CN113568385B - Production scheduling method based on multi-variety mixed flow assembly mode - Google Patents
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Abstract
The invention provides a production scheduling method based on a multi-variety mixed flow assembly mode, which is carried out according to the following steps: 1) collecting annual output data of various products to perform P-Q analysis, 2) obtaining the product with the highest annual output according to the P-Q analysis, drawing an assembly flow chart of the product, 3) optimizing the process and balancing stations by using an ECRS four-principle question asking method under the flow chart, 4) solving an optimal production scheduling scheme of the mixed flow assembly line by using a genetic algorithm with the shortest assembly cycle time as a target, and 5) analyzing results; the production scheduling scheme of the mixed flow assembly line aiming at the shortest assembly cycle time of an enterprise can be obtained through the steps. The method is based on a multi-variety mixed-flow assembly mode, assembly line balance is carried out, a genetic algorithm model is constructed by taking the shortest assembly cycle time as a target, and an optimal scheduling scheme of the mixed-flow assembly line is found out. The scheduling scheme obtained by the invention can objectively reduce the assembly cycle time, effectively shorten the delivery time of the enterprise order and improve the production efficiency of the enterprise.
Description
Technical Field
The invention relates to an enterprise assembly production scheduling method, in particular to a production scheduling method based on a multi-variety mixed flow assembly mode.
Background
Nowadays, enterprises are very competitive, different types of products meeting the diversified demands of the market need to be produced in order to stabilize the market, and meanwhile, the delivery date of the products also needs to be met, so that the method is very important for assembly line scheduling. At present, an assembly line determines production scheduling according to order time and order quantity, and the phenomenon of queue insertion and order scheduling disorder exists in the mode. To meet the diversified market demands and product delivery dates, a mixed flow assembly method has to be adopted.
Because different models of products are assembled and have similar process flows and production structures, the die changing time of the automatic assembly part is also optimized to a certain extent, and necessary conditions are provided for the implementation of a mixed flow assembly mode. The assembly mode can better adapt to the market with fierce competition at present, improves the competitiveness of enterprises, but increases the difficulty of production task scheduling in workshops when in use. The scheduling is a key for controlling the workshop production efficiency, and influences the utilization rate of enterprise equipment, the die change time, the material flow and other aspects, so that the good scheduling plan can better perform effective management on a workshop assembly line.
Disclosure of Invention
The invention aims to provide a production scheduling method based on a multi-variety mixed flow assembly mode. The method starts from an enterprise assembly line, performs P-Q analysis on the types and the number of products of the assembly line, performs assembly line balance on key products, uses a genetic algorithm to solve the optimal scheduling scheme of the mixed-flow assembly line, and provides a reasonable assembly scheme for the enterprise assembly line.
In order to achieve the above purpose, the invention adopts the following scheme:
a production scheduling method based on a multi-variety mixed flow assembly mode comprises the following scheme:
the method comprises the following steps: according to product P-Q analysis
And collecting information such as product types and annual loading quantities of an enterprise assembly line, drawing a P-Q diagram, and finding out key products.
Step two: drawing a flow chart
And (4) according to the key product obtained in the step one, determining the information of the key product, such as the assembly flow, personnel, time, required stations and the like, and drawing an assembly flow chart.
Step three: assembly line balance
And (4) according to the assembly flow diagram obtained in the step two, canceling, combining, recombining and optimizing the processes in the assembly flow by using an ECRS four-principle question technique to improve the assembly flow, and dividing the improved processes according to time to balance the assembly line as much as possible.
Step four: method for solving optimal scheduling scheme of mixed-flow assembly line by using genetic algorithm
After the assembly line is balanced through the third step, a typical genetic algorithm is designed to solve the optimal scheduling scheme of the mixed flow assembly line, and the specific steps are as follows:
(1) Determining the gene coding mode.
Different types of products are assembled according to an assembly line, and the product numbers of the same model are represented by real numbers by using the characters corresponding to the model numbers of the products. Each product put into production corresponds to a gene sequence position, and the length of the gene string is consistent with the number of products in each production cycle. The initial population was generated using a completely random method.
(2) And designing an objective function.
