate the similarity. For example, the degree of similarity between the process type K3 and the process type K2 is “0.5”.

Based on the result thus obtained, the condition similarity between the improvement plan 1 and the input data is obtained. In this embodiment, the degree of similarity between the current system configuration and the improvement plan 1,
The conditional similarity was calculated by calculating the logical product of the similarity between the current problem and the problem of improvement plan 1, and the similarity between the typical process type and the process type of improvement plan 1.

Next, returning to FIG. 10, in step ST5, it is determined whether or not to leave the improvement plan as an improvement plan.
In the present embodiment, a fuzzy operation is performed using the condition similarity and the improvement effect expectation of the improvement plan to make a determination. Specifically, the rules shown in FIG. 11 and FIG.
2. Using the membership function shown in FIG. 13, for each rule, the conformance of the antecedent part is calculated, and the conformance of the consequent part is calculated based on this. Based on the suitability of the consequent part for all the rules, it is determined whether the improvement plan is an effective improvement plan, that is, whether the improvement plan is left as an improvement plan.

Returning to FIG. 10, if it is determined in step ST5 that the improvement plan is left as the improvement plan, step S5.
Proceed to T6 and leave the improvement plan as an improvement plan.

Returning to step ST1, steps ST1 to ST6 are repeated for all improvement plans. If it is determined in step ST5 that the improvement plan is not left as the improvement plan, the fuzzy calculation is not performed for the improvement plan and the step S is performed for other improvement plans.
Repeat T1 and below.

In this way, the improvement plan DB4 shown in FIG.
For each improvement plan stored in 6, it is determined whether or not it is an effective improvement plan.

The similar system plan retrieving unit 54 uses the system DB 47 for each machine described in the improvement contents of the remaining improvement plan, is a machine having a high affinity, and is not listed as a machine configuration. Determine if it is a machine.
By adding a machine having a high degree of affinity as a machine structure, the production efficiency can be further improved. The operation of the similar system plan search unit 54 will be described with reference to FIG. Here, the case where the improvement plan 1 is left as the improvement plan and the similar system plan is searched will be described.

First, the improvement content “M” of the improvement plan 1 shown in FIG.
Using each of the machines "A1, MB3, MC1" as a combination machine, it is searched whether or not there is a combination machine (similar machine) having a high affinity (step ST21 in FIG. 14). In the present embodiment, whether or not the machine is a similar machine is determined using the affinity of 0.8 as a threshold value. For example, as shown in FIG.
If 1 is a key machine, the affinity for machine MD3 is "0.9", so there is a similar machine. In step ST23, it is determined whether or not the similar machine satisfies the machine requirement condition. For example, in this case, the machine MD3 has the machine requirement condition "the required layout area is 20
It is determined whether or not "m ² " is satisfied. If the machine requirements are satisfied, the process proceeds to step ST24, and it is determined whether or not the machine MD3 is a machine not listed as a configuration (whether or not it is a machine not yet introduced). If the machine is not listed as a configuration, a new improvement plan is created by adding the machine to the improvement plan.

In this embodiment, the affinity is 0.8.
Although it is determined whether or not the machine is a similar machine by using as a threshold value, the threshold value may be set without being limited to this.

Returning to step ST21, steps ST21 to ST25 are repeated until there are no similar machines. If the machine requirement is not satisfied in step ST23, or if the machine is listed as the configuration in step ST24, steps ST21 and thereafter are repeated.

In this way, the similar system plan retrieval unit 5
4 creates a new improvement plan having a machine configuration having a machine having a high affinity for the improvement plan output from the improvement system plan search unit 52.

The system plan evaluation / comparison unit 56 uses the improvement plan output from the similar system plan search unit 54 as shown in FIG.
The evaluation value of the improvement plan is obtained using the rule shown in FIG. 5 and the membership function shown in FIGS. At that time, by referring to the investment cost of the machine stored in the machine DB 48, an evaluation value with a good balance between the investment amount and the improvement effect can be obtained. The obtained evaluation values are in descending order of evaluation value,
It is output as an improvement plan.

In this way, the improvement system plans 1 to n shown in FIG. 3 are output.

2. Machine placement determining device 61 2-1. Overall Configuration Another embodiment of the present invention will be described with reference to the drawings. FIG. 17
A functional block diagram of the machine location determining device 61 is shown in FIG. The machine placement determination device 61 includes a shape data storage unit 65, a shape division unit 62, a machine assignment unit 67, a machine assignment change unit 63, a placement evaluation unit 69, and a placement determination unit 71.
Is equipped with.

