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CN112950101B - Automobile factory mixed flow line production scheduling method based on yield prediction - Google Patents

Automobile factory mixed flow line production scheduling method based on yield prediction Download PDF

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CN112950101B
CN112950101B CN202110520213.7A CN202110520213A CN112950101B CN 112950101 B CN112950101 B CN 112950101B CN 202110520213 A CN202110520213 A CN 202110520213A CN 112950101 B CN112950101 B CN 112950101B
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李瑞瑞
金楚欣
赵伟
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Beijing Futong Oriental Technology Co ltd
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Abstract

The invention discloses a mixed flow line production arrangement method of an automobile factory based on yield prediction, which comprises the steps of obtaining the demand of products of various types in a historical period; predicting the production number of the products of each model of the mixed flow line in the batch by a time series prediction method; establishing a scheduling optimization model; solving the model to obtain a mixed flow line production plan of the batch; and then the product is arranged to the machine according to the rule by the scheduling plan. Through the mode, the production scheduling method has good adaptability to production scenes with unclear demand through yield prediction based on historical data, reduces production cost while meeting production efficiency through a production scheduling result, and can improve the operation efficiency of the mixed flow line through a pre-production mode.

Description

Automobile factory mixed flow line production scheduling method based on yield prediction
Technical Field
The invention relates to the technical field of manufacturing and information, in particular to a production scheduling method of a mixed flow line of an automobile factory based on yield prediction.
Background
The mixed flow line refers to a production line for producing various types of products, and the production mode is widely applied to the production of automobile parts. The traditional method for manually arranging the production plan is difficult to meet the large-scale and multi-constraint production scene, and the key research problem of how to arrange the production plan to improve the production efficiency of the machine, reduce the running cost of the machine and avoid that a large amount of stock becomes mixed flow line production scheduling is solved.
In the production process of the mixed flow line, various optimization targets and constraint conditions need to be considered, wherein the optimization targets are that the mold changing cost is minimum, the color changing cost is minimum, the bracket changing cost is minimum and the like; the constraint conditions include the number of machines, the number of molds, the matching constraint of the machines and the molds, and the like.
In the existing mixed flow line production scheduling method, the demand quantity of products of various models is generally considered, and a heuristic algorithm is used for solving an optimal production plan with the goals of minimum completion time, minimum production cost and the like as targets. However, in practical applications, the following problems often occur:
(1) the demand of each model product is not clear before a factory arranges a production plan, and the demand of each model product cannot be accurately predicted through the experience of production scheduling personnel, so that the capacity is wasted.
(2) The branch lines cannot be optimized simultaneously, and the heuristic method has low solving quality.
In order to solve the problems, the invention discloses a mixed flow line production arrangement method of an automobile factory based on yield prediction.
Three-stage meta-heuristic search algorithm: the notice number is CN 111563636B, named as a three-stage meta-heuristic optimal solution search technology disclosed in a three-stage meta-heuristic parking space allocation optimization method.
The patent document with the publication number of CN 111913943A and the name of CN is a method and a system for acquiring and processing data suitable for automatic production scheduling of a factory, and provides a scheme for collecting batch production data and calculating a production scheduling task through a production scheduling calculation version manually created by a user, wherein the patent mainly improves the data processing and analysis in the flow of a production scheduling system, but a production scheduling calculation part is manually created by the user and has a certain use threshold; the patent publication No. CN 101923342B is a patent document named as a method for reducing the switching times of mixed-flow assembly line products of automobiles, and the aim of reducing the switching times of the mixed-flow assembly line products is fulfilled by combining an annular buffer zone with a mixed progressive multi-target genetic algorithm.
Disclosure of Invention
The invention provides a production prediction-based mixed flow line production scheduling method for an automobile factory, which can solve the problems that the existing production scheduling technology cannot accurately predict the demand of products of various types, so that the production capacity is wasted, various branch lines cannot be simultaneously optimized, and the solving quality is low.
In order to solve the technical problems, the invention adopts a technical scheme that: the production prediction-based mixed flow line production scheduling method for the automobile factory is characterized by comprising the following steps of:
step 1: acquiring the demand of each type of product in a historical period;
step 2: predicting the production number of products of various types of the mixed flow line in the batch by a time series prediction method;
further, the time series prediction method uses an ARMA model or an ARIMA model.
And step 3: establishing a scheduling optimization model, comprising:
step 3-1: determining the production period of the current batch and the production number of products of each model;
step 3-2: determining a scheduling optimization constraint function;
step 3-3: and determining a scheduling optimization objective function.