Scheduling problems based on man-hours generally target the shortest production cycle time. Because the work time of the work stations of different types of products is fixed, the shortest production cycle period is taken as the scheduling optimization target. Hypothetical assembly lineProduce M different series of products, wherein the demand ratio is x1: x2: x3:
and sequencing the products. The following function is constructed according to the optimization objective:
MinT＝max(KM(N-1)，K(N-1)M)+tMN+GMN (1)
K1y＝K1(y-1+t1y (2)
Kx1＝K(x-1)1+tx1 (3)
Kxy＝max(K(x-1)y，Kx(y-1))+txy (4)
(x＝1，2，...，M；y＝1，2，...，N)
in the formula, x is the serial number of the sequence position; y is the station number; m is the total number of products in the assembly cycle period; n is the number of assembly line stations; kxy is the assembly completion time, s, of the product at the x-th position at the y-th station; txy is the working time, s, of the product at the x position at the y station; and GMN is the mold changing time of the product on the M position at the Nth station. Wherein the objective function (1) represents that the cycle time of the production cycle is shortest; formula (2) shows that under a certain scheduling mode, the time for completing the assembly of the 1 st product at the y-th station is equal to the finishing time of the product when completing the assembly at the (y-1) th station plus the working time of the product at the y-th station; formula (3) shows that the assembly time of the product at the x-th position for completing the 1 st station is equal to the end time of the product at the (x-1) th position for completing the assembly at the 1 st station plus the working time of the product at the 1 st station; equation (4) indicates that the end time when the product at the x-th position completes the assembly at the y-th station is equal to the larger of the working time of the product at the y-th station plus the end time when the product at the (y-1) th station completes the assembly and the end time when the product at the (x-1) th position enters the assembly line to complete the assembly at the y-th station.
(3) A fitness function.
The fitness of the genetic algorithm is used in the using process to identify the individual quality degree of the population and is used as a basis. For different individuals in the population, each of the individuals represents a feasible solution in an actual problem, the fitness function obtains function values corresponding to the individuals in a mapping mode, and the function values represent the fitness. The survival rate of the individual in the evolution process is also determined by the fitness, and meanwhile, the convergence speed of the algorithm is also influenced by the fitness value. The general fitness function is converted by an objective function.
The objective function is a minimization optimization model, the target values are all non-negative, and the fitness is larger and the individual is better for evolution, so the fitness function is in inverse proportion to the objective function. When the fitness function value is larger, the individual fitness is higher, the objective function value is smaller, and correspondingly, the completion time of all products put into production in the period is smaller.
(4) And (4) genetic operator design.
The selection of genetic operators determines which chromosomes become the next generation candidate set, and it is expected that the next generation will possess the superiority of the previous generation in the population, so that the more superior chromosomes will more readily become the next generation candidate set. The russian roulette method is used herein as an alternative.
On the premise of ensuring the basic characteristics of the population, the crossover and mutation can obtain chromosomes with better quality. The crossover operation is the selection of two pairs of chromosomes and then crossover each pair of randomly selected crossover points. The mutation operation can enable gene individuals to show new characteristics, enable the genetic algorithm to jump out of local search, and avoid the algorithm from obtaining a local optimal solution after being premature. The partial mapping cross mode and the random variation mode are selected.
(5) And substituting the actual product assembly sequence and the actual product assembly quantity as initial population into an algorithm model to solve to obtain an optimal scheme.
Step five: analysis of results
And D, comparing the assembly sequence of the product obtained in the step four with the assembly cycle time, efficiency, order delivery and the like of the initial assembly sequence, and substituting the obtained assembly sequence into the actual assembly production.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a buffer production chronology chart
FIG. 3 is a flow chart of 210C buffer assembly
FIG. 4 is an assembly line station timing diagram
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without any creative effort based on the embodiments of the present invention belong to the protection scope of the present invention.
The invention discloses a production scheduling method based on a multi-variety mixed flow assembly mode, which comprises the following steps of:
the method comprises the following steps: according to product P-Q analysis
Collecting information such as product types and annual loading quantities of an assembly line of an elevator buffer of an enterprise, drawing an annual output quantity map of the assembly line, and finding out key products. As shown in fig. 2, is an annual capacity map of an elevator buffer.
Step two: drawing a flow chart
And (4) according to the step one, the key product is a 210C buffer, the information of the assembly flow, personnel, time, required stations and the like of the product is clarified, and an assembly flow chart is drawn. As shown in fig. 3, a flow chart for 210C buffer assembly is shown.
Step three: assembly line balance
According to the assembly flow chart obtained in the second step, the procedures in the assembly flow are cancelled, combined, recombined and simplified by using an ECRS four-principle question asking technology, so that the assembly flow is improved, and the specific question asking details are shown in Table 1
Table 1 ECRS four major principle optimization table
And the improved working procedures are divided according to time, so that the assembly line is balanced as much as possible. FIG. 4 shows a timing diagram of the post-stage 210C buffer equilibration.
Step four: method for solving optimal scheduling scheme of mixed-flow assembly line by using genetic algorithm
After the assembly line is balanced through the third step, a typical genetic algorithm is designed to solve the optimal scheduling scheme of the mixed flow assembly line, and the specific steps are as follows:
(1) Determining the gene coding mode.