The shape data storage means 65 stores the shape data of the given factory. The shape dividing means 62 divides the shape of the factory into a predetermined number of virtual blocks based on the shape data. The machine assigning unit 67 assigns a machine determined based on the given machine configuration improvement plan data to each divided virtual block. The machine assignment changing means 63 changes the assignment of the machine.

The layout evaluation means 69 calculates the layout evaluation based on the allocation state of each machine and stores the calculation result.
The arrangement determining means 71 determines arrangement information data based on the calculation result.

In this way, the machine determined based on the given machine configuration improvement plan data is allocated to the virtual block, the allocation is changed, and the allocation evaluation is obtained based on each allocation state, thereby producing A highly efficient arrangement can be obtained.

Since the hardware configuration realized by using the CPU is almost the same as that of the machine configuration determining apparatus 1, its explanation is omitted.

2-2. Layout Operation A layout (assignment) operation of the machine layout determining device 61 will be described with reference to FIGS. First, the shape data of a given factory is divided into cells as shown in FIG. In the present embodiment, it is divided into 9 as shown in the figure.

Next, the machine to be laid out and the constraint conditions are input as shown in FIG. 18 (step ST31).
The constraint condition refers to a condition that constrains the layout such as the position of the doorway, the position of the obstacle, and the size of the obstacle. For example, in FIG. 21A, an obstacle is arranged in cell C5, an inlet is arranged in cell C3, and an outlet is arranged in cell C7. Note that the notation of components is shown in FIG. 21B.

Returning to FIG. 18, the initial position is input. The initial position is a cell position to which a central machine tool in the machine configuration is assigned. In this way, the operator can specify the cell position of the machine tool, which is important in determining the layout, so that the layout calculation can be further reduced. The machine tool, which is the main machine, is placed at this initial position (step ST32). Here, as shown in FIG. 21A, it is assumed that the machine tool is arranged in the cell C1.

Next, in step ST33, a robot, which is an auxiliary work machine, is arranged. The robot is
Material is moved from the end of the carrier machine to the work table of the machine tool, or the machined product is moved to the carrier machine.

Next, in step ST34, the transport path from the entrance to the machine tool and from the machine tool to the exit is determined. At that time, the conveyor and the turntable, which are transport machines, are arranged so that the transportation distance and the number of bendings are minimized. In this way, as shown in FIG. 21B, the main machine, the auxiliary work machine, and the transfer machine are arranged in each cell.

Next, in step ST35, the layout result is evaluated. The layout result is evaluated as follows. From the layout state, the transport distance, the number of bends, the distance between the entrance and the machine tool, the distance between the exit and the machine tool, and the like are obtained as the characteristic quantities. Based on the obtained feature amount, fuzzy calculation is performed using the rules and membership functions shown in FIGS. 24 to 25 to obtain the evaluation value.

Returning to FIG. 18, cells are exchanged (step ST36). Regarding cell replacement Fig. 19
Will be explained. Replace the cell where the machine tool is placed with another cell and perform the relayout (step ST
41). For example, when the layout shown in FIG. 22A is laid out from the state shown in FIG. 22A, the cells 5C and C3 are exchanged as shown in FIG. 22C.

After the re-layout, the layout result in this state is evaluated in the same manner as described above (step ST
42). Next, the layout having the highest evaluation value is selected (step ST43).

As described above, it may be possible to obtain a layout with higher production efficiency by exchanging the cells. For example, when the layout is changed from FIG. 23B to FIG. 23C, the conveyance distance, the number of bendings, and the investment amount do not change in both cases. However, when the layout is changed from FIG. 22B to FIG. 22C, comparing the two, the transport distance and the number of bends do not change, but in FIG. 22B, short conveyors CO1 and CO2 and two conveyors are required At 22C, since one long conveyor CO3 is sufficient, the investment amount is reduced.

In this embodiment, the cells were exchanged so that the machine tool was assigned to the initially set transport path (see FIGS. 22B and 22C). As a result, the configuration of the transport machine can be made more efficient without increasing the number of calculations so much.

In this embodiment, the cells are exchanged only once, but they may be exchanged a plurality of times.

3. Machine placement determination device 81 3-1. Configuration Another embodiment of the present invention will be described with reference to the drawings. FIG. 26
A functional block diagram of the machine location determining device 81 is shown in FIG. The machine layout determination device 81 includes a layout information input unit 82, a shape division unit 83, a shape data storage unit 84, a machine allocation unit 85, a machine allocation change unit 86, a layout evaluation unit 88, and
And the arrangement determining means 90. At least the shape data of the factory and the identifier for identifying the machine to be arranged are input to the arrangement information input means 82. In this embodiment, the machine name is used as the identifier.