Further, the scheduling optimization constraint function includes:
a model change cost calculation function:
Figure 286556DEST_PATH_IMAGE001
；
color change cost calculation function:
Figure 266014DEST_PATH_IMAGE002
；
mold preparation function:
Figure 45751DEST_PATH_IMAGE003
Figure 440960DEST_PATH_IMAGE003
；
Figure 255332DEST_PATH_IMAGE004
Figure 77795DEST_PATH_IMAGE004
mold switching function:
Figure 420527DEST_PATH_IMAGE005
；
Figure 619427DEST_PATH_IMAGE006
；
wherein
Figure 288306DEST_PATH_IMAGE007
In order to change the cost factor of the mold,
Figure 78407DEST_PATH_IMAGE008
in order to change the color cost factor,
Figure 832737DEST_PATH_IMAGE009
the production number of each type of product in the batch is calculated;
Figure 569749DEST_PATH_IMAGE010
indicating the model of the mold used by each model of product;
Figure 93134DEST_PATH_IMAGE011
representing the color number corresponding to each model of product;
Figure 336028DEST_PATH_IMAGE012
when in position
Figure 577653DEST_PATH_IMAGE013
Product type of
Figure 852777DEST_PATH_IMAGE014
When the temperature of the water is higher than the set temperature,
Figure 496247DEST_PATH_IMAGE015
otherwise
Figure 831414DEST_PATH_IMAGE016
；
Figure 622652DEST_PATH_IMAGE017
Is a position
Figure 701467DEST_PATH_IMAGE013
Model of product mold, as
Figure 199444DEST_PATH_IMAGE013
The model of each product is not matched with the injection mold, namely when the mold needs to be replaced
Figure 971091DEST_PATH_IMAGE018
Otherwise
Figure 921730DEST_PATH_IMAGE019
；
Figure 617284DEST_PATH_IMAGE020
Is a position
Figure 969768DEST_PATH_IMAGE013
Color number of the product, when
Figure 912316DEST_PATH_IMAGE013
The model of each product is not matched with the current color of the spraying line, namely when the color needs to be changed
Figure 350251DEST_PATH_IMAGE021
Otherwise
Figure 770868DEST_PATH_IMAGE022
。
Further, the objective function is:
Figure 571334DEST_PATH_IMAGE023
；
wherein
Figure 684783DEST_PATH_IMAGE024
In order to be the weight coefficient of the objective function,
Figure 610014DEST_PATH_IMAGE025
、
Figure 834322DEST_PATH_IMAGE026
a function is calculated for the cost.
And 4, step 4: solving the model to obtain a mixed flow line production plan of the batch;
further, solving the model uses a three-stage meta-heuristic search method to solve the model.
And 5: the scheduling plan schedules the product to the machine according to the rules, including:
step 5-1: acquiring the number of each machine table of the mixed flow line of the batch;
step 5-2: calculating the starting time of the production line through an inventory allocation mechanism;
step 5-3: sequencing all the placeable platforms of the current product from small to large according to the quantity of the arranged products to obtain a sequence set, and arranging the current product on the first platform in the sequence set;
step 5-4: and (5) repeating the step (5-3) until all the products of each model of the batch are distributed to the machine.
Further, the inventory allocation mechanism includes:
starting an injection molding production line, and putting m products into stock so that enough products can be available when the spraying production line starts to run;
and m is calculated according to the production cycle and the capacity of different production lines:
Figure 895819DEST_PATH_IMAGE027
；
Figure 727640DEST_PATH_IMAGE028
；
wherein T is the production number of each model product in the batch,
Figure 140167DEST_PATH_IMAGE029
、
Figure 168165DEST_PATH_IMAGE030
respectively the number of products which can be processed in a spraying production line and an injection molding production line in unit time,
Figure 349748DEST_PATH_IMAGE031
in order to start the spraying production line,
Figure 539421DEST_PATH_IMAGE032
the starting time of the injection molding production line.
The invention has the beneficial effects that:
1. through the yield prediction based on historical data, the method has good adaptability to production scenes with unclear demand quantity;
2. three-stage meta-heuristic algorithm solving is introduced, a cost calculation function and a die switching function are comprehensively considered, and compared with a manual scheduling and heuristic scheduling method, the scheduling result meets the production efficiency and reduces the production cost;
3. and an inventory distribution mechanism is added, and the mixed flow line operation efficiency is improved in a pre-production mode.
Drawings
FIG. 1 is a flow chart of a method for scheduling production of mixed flow lines in an automobile factory based on production prediction according to a preferred embodiment of the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Referring to fig. 1, an embodiment of the present invention includes:
a production prediction-based mixed flow line production scheduling method for an automobile factory comprises the following steps:
step 1: acquiring the demand of each type of product in a historical period;
step 2: predicting the production number of products of various types of the mixed flow line in the batch by a time series prediction method;
further, the time series prediction method uses an ARMA model or an ARIMA model.