The buffer is divided into 9 products such as 80B, 80F, 80H0, 175A and the like according to the inner diameter and the length. Different products are indicated by corresponding product type characters (wherein 80B ao si, 175A au are indicated as 80BA and 175 AA), and product numbers of the same type are indicated by real numbers. If the total number of the 80B products is a, and the total number of the 175A Olympics is B, the corresponding codes are 80B-1, 80B-2, 80B-3, …, 80B-a,175AB-1, 175AB-2, … and 175AB-B. Each product put into production corresponds to a gene sequence position, and the length of the gene string is consistent with the number of products in each production cycle. The initial population was generated using a completely random method.
(2) And designing an objective function.
The scheduling problem based on man-hours is generally targeted to the shortest cycle time and a certain mold change time. As the working time of the stations of the buffers of different models is fixed, the shortest cycle time of production is taken as a scheduling optimization target. Assuming that the assembly line produces M different series of buffers, where the demand ratio is x1: x2: x3
And sequencing the products. The following functions are constructed according to the optimization objective:
MinT＝max(KM(N-1)，K(N-1)M)+tMN+GMN (1)
K1y＝K1(y-1+t1y (2)
Kx1＝K(x-1)1+tx1 (3)
Kxy＝max(K(x-1)y，Kx(y-1))+txy (4)
(x＝1，2，...，M；y＝1，2，...，N)
in the formula, x is the serial number of the sequence position; y is the station number; m is the total number of products in the assembly cycle period; n is the number of assembly line stations; kxy is the assembly completion time, s, of the product at the x-th position at the y-th station; txy is the working time, s, of the product at the x position at the y station; and GMN is the mold changing time of the product on the M position at the Nth station. Wherein the objective function (1) represents that the cycle time of the production cycle is shortest; formula (2) shows that under a certain scheduling mode, the time for the 1 st product to finish assembling at the y-th station is equal to the finishing time of the product when the product finishes assembling at the (y-1) th station plus the working time of the product at the y-th station; formula (3) shows that the assembly time of the product at the x-th position for completing the 1 st station is equal to the end time of the product at the (x-1) th position for completing the assembly at the 1 st station plus the working time of the product at the 1 st station; equation (4) indicates that the end time when the product at the x-th position completes the assembly at the y-th station is equal to the larger of the working time of the product at the y-th station plus the end time when the product at the (y-1) -th station completes the assembly at the y-th station and the end time when the product at the (x-1) -th position enters the assembly line to complete the assembly at the y-th station.
(3) A fitness function.
The genetic algorithm uses the fitness to identify the individual quality degree of the population in the using process and is used as a basis. For different individuals in the population, each of the individuals represents a feasible solution in an actual problem, the fitness function obtains function values corresponding to the individuals in a mapping mode, and the function values represent the fitness. The survival rate of the individual in the evolution process is also determined by the fitness, and meanwhile, the convergence speed of the algorithm is also influenced by the fitness value. The general fitness function is converted by an objective function.
The objective function is a minimum optimization model, the target values are all non-negative, and for evolution, the larger the fitness is, the better the individual is, so the fitness function is in an inverse relation with the objective function. When the fitness function value is larger, the individual fitness is higher, the objective function value is smaller, and correspondingly, the completion time of all products put into production in the period is smaller.
(4) And (4) genetic operator design.
The selection of genetic operators determines which chromosomes become the next generation candidate set, and it is expected that the next generation will possess the superiority of the previous generation in the population, so that the more superior chromosomes will more readily become the next generation candidate set. The russian roulette method is used as an alternative.
On the premise of ensuring the basic characteristics of the population, crossover and mutation can obtain chromosomes with better quality. The crossover operation is the selection of two pairs of chromosomes and then crossover each pair of randomly selected crossover points. The mutation operation can enable gene individuals to show new characteristics, enable the genetic algorithm to jump out of local search, and avoid the algorithm from obtaining a local optimal solution after being premature. The partial mapping cross mode and the random variation mode are selected.
(5) According to 3400 assembly amount in half a month of an assembly line, in a simulation experiment, one chromosome consists of 3400 genes and is respectively represented by 210C1, 210C-2, 210. And 210C-2000;80B-1, a. The production of the initial population was done by a completely random method. According to the method, the shortest minimum assembly cycle time and certain mould changing time are taken as targets, the initial conditions of the genetic algorithm are that the generation number is 30, the generation size is 3400, the cross probability is 0.9 and the variation probability is 0.1, corresponding statistical data are generated, and then the required objective function value is obtained.