The shape data storage means 84 stores the shape data of the given factory. The shape data of this factory is
It includes fault data indicating the fault where the machine cannot be located. The shape dividing means 83 divides the shape of the factory into a predetermined number of virtual blocks based on the shape data of the factory.

The machine allocating means 85 allocates a machine represented by an identifier for identifying the machine input from the layout information inputting means 82 to each of the divided virtual blocks.
The machine assignment changing means 86 changes the assignment of the machines. With respect to the allocation and its change, a machine is allocated in consideration of the faulty part data. The layout evaluation means 88 calculates the layout evaluation based on the allocation state of each machine and stores the calculation result. Arrangement determining means 9
0 determines the arrangement information data based on the calculation result.

As described above, in this embodiment, when the shape data of the factory and the identifier for identifying the machine to be arranged are input, the obstacle portion data is taken into consideration,
The placement information data is automatically determined.

Since the hardware configuration realized by using the CPU is almost the same as that of the machine configuration determining apparatus 1, its explanation is omitted.

3-2. Operation The layout (assignment) operation of the machine location determining device 81 will be described with reference to FIGS. 27 to 28. First, the shape data of a given factory is divided into cells in the same manner as the machine layout determining device 61.

Next, the machine to be laid out and the constraint condition are input as shown in FIG. 27 (step ST5).
2). The constraint conditions have been described in the machine placement determining device 61, and thus will be omitted.

Obstacles are placed based on the constraint conditions (step ST53). Next, the machine tool, which is the main machine, is placed in any of the cells C1 to C9 (step ST54).

Next, in step ST55, the transport path from the entrance to the machine tool and from the machine tool to the exit is determined. At that time, similarly to the machine layout determining apparatus 61, the conveyor and the turntable, which are the transport machines, are arranged so that the transport distance and the number of bendings are minimized.

Thus, for example, as shown in FIGS. 21A to 21D, the main machine, the auxiliary work machine, and the transfer machine are arranged in each cell in consideration of obstacles.

Returning to FIG. 27, the layout result is evaluated (step ST57). The layout result is evaluated in the same manner as the machine layout determining device 61.

In step ST58, it is determined whether or not the initial positions have been set for all cells. That is, it is determined whether or not there is a cell in which the machine tool is not arranged among the cells C1 to C9. If there is a cell in which the machine tool is not placed among the cells C1 to C9, step ST
54 to step ST57 are repeated.

When there is no cell among the cells C1 to C9 in which the machine tool is arranged, the process proceeds to step ST59. In step ST59, the layout having the highest evaluation value is selected.

In this way, by laying out all the cells as initial positions, a layout with higher production efficiency can be obtained. Further, even if the shape data of the factory has obstacles, the layout can be avoided.

4. Machine Placement Determination Device 91 Another embodiment of the present invention will be described with reference to the drawings. FIG. 29
A functional block diagram of the machine location determining device 91 is shown in FIG. The machine layout determination device 91 uses the standard allocation data storage means 9
8, machine type storage means 97, shape data storage means 93,
The calculation target data input unit 92, the similarity calculation unit 95, the candidate data identification unit 99, and the arrangement determination unit 100 are provided.

The standard allocation data storage means 98 divides the shape of the factory into a predetermined number of virtual blocks, and stores allocation standard information data in which a machine is allocated to each of the divided virtual blocks, for each shape data of the factory. The machine type storage means 97 stores the similarity between machines as the machine similarity data.

The shape data storage means 93 stores the similarity between the shape data of each factory as the shape similarity data. The shape data storage unit 93 includes an entrance / exit similarity dictionary and an obstacle similarity dictionary.

About Doorway Similarity Dictionary FIGS. 35 to 37
Will be explained. As shown in FIG. 35, the shape data of the factory is divided into cells, and entrance / exit numbers D1 to D12 are attached to the entrance / exit positions of each cell. It should be noted that the position of the entrance is normalized so that it is located at any one of D1 to D3.

The entrance / exit similarity dictionary includes an entrance similarity dictionary and an exit similarity dictionary. The entrance similarity dictionary is a dictionary defining a fuzzy relationship between two entrances, as shown in FIG. It has a weak relationship with distant entrances. For example, as shown in FIG. 36, the relationship between the entrance D1 and the entrance D3 is “0.2”. The relationship between the entrance D1 and the entrance D2 is “0.5”.

The exit similarity dictionary is, as shown in FIG.
It is a dictionary that defines a fuzzy relationship between two exits. It has a weak relationship with distant exits. For example,
As shown in the figure, the relationship between the exit D4 and the exit D6 is "0.
2 ”, which has a weak relationship. Also, exit D7 and exit D6
The relationship is "0.8", which means a strong relationship.

