1. A production line bottleneck identification method is characterized by comprising the following steps:

step S1, acquiring production line data, wherein the production line data comprises a process sequence, process division, the number of times of statistics of each process and an operation priority matrix;

step S2, obtaining the operation priority according to the operation priority matrix, and dividing the process set according to the operation priority to determine the equipment operation process;

step S3, calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time respectively;

and step S4, calculating the load of the equipment on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the load of the equipment on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

2. The method according to claim 1, wherein the step of obtaining production line data comprises: collecting production line data, and preprocessing the production line data; and determining the production line process steps according to the pre-processed production line data, and counting each process time in the production line process steps.

3. The production line bottleneck identification method according to claim 1 or 2, further comprising: judging whether the process statistical time is within a preset operation time range by utilizing a triple standard deviation method, and if so, ensuring that the process statistical time is reliable; if not, the process statistics time is abnormal.

4. The production line bottleneck identification method according to claim 1 or 2, wherein the process statistic times are calculated by using the following formula, and the reliability of the process statistic times is calculated by adopting an error bound method;

in the formula, N is the lowest observation frequency, N is the current measurement frequency, t_iThe time value of the ith (i-1, 2, …, n) measurement is shown.

5. The method for identifying a production line bottleneck as recited in claim 1, further comprising: calculating the standard operation time of the production line by adopting the following formula:

TS(S_k)＝T(S_k)·γ·(1+θ)

in the formula, TS (S)_k) Standard operating time for the production line, T (S)_k) The working time of the kth (k is 1,2, …, m) device; gamma is a scale coefficient; θ is the magnification.

6. The method for identifying a production line bottleneck as recited in claim 1, further comprising: and taking a plurality of devices of the same type as a whole, evaluating the expected production load of the whole and calculating the actual production load of the whole, comprehensively considering the production loads of the devices of the upper link and the lower link of the whole, and calculating the production load of the whole.

7. The production line bottleneck identification method according to claim 1 or 6, wherein the load of each equipment of the production line is calculated by adopting the following formula:

ε＝L[M(σ_n)+1]-L[M(σ_n)-1]

in the formula, M different devices M in the assembly production line belong to {1, 2.. and M }, and the product is subjected to N assembly processes N belong to {1, 2.. and N }, and M (sigma.. and M) belongs to {1, 2.. and N }_n) J is the number of M devices of the same type,is a device M (sigma)_n) In practice, the actual processing time is,is a device M (sigma)_n) In practice, the time of the actual preparation,is a device M (sigma)_n) The maximum available working hours is as high as possible,is a device M (sigma)_n) In practice, the capacity of the machine is actually processed,is a device M (sigma)_n) Upper maximum total volume, epsilon being shadowCoefficient of response, L [ M (σ)_n)+1]Is a device M (sigma)_n) Production load of preceding adjacent process integrated equipment, L [ M (sigma) ]_n)-1]Is a device M (sigma)_n) Production load of post-adjacent process integration equipment, L [ M (σ)_n)]The whole equipment with the maximum production load value is the bottleneck of the production line.

8. The method for identifying a production line bottleneck as recited in claim 1, further comprising: according to the sequence of the working procedures in the equipment, the working time of each equipment is calculated by adopting the following formula;

in the formula, T (S)_k) Is the working time of the kth (k ═ 1,2, …, m) equipment, T_pThe time for the p (p ═ 1,2, …, n) th process of the k-th plant.

9. The method for identifying the bottleneck in the production line according to claim 1, wherein the step S2 is executed in no order with the step S3.

10. A production line bottleneck identification system, the system comprising:

the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring production line data, and the production line data comprises a process sequence, process division, the statistical times of each process and an operation priority matrix;

the procedure production module is used for obtaining the operation priority according to the operation priority matrix and dividing a procedure set according to the operation priority so as to determine the equipment operation procedure;

the calculation module is used for respectively calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time;

and the bottleneck identification module is used for calculating the load of the equipment on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the load of the equipment on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

Background

The production resources of intelligent manufacturing are limited, and the manufacturing system has dynamics and equipment mutual restriction, so that the maximum production of an intelligent production line has a bottleneck phenomenon. The Bottleneck (bottleeck) can limit the output speed of the production line and also can affect the efficiency of the production capacity of other links.

