CN114091821A - Refrigerator industry production scheduling data processing method and device - Google Patents
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Abstract
The invention provides a method and a device for processing data of refrigerator industrial production scheduling, firstly, a parameter calculation formula is adopted to calculate and obtain specific values corresponding to a decision variable and each target constant based on a target constant and the decision variable, then, based on the preset constraint conditions, each optimization target model generates a corresponding calculation result based on the decision variables and the specific values corresponding to the target constants, analyzing the calculation result of the optimization target model obtained by combining the values of different decision variables, taking the calculation result of the optimization target model meeting preset conditions as a target calculation result, and extracting the values of the decision variables corresponding to the target calculation result, wherein at the moment, the value of the decision variable is the target production plan, and the production line can be scheduled according to the target production plan.
Description
Technical Field
The invention relates to the technical field of production management, in particular to a refrigerator industry production scheduling data processing method and device.
Background
The refrigerator industry production schedule is to determine the production quantity of various types of products on each production line in a certain time period. The mixed flow production is characterized in that products of different models are produced simultaneously on a plurality of production lines. In order to comprehensively consider various factors such as production orders, factory line productivity, available time of equipment and molds, core material constraints and the like, a scheduling method which gives consideration to order delivery rate and factory capacity utilization rate maximization needs to be designed.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a method and an apparatus for processing scheduling data in refrigerator industry, so as to provide a scheduling method for improving the productivity utilization of a factory.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a refrigerator industry production scheduling data processing method comprises the following steps:
acquiring a target constant and a decision variable of refrigerator production scheduling, wherein the target constant is production process constant data, and the decision variable is positioned in variable data in a production process matched with order data;
determining specific values of the decision variables under each target constant;
obtaining an optimization target model established based on the mapping relation between the decision variables and the total delay number and the number of times of model changing of the order;
substituting specific values of the decision variables under each target constant into an optimization target model, wherein the optimization target model comprises: the first model is used for calculating the total delay number of orders based on the specific values of the decision variables under the target constants, and the second model is used for calculating the mold changing times of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants;
solving an optimization target model based on the specific values of the decision variables under the target constants and preset constraint conditions to obtain the calculation results of the target optimization model under the combination of the specific values of the decision variables under the target constants;
and acquiring a calculation result meeting a preset condition as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a fixture in a production thread in decision variables corresponding to the target calculation result as target variables.
Optionally, in the method for processing data of refrigerator industrial production schedules, the decision variables include:
the model arrangement output, the mould arrangement output, the order delay amount, the order delay state, the accumulated delay amount of the order every day, the mould production duration, the mould hanging state and the clamp mould changing sequence state.
Optionally, the optimizing the objective model further includes:
and the third model is used for calculating the delay penalty of the order every day based on the specific values of the decision variables under the various target constants.
Optionally, in the method for processing scheduling data in refrigerator industry production, the first model is specifically configured to calculate the delay number of the total orders based on the delay status of each order;
the second model is specifically used for calculating the total die change times of the die based on the absolute value of the difference value of the on-hook states of the die on two adjacent days;
the third model is specifically used for calculating the total daily delay penalty of the orders based on the corresponding daily order delay penalty coefficient of each order and the daily accumulated delay of the orders.
Optionally, in the method for processing data of refrigerator industrial production schedules, the obtaining of the calculation result meeting the preset condition and meeting the preset condition as a target calculation result includes:
and multiplying the calculation results of the first model, the second model and the third model by corresponding weight coefficients, calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted to be a candidate result, and taking the candidate result of the minimum value as a target calculation result.
Optionally, in the above method for processing data of refrigerator industrial production schedule, the constraint condition includes one or more of the following combinations:
the required capacity of the order is equal to the actual capacity of the order plus the delay of the order:
the sum of the accumulated planned amount and the accumulated delay of the orders ending to a certain day is not less than the accumulated demand of the orders;
the order row output is equal to the model row output;
the model row output is equal to the mould row output;
the mold row output is equal to the mold production time multiplied by the beat;
the single-day yield of the line body cannot exceed the upper limit of the line body;
the mold throughput cannot exceed the theoretical throughput upper limit;
the sum of the actual production time of the die on the single-day single clamp and the die change loss time cannot exceed the upper limit of the production time of the day;
the mould hanging sequence on the single-day single clamp is unique;
the incidence relation between the mold hanging sequence of the single-day single clamp and the mold hanging state on the sequence is as follows:
only one mould can be mounted on a single-day single clamp in a single sequence;
the mold hanging states of the molds between two adjacent days are forced to be consistent;
the total length of time the mould is used must not exceed the theoretical total upper limit.