Next, the obstacle similarity dictionary will be described. In the obstacle similarity dictionary, the similarity of obstacles is
It is determined from the area and positional relationship of obstacles. The degree of similarity to the area is determined by the area ratio. Specifically, it is represented by (area of obstacle to be laid out / area of obstacle of standard pattern).

The positional relationship of obstacles is determined by a dictionary defining a fuzzy relationship between the positions of two obstacles, as shown in FIG. For distant obstacles,
Have a weak relationship. For example, as shown in the figure, the relationship between the obstacles in the cells C5 and C1 is "0.2", which is a weak relationship. The relationship between the obstacles in the cells C4 and C1 is “0.4”, which means that the relationship is somewhat weak.

When the area of the obstacle is larger than that of two cells, it is sufficient that the two cells have the obstacle.

Returning to FIG. 29, the operated data input means 92.
For, the shape data of the machine and the factory whose layout is desired is input as the data to be operated. The similarity calculation means 95
For the calculated data, the similarity between the assigned standard information data and the similarity between machines and shape data is used. The candidate data specifying means 99 specifies the machine layout improvement plan candidate data based on the similarity calculated by the similarity calculating means 95.

The layout determining means 100 determines layout of the machine layout improvement plan candidate data and determines the layout of machines based on this layout evaluation.

As described above, the allocation standard information data is stored in advance, and the arrangement of the machines is determined based on the similarity between the input operated data and the allocation standard information data. Can be placed.

Further, the layout determining means 100 makes the machine layout improvement plan candidates such that the machine layout improvement plan candidate data matches the constraint conditions determined by the operated data before obtaining the layout evaluation. Correct the data. Therefore, it is possible to perform the machine layout that more closely matches the operated data.

Further, the layout determining means 100 perturbs the machine allocation improvement plan candidate data after modification by perturbing the machine assigned to the virtual block to obtain post-perturbation candidate data. Therefore, it is possible to perform the machine layout that more closely matches the operated data.

Since the hardware configuration realized by using the CPU is almost the same as that of the machine configuration determining apparatus 1, its explanation is omitted.

4-1. Standard Pattern Selection A layout (assignment) operation of the machine layout determination device 91 will be described with reference to FIGS. 30 to 38. As shown in FIGS. 30A to 30H, the standard allocation data storage unit 98 stores a standard pattern which is allocation standard information data for each shape data of the factory. With standard information data, the shape of the factory is divided into a predetermined number of virtual blocks,
The data in which the main machine, auxiliary work machine, and transfer machine are assigned to each of the divided virtual blocks.

Next, a machine to be laid out as shown in FIG. 30 and constraints are input (step ST6).
1). The constraint conditions have been described in the machine placement determining device 61, and thus will be omitted.

In step ST62, a fuzzy search is performed for a pattern that matches the input condition. Specifically, using the inter-machine similarity data stored in the machine type storage means 97 and the shape similarity data stored in the shape data storage means 93, the standard pattern stored in the standard allocation data storage means 98 is set. Just do a fuzzy search. The fuzzy search method is the same as that of the machine configuration determination device 40, and therefore its explanation is omitted. 4-2. Correction of standard pattern Proceeding to step ST63, some patterns having a high degree of conformity are selected. Of these, the part that does not work well with the standard pattern (the part that does not meet the conditions) is corrected (step ST64). For example, given the shape shown in FIG. 39A,
When the pattern shown in FIG. 31C is selected, the turntable TT1 may be removed and the conveyor CO1 may be laid out as shown in FIG. 39B.

Returning to FIG. 30, layout evaluation is performed on the corrected layout (step ST65). Since the evaluation of the layout result is the same as that of the machine layout determining device 61, the description is omitted.

4-3. Regarding perturbation, proceed to step ST66 and apply perturbation. Perturbation means to slightly shift the position of the machine tool vertically and horizontally within the cell, as shown in FIG. This makes it possible to obtain a layout that better matches the input data.

The perturbation process will be described with reference to FIG. In step ST71, the position of the machine tool is slightly shifted vertically and horizontally, and the layout is re-arranged. Then, the layout result is evaluated (step ST72). Step S
Proceed to T73 to select one with a high evaluation. This is the end of the perturbation process.

Returning to FIG. 30, the process proceeds to step ST67.
In step ST67, the processes of steps ST64 to ST66 are performed for all the selection patterns selected in step ST63.

Then, in step ST68, a highly evaluated pattern is selected from the perturbed patterns.