At present, the traditional bottleneck identification methods are mainly divided into two types: one is to use the appearance characteristics of the equipment as the basis of the bottleneck identification of the production line, namely, the representation of the production line equipment is used as the basis of the bottleneck identification; the other method is to take the internal characteristics of the equipment as the basis for identifying the production line bottleneck, and mainly consider the internal factors, the processing condition and the scheduling scheme which cause the production bottleneck of the production line. However, the existing production line is transited from a serial structure to a serial-parallel hybrid structure, and the production line is changed from a single line to a hybrid line, which means that the production line is dynamic, and these methods only consider the static load of the equipment, and cut the equipment from the production line, and cannot take care of all conditions of the whole production line, that is, do not consider the defect problems of the dynamic load of the adjacent equipment and the like. Therefore, a method for identifying the bottleneck of the production line is needed to adapt to the static load and the dynamic load of the whole production line.

Disclosure of Invention

In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method and a system for identifying a bottleneck of a production line, which are used to solve the problem that the static load and the dynamic load of the whole production line cannot be adapted when the production line is dynamically changed in the prior art.

In order to achieve the above and other related objects, the present invention provides a method for identifying a bottleneck in a production line, comprising:

step S1, acquiring production line data, wherein the production line data comprises a process sequence, process division, the number of times of statistics of each process and an operation priority matrix;

step S2, obtaining the operation priority according to the operation priority matrix, and dividing the process set according to the operation priority to determine the equipment operation process;

step S3, calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time respectively;

and step S4, calculating the load of the equipment on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the load of the equipment on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

Another object of the present invention is to provide a production line bottleneck identification system, comprising:

the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring production line data, and the production line data comprises a process sequence, process division, the statistical times of each process and an operation priority matrix;

the procedure production module is used for obtaining the operation priority according to the operation priority matrix and dividing a procedure set according to the operation priority so as to determine the equipment operation procedure;

the calculation module is used for respectively calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time;

and the bottleneck identification module is used for calculating the equipment load on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the equipment load on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

As described above, the method and system for identifying the bottleneck of the production line of the present invention have the following advantages:

the invention regards a plurality of devices of the same kind as a whole device, not only calculates the production loads of different devices in the production line, namely the static loads, but also calculates the dynamic loads between adjacent devices, calculates the sum of the static loads of the devices on the production line and the dynamic loads between the adjacent devices, and takes the device with the largest sum of the production loads as the bottleneck of the production line, thereby improving the efficiency and the reliability of bottleneck identification of the production line, being beneficial to enterprises to make production plans and being beneficial to optimizing production targets.

Drawings

FIG. 1 is a flow chart of a method for identifying a bottleneck in a production line according to the present invention;

FIG. 2 is a diagram illustrating a bottleneck identification framework of a production line according to the present invention;

FIG. 3 is a diagram illustrating an example of a pre-process and post-process priority sequence according to the present invention;

FIG. 4 is a diagram illustrating a process division embodiment according to the present invention;

FIG. 5 is a diagram illustrating an embodiment of the reliability of time statistics provided by the present invention;

FIG. 6 is a diagram illustrating another embodiment of the reliability of time statistic according to the present invention;

FIG. 7 is a diagram illustrating an embodiment of standard operation time according to the present invention;

fig. 8 is a block diagram illustrating a bottleneck identification system of a production line according to the present invention.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.

Referring to fig. 1, a flowchart of a method for identifying a bottleneck in a production line according to the present invention includes:

step S1, acquiring production line data, wherein the production line data comprises a process sequence, process division, the number of times of statistics of each process and an operation priority matrix;

it should be noted that the process sequence, the process division, the statistical number of each process, and the job priority matrix are all initial data of the production line, that is, currently executed production data.