Step five: analysis of results
The optimal scheme obtained by the fourth step is 210C-1,. And 210C-1200;175AB-1, 175AB-200;80B-1,. And 80B-100;80AB-1,. And 80AB-100;80B-101, 80B-400;175F-1,. Or 175F-100;80B-401, 80B-500;80H-1,. And 80H-100; 210C-1201.., 210C-2000;175H-1,. Or.175H-100; 175A-1, 175A-200, with an optimal fitness of about 5 days, 15 hours, 45 minutes, 21 seconds, and a lead time reduced by about 9 days compared to the initial assembly sequence. This assembly sequence can be applied to actual production.
Claims (1)
1. A production scheduling method based on a multi-variety mixed flow assembly mode is characterized by comprising the following steps:
the method comprises the following steps: P-Q analysis from the product
Collecting product types and annual assembly quantity information of an enterprise assembly line, drawing a P-Q diagram, and finding out key products;
step two: drawing a flow chart
According to the key product obtained in the first step, the assembly process, personnel, time and required station information of the key product are determined, and an assembly process diagram is drawn;
step three: assembly line balancing
According to the assembly flow chart obtained in the second step, procedures in the assembly flow are cancelled, combined, recombined and optimized by using an ECRS four-principle question asking technology so that an assembly line is balanced;
step four: and (3) solving the optimal scheduling scheme of the mixed flow assembly line by using a genetic algorithm, and designing a typical genetic algorithm to solve the optimal scheduling scheme of the mixed flow assembly line after balancing the assembly line by the third step, wherein the specific steps are as follows:
(1) Determination of Gene coding Pattern
Assembling different types of products according to an assembly line, expressing the product numbers of the same type by using the corresponding product type characters of different products by using real numbers, corresponding a gene sequence position to each product put into production, enabling the length of a gene string to be consistent with the number of the products in each production cycle period, and generating an initial population by adopting a completely random method;
(2) Design of objective function
The scheduling problem based on the working hours generally sets the target as the shortest production cycle period time, because the working time of stations of different types of products is fixed, the shortest production cycle period time is taken as the scheduling optimization target, and the assembly line is supposed to produce M different series of products, wherein the demand ratio is x 1 :x 2 :x 3 :...:x m In the actual assembly process, only the pair is needed
Arranging each productAnd (3) sequentially constructing the following functions according to the optimization target:
MinT＝max(K M(N-1) ，K (N-1)M )+t MN +G MN (1)
K 1y ＝K 1(y-1) +t 1y (2)
K x1 ＝K (x-1)1 +t x1 (3)
K xy ＝max(K (x-1)y ，K x(y-1) )+t xy (4)
(x＝1，2，...，M；y＝1，2，...，N)
in the formula: x is the sequence position number; y is the station number; m is the total number of products in the assembly cycle period; n is the number of assembly line stations; k xy The assembly completion time of the product at the x position at the y station is s; t is t xy The working time of the product on the x position at the y station, s; g MN The die change time of the product on the M position at the Nth station;
wherein the objective function (1) represents that the cycle time of the production cycle is shortest; formula (2) shows that under a certain scheduling mode, the time for the 1 st product to finish assembling at the y-th station is equal to the finishing time of the product when the product finishes assembling at the (y-1) th station plus the working time of the product at the y-th station; formula (3) shows that the assembly time of the product at the x-th position for completing the 1 st station is equal to the end time of the product at the (x-1) th position for completing the assembly at the 1 st station plus the working time of the product at the 1 st station; equation (4) shows that the end time when the product at the x-th position completes the assembly of the y-th station is equal to the larger of the operation time of the product at the y-th station plus the end time when the product at the (y-1) th station completes the assembly and the end time when the product at the (x-1) th position enters the assembly line to complete the assembly of the y-th station;
(3) Fitness function
The fitness of the genetic algorithm is used in the using process to identify the individual quality degree of the population and is used as a basis; for different individuals in a population, each individual represents a feasible solution in an actual problem, the fitness function obtains function values corresponding to the individuals in a mapping mode, the function values represent the fitness, the fitness also determines the survival rate of the individuals in the evolution process, meanwhile, the convergence speed of the algorithm is also influenced by the fitness value, and the general fitness function is converted by a target function;
(4) Genetic operator design
The selection of genetic operators determines the candidate set of chromosomes to become the next generation, and the next generation is expected to have the superiority of the previous generation in the population, and the more excellent chromosomes are easy to become the next generation candidate set, so that the Russian wheel disc method is adopted as a selection mode;
(5) And step five, substituting the actual assembly sequence and the actual number of the products as initial population into an algorithm to solve to obtain an optimal scheme: analysis of results
And D, comparing the product assembly sequence obtained in the step four with the assembly cycle time, efficiency and order delivery of the initial assembly sequence, and substituting the obtained assembly sequence into the actual assembly production.
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