As described above, by selecting from the layout patterns stored in advance, it is possible to easily obtain a layout with high production efficiency. Also, it is possible to correct only the part that does not meet the conditions so that it matches the input data.

Furthermore, by perturbing after correction and obtaining post-perturbation candidate data, it is possible to easily obtain a layout with higher production efficiency.

In the present embodiment, the standard information data includes the allocation data of the transport machine. But,
Also in this case, the standard information data does not include the allocation data of the transport machine, only the allocation data of the main machine and the auxiliary work machine is stored, and the machine placement determining devices 61 and 7 are used.
After assigning the main machine and auxiliary work machine in the same way as in 1,
You may make it determine a conveyance machine separately.

5. Other Application Examples The perturbation process can be combined with each of the above embodiments.

Further, in the machine placement determining devices 61 and 71, the conveyor machines such as conveyors and turntables are
Not given to the placement information input means 82 (see FIG. 26),
After the layout is determined, the transport machine to be used and its layout are determined. However, the transport machine may be determined when the machine configuration is determined.

In the machine layout determining device 61,
The cells were exchanged so that the machine tool was assigned to the initially set transport path (see FIGS. 22B and C). However, the present invention is not limited to this, and cells may be exchanged after leaving the initially set transport path.

In each of the above embodiments, the shape of the factory is 3 × 3 divided into 9, but the shape is not limited to this.
For example, 2 × 2 4 divisions, 4 × 4 16 divisions, or 3 × 4 12 divisions may be used.

In the machine configuration determining device 40, regarding the improvement plan in the improvement plan DB 46, whether to leave the improvement plan or not is determined by using fuzzy calculation. However, instead of the fuzzy calculation, for example, a predetermined threshold value is used. You may do it.

In each of the above embodiments, the CPU 23 is used to realize the function shown in FIG. 1, and this is realized by software. However, some or all of it
It may be realized by hardware such as a logic circuit.

[0130]

According to the machine configuration determining method or apparatus of claim 1, claim 2, claim 11, or claim 12, when the current arrangement machine data and the current problem data are input, they are input. For the data, the similarity between the machine configuration data and the problem point similarity data is used to determine the similarity with the machine configuration improvement plan data, and based on this, at least one machine configuration improvement plan data is output.

Therefore, the machine configuration improvement plan data can be output based on the current arrangement machine data and the current problem data.

Thus, there is provided a machine configuration determining method or apparatus capable of simply and easily determining a machine to be arranged in a factory by inputting the current arrangement machine data and the current problem data. You can

In the machine configuration determining method or apparatus according to claim 3 or 13, when the typical process data based on the current configuration is further input, the typical process data input using the typical process similarity data is used. The degree of similarity is calculated in consideration of the process data. Therefore, it is possible to output the machine configuration improvement plan data in consideration of the typical process data.

As a result, it is possible to provide a machine configuration determination method or its apparatus in consideration of typical process data of machines arranged in a factory.

In the machine configuration determining method or apparatus according to claim 4 or 14, when the machine requirement data required for the configured machine is input, this machine requirement data is also taken into consideration. Therefore, at least one machine configuration improvement plan data is output. Therefore, the machine configuration improvement plan data in consideration of the machine requirement data can be output.

As a result, it is possible to provide the machine configuration determining method or its apparatus in consideration of the machine requirement data of the machines arranged in the factory.

In the machine configuration determining method or apparatus according to claim 5 or 15, further, the factory configuration is divided into a predetermined number of virtual blocks based on the given factory configuration data, and the machine configuration improvement plan is provided. The machine determined based on the data is assigned to each of the divided virtual blocks, and the placement evaluation is calculated. Also, when the allocation of the machine is changed, the placement evaluation is calculated, and the placement information data is determined based on the results of these calculations. Therefore, the arrangement information data can be obtained based on the machine configuration improvement plan data.

As a result, it is possible to provide a machine layout determining method or apparatus capable of easily and easily determining the layout of machines in a factory.

In the machine configuration determining method or apparatus according to claim 6 or 16, the shape of the factory is divided into a predetermined number of virtual blocks based on the shape data of the factory, and the identifier is assigned to each divided virtual block. While assigning the machine represented by, the assignment of the machine is changed, the placement evaluation is calculated based on the assignment state of each machine, and the placement information data is determined. Therefore, the layout information data can be obtained when the factory shape data and the identifier are input.

As a result, it is possible to provide a machine layout determining method or apparatus capable of simply and easily determining the layout of machines in a factory only by inputting the shape data of the factory and the identifier. .

In the machine configuration determining method or apparatus according to claim 7 or 17, when the shape data of the factory including the fault part data indicating the fault part where the machine cannot be arranged is given, the fault is generated. A machine is assigned to each virtual block obtained by dividing the machine in consideration of partial data. Therefore, it is possible to obtain the arrangement information data even with the shape data of the factory having the obstacle portion.