Step S2, obtaining the operation priority according to the operation priority matrix, and dividing the process set according to the operation priority to determine the equipment operation process;

the equipment operation process can refer to fig. 3;

step S3, calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time respectively;

for example, the operation time of each process is counted 3 times. Each process in the assembly line is a work unit. T is_iThe time of the i-th (i-1, 2, …, n) operation unit, that is, the process is shown.

It should be noted that, the execution sequence of step S2 and step S3 is not sequential, that is, both steps may be performed simultaneously.

And step S4, calculating the load of the equipment on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the load of the equipment on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

The method comprises the steps of taking a plurality of devices of the same type as a whole, evaluating the expected production load of the whole and calculating the actual production load of the whole, comprehensively considering the production loads of the devices of the upper link and the lower link of the whole, and calculating the production load of the whole.

In this embodiment, a plurality of devices of the same type are regarded as a whole device, which not only calculates the production loads, i.e., static loads, of different devices in the production line, but also calculates the dynamic loads between adjacent devices, calculates the sum of the static loads of the devices on the production line and the dynamic loads between the adjacent devices, and uses the device with the largest sum of the production loads as the bottleneck of the production line, thereby improving the efficiency and reliability of bottleneck identification of the production line, facilitating enterprise to make a production plan, and also facilitating optimization of production targets.

In another embodiment, please refer to fig. 2, which is a production line bottleneck identification frame diagram according to the present invention, the difference between the embodiments is that:

in step S1, production line data is collected, which includes a sequence of processes (see fig. 3 for details, the execution processes are arranged in sequence, and have a series structure, a parallel structure, etc.), process division, the number of times of statistics for each process, and an operation priority matrix; the production line data is preprocessed (for example, the preprocessing comprises a plurality of methods, such as data cleaning, data integration, data transformation, data reduction and the like, and the preprocessing technologies are used before data mining, so that the quality of a data mining mode is greatly improved, and the time required by actual mining is reduced); determining the process steps of the production line according to the pre-processed production line data (see fig. 4 for details, including the workstation serial number, the process serial number, and the execution content corresponding to each process serial number), and counting the process time in the process steps of the production line.

In step S4, the equipment load model, for example, a combined load model (including a static load model and a dynamic load model), a linear differential equation load model, and here the combined load model is prioritized.

The beat, the processing time, the preparation time, the available time, the processing capacity and the maximum capacity of the production line can be calculated through the equipment load model, the production load of the equipment is obtained according to the parameters and a production load calculation formula, the production load (namely, dynamic load) of the equipment in the upper link and the lower link is comprehensively considered, and an influence coefficient epsilon is introduced. By selecting the equipment corresponding to the maximum production load as the bottleneck identification method of the assembly production line, the bottleneck of the assembly production line can be effectively and quickly identified, the reliability of bottleneck identification is improved, and a target is provided for the optimization of subsequent enterprise production.

In other embodiments, the process statistics reliability is calculated by using the following formula and using an error bound method;

in the formula, N is the lowest observation frequency, N is the current measurement frequency, t_iThe time value of the ith (i-1, 2, …, n) measurement is shown.

Specifically, please refer to fig. 5, which is a diagram illustrating an embodiment of a time counting time reliability according to the present invention, for example, a reliability of counting process times; because the time of each process operation is not completely determined, the average operation time of the process needs to be calculated through multiple statistical mean values, in order to obtain accurate and reliable assembly line time data, firstly, reliability analysis is carried out on the process statistical frequency, if the statistical process statistical frequency is too much, the labor cost is increased, if the statistical process statistical frequency is too little, the reliability of the data is insufficient, and therefore, whether the process statistical frequency meets the requirement or not must be judged. The reliability analysis of the process statistics frequency can adopt an error margin method, when the accuracy is +/-5 percent and the confidence coefficient is 95 percent, the process statistics frequency is calculated by adopting the formula, and the accuracy of the process statistics frequency can be ensured.