And step 3: establishing a scheduling optimization model, comprising:
step 3-1: determining the production period of the current batch and the production number of products of each model;
step 3-2: determining a scheduling optimization constraint function;
step 3-3: and determining a scheduling optimization objective function.
Further, the scheduling optimization constraint function includes:
a model change cost calculation function:
Figure 501561DEST_PATH_IMAGE001
；
color change cost calculation function:
Figure 67671DEST_PATH_IMAGE002
；
mold preparation function:
Figure 369340DEST_PATH_IMAGE003
；
Figure 729914DEST_PATH_IMAGE004
mold switching function:
Figure 117033DEST_PATH_IMAGE033
；
Figure 320391DEST_PATH_IMAGE034
；
wherein
Figure 210987DEST_PATH_IMAGE007
In order to change the cost factor of the mold,
Figure 8042DEST_PATH_IMAGE008
in order to change the color cost factor,
Figure 616878DEST_PATH_IMAGE009
the production number of each type of product in the batch is calculated;
Figure 790370DEST_PATH_IMAGE010
indicating the model of the mold used by each model of product;
Figure 863368DEST_PATH_IMAGE011
representing the color number corresponding to each model of product;
Figure 831324DEST_PATH_IMAGE012
when in position
Figure 927456DEST_PATH_IMAGE013
Product type of
Figure 639060DEST_PATH_IMAGE014
When the temperature of the water is higher than the set temperature,
Figure 504248DEST_PATH_IMAGE015
otherwise
Figure 456155DEST_PATH_IMAGE016
；
Figure 39583DEST_PATH_IMAGE017
Is a position
Figure 289299DEST_PATH_IMAGE013
Model of product mold, as
Figure 274572DEST_PATH_IMAGE013
The model of each product is not matched with the injection mold, namely when the mold needs to be replaced
Figure 584331DEST_PATH_IMAGE035
Otherwise
Figure 717372DEST_PATH_IMAGE036
；
Figure 770778DEST_PATH_IMAGE020
Is a position
Figure 610558DEST_PATH_IMAGE013
Color number of the product, when
Figure 91218DEST_PATH_IMAGE013
The model of each product is not matched with the current color of the spraying line, namely when the color needs to be changed
Figure 196709DEST_PATH_IMAGE021
Otherwise
Figure 53806DEST_PATH_IMAGE022
。
Further, the objective function is:
Figure 748093DEST_PATH_IMAGE037
；
wherein
Figure 399654DEST_PATH_IMAGE024
In order to be the weight coefficient of the objective function,
Figure 444970DEST_PATH_IMAGE025
、
Figure 902497DEST_PATH_IMAGE026
a function is calculated for the cost.
And 4, step 4: solving the model to obtain a mixed flow line production plan of the batch;
further, solving the model uses a three-stage meta-heuristic search method to solve the model.
And 5: the scheduling plan schedules the product to the machine according to the rules, including:
step 5-1: acquiring the number of each machine table of the mixed flow line of the batch;
step 5-2: calculating the starting time of the production line through an inventory allocation mechanism;
step 5-3: sequencing all the placeable platforms of the current product from small to large according to the quantity of the arranged products to obtain a sequence set, and arranging the current product on the first platform in the sequence set;
step 5-4: and (5) repeating the step (5-3) until all the products of each model of the batch are distributed to the machine.
Further, the inventory allocation mechanism includes:
starting an injection molding production line, and putting m products into stock so that enough products can be available when the spraying production line starts to run;
and m is calculated according to the production cycle and the capacity of different production lines:
Figure 451290DEST_PATH_IMAGE027
；
Figure 539331DEST_PATH_IMAGE028
；
wherein T is the production number of each model product in the batch,
Figure 806365DEST_PATH_IMAGE029
、
Figure 5265DEST_PATH_IMAGE030
respectively the number of products which can be processed in a spraying production line and an injection molding production line in unit time,
Figure 221614DEST_PATH_IMAGE031
in order to start the spraying production line,
Figure 480557DEST_PATH_IMAGE032
the starting time of the injection molding production line.
In another embodiment, a car factory predicts that 10 types of bumpers should be produced in the current production cycle, and the 10 types of bumpers should be produced through a two-stage mixing line of injection molding and spraying, and the predicted quantity is as shown in the following table.
Figure 234886DEST_PATH_IMAGE038
Based on historical data, color change cost coefficients
Figure 971898DEST_PATH_IMAGE039
Coefficient of die change cost
Figure 495283DEST_PATH_IMAGE040
Number of machines
Figure 987444DEST_PATH_IMAGE041
The amount of the water per unit time is one hour,
Figure 963491DEST_PATH_IMAGE042
，
Figure 504193DEST_PATH_IMAGE043
。
and establishing a scheduling optimization model, and determining the optimal cost as 0.6 of the weight coefficient through a plurality of groups of experiments.