Accordingly, it is possible to provide a machine location determining method or apparatus capable of easily and easily determining the location of the machine in the factory even in the factory having the obstacle portion.

In the machine configuration determining method or apparatus according to claim 8 or 18, when the shape data of the machine and the factory for which layout determination is desired is input as the data to be calculated,
For the operated data, the similarity between the assigned standard information data and the allocation standard information data is obtained using the similarity between machines and the similarity between shape data, and the machine layout improvement plan candidate data is specified. Then, a layout evaluation is obtained for this machine layout improvement plan candidate data, and the machine layout is determined based on this layout evaluation. As a result, it is possible to provide a machine location determination method or apparatus that can easily and easily determine the location of a machine in a factory by simply inputting the shape data of the machine and the factory for which the location is desired. .

In the machine configuration determination method or apparatus according to claim 9 or claim 19, further, the machine layout improvement plan candidate data conforms to the constraint condition determined by the operated data before determining the layout evaluation. The machine layout improvement plan candidate data is corrected so as to do so. Therefore, it is possible to obtain the machine layout data that better matches the operated data.

As a result, it is possible to provide a machine placement determining method or apparatus that can obtain a machine placement that meets the constraint conditions.

In the machine configuration determination method or apparatus according to claim 10 or 20, further, with respect to the modified machine placement improvement plan candidate data, the machine assigned to the virtual block is perturbed to obtain post-perturbation candidate data. Ask. Therefore, it is possible to obtain the machine layout data that better matches the operated data.

Thus, the machine layout determining method or apparatus for easily and easily determining the machine layout in the factory by inputting the shape data of the machine and the factory whose layout is desired is provided. be able to.

[Brief description of drawings]

FIG. 1 is a functional block diagram of a machine configuration determination device 1 according to the present invention.

FIG. 2 is a block diagram showing a machine configuration determining device 21 according to the present invention,
It is a figure which shows the hardware constitutions realized by.

FIG. 3 is a system conceptual diagram of a machine configuration determination device 40.

FIG. 4 is a diagram showing machine configuration improvement plan data in the improvement plan DB 46.

FIG. 5 is a diagram showing machine data of a machine DB 48.

FIG. 6 is a diagram showing an affinity between a combined machine and a combined machine in a system DB 47.

7 is a diagram showing a fuzzy relationship between two problems in the problem type dictionary 42. FIG.

8 is a diagram showing a fuzzy relationship between two machines in the machine type dictionary 41. FIG.

9 is a diagram showing a fuzzy relationship between two typical processes in the process type dictionary 43. FIG.

FIG. 10 is a flowchart in which the improvement system plan search unit 52 obtains a condition similarity.

FIG. 11 is a diagram showing an example of a rule for performing a fuzzy calculation as to whether or not to leave it as an improvement plan.

FIG. 12 is a diagram showing an example of a membership function for performing a fuzzy operation on whether or not to leave it as an improvement plan.

FIG. 13 is a diagram showing an example of a membership function for performing a fuzzy operation on whether to leave it as an improvement plan.

FIG. 14 is a flowchart for creating a similar system plan.

FIG. 15 is a diagram showing an example of a rule for performing a fuzzy operation for placement evaluation.

FIG. 16 is a diagram showing an example of a membership function for performing a fuzzy operation on a placement evaluation.

FIG. 17 is a functional block diagram of a machine location determining device 61 according to the present invention.

FIG. 18 is a flowchart of layout determination by the machine layout determining device 61.

FIG. 19 is a flowchart of cell replacement in the machine placement determining apparatus 61.

20 is a diagram showing virtual blocks in the machine layout determining device 61. FIG.

FIG. 21 is a diagram showing an example of layout in virtual blocks.

FIG. 22 is a diagram showing a state in which cells have been replaced after being laid out in a virtual block.

FIG. 23 is a diagram showing a state in which cells have been exchanged after being laid out in a virtual block.

FIG. 24 is a diagram showing an example of a rule for performing a fuzzy operation for placement evaluation.

FIG. 25 is a diagram showing an example of a membership function for performing a fuzzy operation on a placement evaluation.

FIG. 26 is a functional block diagram of a machine location determining device 81 according to the present invention.

FIG. 27 is a flowchart of layout determination by the machine layout determination device 81.

FIG. 28 is a diagram showing an example of a layout in the machine layout determining device 81.

FIG. 29 is a functional block diagram of a machine location determining device 91 according to the present invention.