For example, the three times of operation time of the equipment under each process sequence number is measured through each process sequence number, the first power of the three times of operation time is summed, the second power is summed, and the second power of the first power is calculated after the first power is summed integrally, so that the current lowest observation times are calculated according to the formula.

In other embodiments, a triple standard deviation method is used to determine whether the process statistical time is within a preset operation time range, and if so, the process statistical time is reliable; if not, the process statistics time is abnormal.

Please refer to fig. 6, which is a diagram of another embodiment of the time statistic data reliability provided by the present invention, for example, the time reliability is counted in the calculation process; after counting the operation time of the operation unit, since the measurement of the time of each process operation is not completely consistent, it is necessary to count a plurality of times, and the average operation time of the process is obtained as the operation time T of the process by mean calculation_iTherefore, the accuracy of the observation time is especially important, and the average working time is calculated by each measurement and counted by the time t_iDetermined, this is needed for t_iIs judged, and t of each operation unit is judged_iWhether an outlier exists.

For example, the reliability of the calculated data is mainly calculated by the reliability of the statistical time of the process, at present, the statistical measurement of the data is usually manual measurement, and obviously abnormal data can be removed manually, but the subjectivity of the method is too large, and the reliability of the data is difficult to guarantee. Therefore, a classical abnormal data removing method, namely a three-time standard deviation method (method), is adopted, and the main calculation formula is as follows:

wherein:the average value of three-time operation time measurement of each operation unit is referred to; n represents the number of present measurements, where n is 3; t is t_iRepresents the time value of the i (i ═ 1,2, …, n) th measurement.Normal valueShould be within + -3 sigma, i.e. the mean valueNot at the preset timeWithin the range, the average valueIs determined to be abnormal; mean value ofAt a preset timeWithin the range, the average valueIt is determined that the operation time is reliable (normal).

Specifically, according to the process number, the process counting time of each station number is three times, namely, three times are measured, the standard deviation of the three times of measurement process counting time is calculated, a preset time range formed by subtracting three times of the standard deviation from the process counting time mean and adding three times of the standard deviation from the process counting time mean is obtained, and if the process counting time of each measurement is within the preset time range, the measured process counting time is normal, namely, the reliability is high; otherwise, it is abnormal.

By means of the method, the influence of artificial subjective factors can be effectively avoided, and meanwhile, the accuracy of obtaining the process statistical time is improved.

In other embodiments, please refer to fig. 7, which is a diagram of an embodiment of a standard operation time according to the present invention, for example, the standard operation time of a production line is calculated by the following formula:

TS(S_k)＝T(Sk)·γ·(1+θ)

in the formula, TS (S)_k) Standard operating time of the production line, T (S)_k) The kth (k ═ 1,2, …, m) plantThe working time of (c); -a gamma-scale factor; theta-broadening ratio.

The standard operation time refers to the time consumed by a skilled worker to produce a unit of qualified product according to a specified operation standard in a certain production environment.

Specifically, the evaluation coefficient and the enlargement ratio may be set according to the demand, for example, the evaluation coefficient is a coefficient that corrects the difference in the normal operation time. Because workers and machines need a certain rest time, a certain amount of relaxation time is added on the basis of the working time, the amount of the relaxation time is determined by a company, and the size of the relaxation time is reflected by the relaxation rate.

As shown in fig. 7, the process numbers corresponding to the workstation numbers, the process contents specifically executed by each process number, and the mean time of the operation specifically corresponding to the equipment under each process number are countedWorking time of the device T (S)_k) And standard operation time TS (S)_k)。

In the embodiment, the normal operation conditions, proficiency, operation method, labor intensity and speed and quality standard on the production line can be reflected through the standard operation time; production plans and personnel man-hour plans can be made through standard operation time, so that optimization and formulation of subsequent process schemes are facilitated.