A refrigerator industry production schedule data processing device comprises:
the system comprises a parameter acquisition unit, a parameter storage unit and a parameter processing unit, wherein the parameter acquisition unit is used for acquiring a target constant and a decision variable of refrigerator production scheduling, the target constant is production process constant data, and the decision variable is positioned in variable data in a production process matched with order data;
the parameter calculation unit is used for determining specific values of the decision variables under each target constant;
a model calculation unit, configured to obtain an optimized target model created based on a mapping relationship between the decision variable and the total number of delays in the order and the number of times of mode conversion, and substitute a specific value of the decision variable under each target constant into the optimized target model, where the optimized target model includes: the first model is used for calculating the total delay number of orders based on the specific values of the decision variables under the target constants, and the second model is used for calculating the mold changing times of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants;
the decision variable selection unit is used for solving an optimized target model based on the specific values of the decision variables under the target constants and preset constraint conditions to obtain a calculation result of the target optimized model under the combination of the specific values of the decision variables under the target constants; and acquiring a calculation result meeting preset conditions as a target calculation result, acquiring a calculation result meeting the preset conditions as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a mold in a production thread in decision variables corresponding to the target calculation result as target variables.
Optionally, in the data processing apparatus for refrigerator industrial production scheduling, the decision variables include:
the model arrangement output, the mold arrangement output, the order arrangement output, the total order delay amount, the order delay state, the daily accumulated order delay amount, the mold production duration, the mold hanging state and the mold changing sequence state of the clamp. Optionally, in the refrigerator industry production scheduling data processing apparatus, the first model is specifically configured to calculate the number of delays of the total orders based on a delay status of each order.
The optimization objective model further comprises:
and the third model is used for calculating the delay penalty of the order every day based on the specific values of the decision variables under the various target constants.
The first model is specifically used for calculating the delay number of the total orders based on the delay state of each order;
the second model is specifically used for calculating the total die change times of the die based on the absolute value of the difference value of the on-hook states of the die on two adjacent days;
the second model is specifically used for calculating the total daily delay penalty of the orders based on the corresponding daily order delay penalty coefficient of each order and the daily accumulated delay of the orders.
Optionally, in the refrigerator industry production scheduling data processing apparatus, the decision variable selecting unit is specifically configured to:
and multiplying the calculation results of the first model, the second model and the third model by corresponding weight coefficients, calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted to be used as a candidate result, and using the candidate result of the minimum value as a target calculation result.
Optionally, in the data processing apparatus for refrigerator industrial production schedule, the constraint condition includes one or more of the following combinations:
the required capacity of the order is equal to the actual capacity of the order plus the delay of the order:
the sum of the accumulated planned amount and the accumulated delay of the orders ending to a certain day is not less than the accumulated demand of the orders;
the order row output is equal to the model row output;
the model row output is equal to the mould row output;
the mold row output is equal to the mold production time multiplied by the beat;
the single-day yield of the line body cannot exceed the upper limit of the line body;
the mold throughput cannot exceed the theoretical throughput upper limit;
the sum of the actual production time of the die on the single-day single clamp and the die change loss time cannot exceed the upper limit of the production time of the day;
the mould hanging sequence on the single-day single clamp is unique;
the incidence relation between the mold hanging sequence of the single-day single clamp and the mold hanging state on the sequence is as follows:
only one mould can be mounted on a single-day single clamp in a single sequence;
the mold hanging states of the molds between two adjacent days are forced to be consistent;
the total length of time the mould is used must not exceed the theoretical total upper limit.
Based on the above technical solution, in the above solution provided in the embodiments of the present invention, a parameter calculation formula is first adopted to calculate specific values corresponding to the decision variables and the target constants based on the target constants and the decision variables, the specific values corresponding to the decision variables and the target constants are then based on the preset constraint conditions, each optimized target model generates corresponding calculation results based on the specific values corresponding to the decision variables and the target constants, the calculation results of the optimized target models obtained under different combinations of the decision variables are analyzed, the calculation results of the optimized target models meeting the preset conditions are taken as target calculation results, values of the decision variables corresponding to the target calculation results are extracted, and at this time, the values of the decision variables are the target production plan, in this case, the production line may be scheduled according to the target production plan.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a main flow chart of refrigerator production;
FIG. 2 is a structural view of foaming of the cabinet;
FIG. 3 is a flowchart illustrating a method for processing data of refrigerator manufacturing schedules according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a refrigerator industrial production schedule data processing apparatus according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The main flow of the refrigerator production is shown in fig. 1. The general flow of refrigerator production is divided into 4 parts: the method comprises a former process flow 1, a box body foaming flow 2, a door body foaming flow 3 and a final assembly flow 4. The process 1 mainly comprises the steps of processing and modular assembly of main key parts of a refrigerator door body and the like. The process 2 comprises the steps of putting the assembled box body into a corresponding mould, and then heating, injecting materials, foaming and the like through a foaming mould, and is characterized in that the production beat is slow, and the replacement time of the foaming mould is long. The flow 3 is a foaming process for the door body. The procedure 4 is to assemble the door body of the foamed box. Comparing the four flows, the flow 2 belongs to a bottleneck process.