FIG. 30 is a flowchart of layout determination by the machine layout determination device 91.

FIG. 31 is a diagram showing an example of a standard pattern in the machine layout determining device 91.

FIG. 32 is a diagram showing an example of a rule and a membership function for performing a fuzzy operation on a placement evaluation.

FIG. 33 is a diagram showing perturbations.

FIG. 34 is a flowchart of a perturbation process of the machine placement determination device 91.

FIG. 35: Doorway number D1 to doorway position of each cell
It is a figure which shows the state which added D12.

FIG. 36 is a diagram showing a fuzzy relationship between two entrances in the entrance similarity dictionary.

FIG. 37 is a diagram showing a fuzzy relationship between two exits in the exit similarity dictionary.

FIG. 38 is a diagram showing a fuzzy relationship between two obstacles in the obstacle similarity dictionary.

FIG. 39 is a diagram showing correction of a standard pattern in the machine layout determination device 91.

[Explanation of symbols]

 2 ... Current data input means 4 ... Similarity calculation means 6 ... Machine type data storage means 8 ... Problem data storage means 12 ... Typical process storage means 10. Machine configuration plan data storage means 14 ... Machine data storage means 16 ... Machine-related data storage means 18 ... Machine configuration determination means 65 ... Shape data storage means 62 ... Shape division means 67 ... Machine allocation means 63 ... Machine allocation change means 69 ... Layout evaluation means 71 ... Layout determination means 98 ... Standard allocation data storage means 97 ... Machine type storage means 93 ... Shape data storage Means 92 ... Operated data inputting means 95 ... Similarity calculating means 99 ... Candidate data specifying means 100 ... Arrangement determining means

Claims (20)