In other embodiments, the load of each piece of equipment is calculated, there are M different pieces of equipment M e {1, 2.., M } in the assembly line, and the product is subjected to N pieces of assembly processes N e {1, 2.., N }, M (σ)_n) Representing a set of process steps on a plant M, j being the number of M same kind of plants, so that the plant M (σ)_n) Production load L (σ)_n) Comprises the following steps:

ε＝L[M(σ_n)+1]-L[M(σ_n)-1]

in the formula (I), the compound is shown in the specification,the assembly production line has M different devices M belonging to {1, 2., M }, and the products are subjected to N assembly processes N belonging to {1, 2., N }, M (sigma., M) }_n) J is the number of M devices of the same type,is a device M (sigma)_n) In practice, the actual processing time is,is a device M (sigma)_n) In practice, the time of the actual preparation,is a device M (sigma)_n) The maximum available working hours is as high as possible,is a device M (sigma)_n) In practice, the capacity of the machine is actually processed,is a device M (sigma)_n) Upper maximum total capacity, ε is the coefficient of influence, L [ M (σ)_n)+1]Is a device M (sigma)_n) Production load of preceding adjacent process integrated equipment, L [ M (sigma) ]_n)-1]Is a device M (sigma)_n) Production load of post-adjacent process integration equipment, L [ M (σ)_n)]The whole equipment with the maximum production load value is the bottleneck of the production line.

Thus, if and only if L [ M (σ)_n)]The largest whole equipment is identified as the bottleneck of the production line, for example, the parameters corresponding to the actual capacity of the equipment (And) Parameters corresponding to expected capabilities (And) Therefore, the actual static load of the equipment is obtained, the production load (namely, dynamic load) of the equipment in the upper link and the lower link is comprehensively considered, and an influence coefficient epsilon is introduced.

It should be noted that, in the case where a plurality of identical devices are regarded as a whole, when calculating the dynamic loads between the adjacent devices in the whole, the sum of the dynamic loads between the plurality of identical devices and the adjacent devices should be considered, and by accurately calculating the dynamic loads between the whole device and the adjacent devices, the whole device can be calculated more accurately.

By selecting the equipment corresponding to the maximum production load as the bottleneck identification method of the assembly production line, the bottleneck of the assembly production line can be effectively and quickly identified, the reliability of bottleneck identification is improved, and a target is provided for the optimization of subsequent enterprise production.

Referring to fig. 8, a block diagram of a production line bottleneck identification system provided in the present invention includes:

the system comprises a data acquisition module 1, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring production line data, and the production line data comprises a process sequence, process division, the number of times of statistics of each process and an operation priority matrix;

the procedure production module 2 is used for obtaining the operation priority according to the operation priority matrix and dividing the procedure set according to the operation priority so as to determine the equipment operation procedure;

the calculating module 3 is used for respectively calculating the reliability of the process counting times, the reliability of the process counting time and the standard operation time;

and the bottleneck identification module 4 is used for calculating the equipment load on the production line by using the equipment load model, calculating the dynamic load between adjacent equipment, calculating the sum of the equipment load on the production line and the dynamic load between the adjacent equipment, and taking the maximum value of the production load as the bottleneck of the whole production line.

It should be noted that the production line bottleneck identification system and the production line bottleneck identification method are in a one-to-one correspondence relationship, and the corresponding technical details and technical effects refer to the production line bottleneck identification method, which is not described herein in detail.

In summary, the present invention regards a plurality of devices of the same type as a whole device, not only calculates the production loads of different devices in the production line, i.e. the static loads, but also calculates the dynamic loads between adjacent devices, calculates the sum of the static loads of the devices on the production line and the dynamic loads between the adjacent devices, and uses the device with the largest sum of the production loads as the bottleneck of the production line, thereby improving the efficiency and reliability of bottleneck identification of the production line, facilitating enterprise to make a production plan, and also facilitating optimization of production targets. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.

The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.