Figure 147664DEST_PATH_IMAGE044
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A production prediction-based mixed flow line production scheduling method for an automobile factory is characterized by comprising the following steps:
step 1: acquiring the demand of each type of product in a historical period;
step 2: predicting the production number of products of various types of the mixed flow line in the batch by a time series prediction method;
and step 3: establishing a scheduling optimization model;
and 4, step 4: solving the model to obtain a mixed flow line production plan of the batch;
and 5: the scheduling plan schedules the products to the machine according to rules;
the step 5 comprises the following steps:
step 5-1: acquiring the number of each machine table of the mixed flow line of the batch;
step 5-2: calculating the starting time of the production line through an inventory allocation mechanism;
step 5-3: sequencing all the placeable platforms of the current product from small to large according to the quantity of the arranged products to obtain a sequence set, and arranging the current product on the first platform in the sequence set;
step 5-4: repeating the step 5-3 until all the products of each model of the batch are distributed to the machine;
the inventory allocation mechanism, comprising:
starting an injection molding production line, and putting m products into stock so that enough products can be available when the spraying production line starts to run;
and m is calculated according to the production cycle and the capacity of different production lines:
Figure DEST_PATH_IMAGE001
；
Figure 703095DEST_PATH_IMAGE002
；
wherein T is the production number of each model product in the batch, e1、e2The number of the products which can be processed in the spraying production line and the injection molding production line in unit time, t1For the start-up time of the spraying line, t0The starting time of the injection molding production line.
2. The method as claimed in claim 1, wherein the time series prediction method is an ARMA model or an ARIMA model.
3. The method for scheduling mixed flow lines of an automobile factory based on production prediction as claimed in claim 1, wherein the establishing of the scheduling optimization model comprises:
step 3-1: determining the production period of the current batch and the production number of products of each model;
step 3-2: determining a scheduling optimization constraint function;
step 3-3: and determining a scheduling optimization objective function.
4. The method of claim 3, wherein the scheduling optimization constraint function comprises:
a model change cost calculation function:
Figure DEST_PATH_IMAGE003
；
color change cost calculation function:
Figure 125986DEST_PATH_IMAGE004
；
mold preparation function:
Figure DEST_PATH_IMAGE005
；
Figure 326155DEST_PATH_IMAGE006
mold switching function:
Figure DEST_PATH_IMAGE007
；
Figure 695956DEST_PATH_IMAGE008
；
wherein
Figure DEST_PATH_IMAGE009
In order to change the cost factor of the mold,
Figure 648869DEST_PATH_IMAGE010
in order to change the color cost factor,
Figure DEST_PATH_IMAGE011
the production number of each type of product in the batch is calculated;
Figure 258973DEST_PATH_IMAGE012
indicating the model of the mold used by each model of product;
Figure DEST_PATH_IMAGE013
representing the color number corresponding to each model of product;
Figure 867808DEST_PATH_IMAGE014
when in position
Figure DEST_PATH_IMAGE015
Product type of
Figure 103618DEST_PATH_IMAGE016
When the temperature of the water is higher than the set temperature,
Figure DEST_PATH_IMAGE017
otherwise
Figure 802715DEST_PATH_IMAGE018
；
Figure DEST_PATH_IMAGE019
Is a position
Figure 832987DEST_PATH_IMAGE015
Model of product mold, as
Figure 929119DEST_PATH_IMAGE015
The model of each product is not matched with the injection mold, namely when the mold needs to be replaced
Figure 453773DEST_PATH_IMAGE020
Otherwise
Figure DEST_PATH_IMAGE021
；
Figure 381278DEST_PATH_IMAGE022
Is a position
Figure 520135DEST_PATH_IMAGE015
Color number of the product, when
Figure 103563DEST_PATH_IMAGE015
The model of each product is not matched with the current color of the spraying line, namely when the color needs to be changed
Figure DEST_PATH_IMAGE023
Otherwise
Figure 160469DEST_PATH_IMAGE024
。
5. The method of claim 4, wherein the objective function is:
Figure DEST_PATH_IMAGE025
；
wherein
Figure 208059DEST_PATH_IMAGE026
Is the weight coefficient of the objective function，
Figure DEST_PATH_IMAGE027
、
Figure 330867DEST_PATH_IMAGE028
A function is calculated for the cost.
6. The method for scheduling mixed flow lines in an automobile factory based on yield prediction as claimed in claim 1, wherein in the step 4, the model is solved by using a three-stage meta-heuristic search method.
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