[Claims] 1. A machine configuration plan data storage means for storing machine configuration improvement plan data including at least the following 1) to 4), 1) machine configuration data before improvement, 2) problem data before improvement, and 3). Machine configuration data after improvement, 4) Improvement degree data after improvement, machine type data storage means for storing similarity between machines as similarity data between machines, similarity between problems, similarity between problems Problem data storage means to be stored as data, current layout machine data and current data input means to which current problem data is input, and data input to the current data input means,
A similarity calculation unit that obtains a similarity to the machine configuration improvement plan data using the machine-to-machine similarity data and the problem-to-problem similarity data; and at least 1 based on the similarity calculated by the similarity calculation unit. A machine configuration determining device comprising: a machine configuration determining unit that outputs one machine configuration improvement plan data. 2. The machine configuration determining apparatus according to claim 1, wherein the similarity calculating unit calculates the similarity using fuzzy inference. 3. The machine configuration determination device according to claim 1 or 2, further comprising: a typical process storage unit that stores the similarity between the typical processes as typical process similarity data. The means stores a typical process type for each machine configuration improvement plan data, and when the typical process data according to the current configuration is input to the current condition data input device, the similarity calculation device causes the typical process to be performed. A machine configuration determining apparatus, wherein the similarity is obtained by using the similarity data in consideration of the input typical process data. 4. The machine configuration determination device according to claim 1, wherein the machine configuration determination means receives the machine requirement data required for the configured machine, and outputs the machine requirement data. In consideration of the above, at least one machine configuration improvement plan data is output, and the machine configuration determining apparatus is characterized. 5. The machine configuration determination device according to claim 1, shape data storage means for storing shape data of a given factory, and a predetermined number of virtual shapes of the factory based on the shape data. Shape dividing means for dividing into blocks, a machine determined based on the machine configuration improvement plan data,
Machine allocation means for allocating to each divided virtual block, machine allocation changing means for changing the allocation of the machine, layout evaluation means for calculating the layout evaluation based on the allocation state of each machine, and storing the calculation result, to the calculation result A machine placement determining device, comprising: a placement determining unit that determines placement information data based on the above. 6. Arrangement information input means for inputting at least the following 1) and 2), 1) shape data of a factory, 2) an identifier for identifying a machine to be arranged, based on the shape data of the factory Shape dividing means for dividing the shape of the factory into a predetermined number of virtual blocks, machine allocation means for allocating the machine represented by the identifier to each divided virtual block, machine allocation changing means for changing the allocation of the machine, each machine A machine placement determining apparatus, comprising: a placement evaluating unit that computes a placement evaluation based on the assignment state and stores a computation result; and a placement determining unit that determines placement information data based on the computation result. 7. The machine placement determining apparatus according to claim 6, wherein the shape data of the factory includes fault part data indicating a fault part in which a machine cannot be located, and the machine assigning unit stores the fault part data. In consideration of the above, the machine allocation determining apparatus is characterized by allocating a machine to each virtual block obtained by dividing the machine. 8. A standard allocation data storage means for dividing the shape of a factory into a predetermined number of virtual blocks, and storing allocation standard information data in which machines are allocated to the respective divided virtual blocks for each shape data of the factory. Machine type storage means that stores the similarity between machines as inter-machine similarity data, shape data storage means that stores the similarity between the shape data of each factory as shape similarity data, Shape data is input as the data to be operated, the operated data input means, and similarity of the operated data is obtained by using the machine similarity and the shape data similarity to obtain the similarity with the assigned standard information data. Degree calculating means, candidate data specifying means for specifying machine placement improvement plan candidate data based on the similarity calculated by the similarity calculating means, The arrangement proposed improvements candidate data, determine the placement evaluation, arrangement determining means for determining the placement of the machine on the basis of this arrangement rating, mechanical arrangement determination apparatus characterized by comprising a. 9. The machine placement determining apparatus according to claim 8, wherein the placement determining means matches the constraint condition determined by the operated data before obtaining the placement evaluation for the machine placement improvement plan candidate data. And correcting the machine layout improvement plan candidate data as described above. 10. The machine placement determining apparatus according to claim 9, wherein the placement determining means perturbs a machine assigned to the virtual block to obtain post-perturbation candidate data for the corrected machine placement improvement plan candidate data. A machine placement determining device characterized by: 11. A machine configuration improvement plan data including at least the following 1) to 4) is stored: 1) Machine configuration data before improvement, 2) Problem data before improvement, 3) Machine configuration data after improvement. 4) The improvement data after improvement, the similarity between each machine is stored as the machine similarity data, and the similarity between each problem point is stored as the problem point similarity data. When the placement machine data and the current problem data are input, the similarity between the input data and the machine configuration improvement plan data is calculated using the machine-to-machine similarity data and the problem-to-problem similarity data. And a method for determining at least one machine configuration improvement based on the obtained data. 12. The machine configuration determination method according to claim 11, wherein the similarity is calculated using fuzzy inference. 13. The machine configuration determining method according to claim 11 or 12, wherein the similarity between each typical process is stored as typical process similarity data, and the typical process is set for each machine configuration improvement plan data. The type is stored, and when the typical process data according to the current configuration is input, the similarity is calculated using the typical process similarity data using the input typical process data. Machine configuration determination method. 14. The machine configuration determining method according to claim 11, wherein when machine requirement data required for a machine to be constructed is input, this machine requirement data is also taken into consideration, Outputting at least one machine configuration improvement plan data. 15. The machine configuration determination method according to claim 11, further comprising dividing the factory shape into a predetermined number of virtual blocks based on the given factory shape data, The machine determined based on
A machine placement determining method characterized by: allocating to each divided virtual block, computing a placement evaluation, computing a placement evaluation when the assignment of the machine is changed, and deciding placement information data based on these computation results. . 16. When at least the following 1) and 2) are input, 1) factory shape data, 2) an identifier for identifying a machine to be placed, and a factory shape based on the factory shape data. Is divided into a predetermined number of virtual blocks, the machine represented by the identifier is assigned to each of the divided virtual blocks, the assignment of the machine is changed, and the placement evaluation is calculated based on the assignment state of each machine, A method for determining machine placement, characterized by determining placement information data. 17. The machine placement determining method according to claim 16, wherein when the shape data of the factory including fault part data indicating a fault part in which a machine cannot be placed is given, the fault part data is taken into consideration, A machine allocation determining method, comprising allocating a machine to each virtual block obtained by dividing the machine. 18. A factory shape is divided into a predetermined number of virtual blocks, and allocation standard information data in which a machine is allocated to each of the divided virtual blocks is stored for each shape data of the factory. Is stored as machine-to-machine similarity data, the similarity between the shape data of each factory is stored as shape similarity data, and the shape data of the machine and factory whose layout is desired to be determined is input as data to be operated. Then, using the machine similarity and the shape data similarity of the operated data, the similarity with the allocation standard information data is obtained, and the machine arrangement improvement plan candidate data is specified. A method for determining the machine layout, characterized in that the layout evaluation is requested for the improvement plan candidate data and the machine layout is determined based on the layout evaluation. 19. The machine layout determining method according to claim 18, wherein the machine layout improving plan candidate data is matched with a constraint condition determined by the operated data before obtaining a layout evaluation. A method for determining a machine layout, characterized by correcting candidate improvement data. 20. The machine placement determining method according to claim 19, wherein, with respect to the corrected machine placement improvement plan candidate data, the machine assigned to the virtual block is perturbed to obtain post-perturbation candidate data. Machine placement determination method.