The core of the production schedule of the refrigerator industry is to make a production time sequence plan for the bottleneck process 2 and improve the capacity of finished product assembly by maximally digging the capacity of the bottleneck process. The foaming process of the box body mould comprises a plurality of foaming lines which are coded in a format of L1 and L2; each foaming line contains a number of clips coded in P1, P2.. format; foaming of the cabinet requires associated foaming molds/equipment, coded in M1, M2. As shown in FIG. 2, 2-1 shows a foaming line, 2-2 shows a jig on the foaming line, and 2-3 shows a mold mounted on the jig. Both the clamp and the die belong to scarce resources. Regardless of the failure of the clamps, the clamps can be put into production at the same time, but only one mold can be mounted on one clamp at the same time. The clamps on the same wire body are not particularly distinguished. Meanwhile, the moulds which are put into production can only produce the model products corresponding to the respective moulds. But a single mould can only be produced for one tank at a time. It is also considered that the switching of the mold on the jig is time consuming, i.e. the jig and the associated mold cannot produce the product during this period. Therefore, the core problem to be solved by the patent of the invention is to consider the order delivery time and the mold switching loss cost to be the least under the condition of considering the time loss of mold switching accuracy, namely to consider the order delivery time and the production line utilization rate to be the highest under the condition of considering the time loss of mold switching accuracy.
To better describe how the patent calculates the precise lost time of the mold switching on the foaming line, a time sequence scheduling state table describing the mold is firstly designed, as shown in table 1, and table 1 is a line-clamp-date-mold relation table.
TABLE 1
Assuming that each clamp has at most n-1(n > -2) mold hanging orders, namely at least 1 order, every day, when calculating the specific schedule, the specific schedule can be manually set according to the actual situation. As previously mentioned, only one mold per sequence can be produced per fixture. Taking the gripper P1 on day 1 of the table as an example, mold M1 was scheduled for production first (which is also understood herein as the initial mold loading state), and mold M3 was then scheduled for production in sequences 2 and 3. Note that the last time series mold of the day is identical to the 1 st time series mold of the 2 nd day.
Table 2 shows the time loss of switching between the molds, i.e., the mold change loss time matrix. Wherein it takes 5 minutes to switch to mold M2 as mold M1; it takes 10 minutes for mold M2 to switch to mold M1 because the mold change includes 2 unloading and loading steps, and the different mold times for each step may be different. Table 1 gives the example of the die change loss of clip P1 on the first day at 10 × S (M1- > M3) +15 × S (M3- > M4) ═ 25. The production sequence of the molds scheduled each day is unique for each jig, and therefore the mold change loss time is unique.
| M1 | M2 | M3 | M4 | M5 | M6 | |
| M1 | 0 | 5 | 10 | 5 | 10 | 15 |
| M2 | 10 | 0 | 5 | 10 | 15 | 10 |
| M3 | 7 | 6 | 0 | 15 | 10 | 10 |
| M4 | 10 | 12 | 15 | 0 | 15 | 10 |
| M5 | 15 | 11 | 13 | 10 | 0 | 15 |
| M6 | 8 | 15 | 15 | 15 | 20 | 0 |
TABLE 2
Based on the above background, the present application discloses a method and an apparatus for processing data of refrigerator industrial production schedule, referring to fig. 3, the method includes:
step S101: acquiring a target constant and a decision variable of refrigerator production scheduling;
the target constant is production process constant data, the decision variable is positioned in variable data in a production process matched with the order data, and the specific value of the decision variable is different along with the difference of the target constant.
In this embodiment, the specific selection of the target constant may be set based on a user requirement, for example, in the technical solution disclosed in this embodiment, the target constant includes a combination of one or more of the following parameters:
foaming line body coding, scheduling calendar coding, line body production duration, line body production capacity, fixture coding, maximum mold changing times of a mold on the fixture, product model coding, foaming mold coding, mold quantity, mold beat, mold model corresponding production state, order coding, order quantity, order delivery date state, order delay penalty coefficient, mold changing sequence set and mold changing loss time;
specifically, the method comprises the following steps:
foaming line body code LlWherein l is 1,2, 3 … ….
Calendar code for scheduling DdWhere D is 1,2, 3 … …, e.g. D2Indicating day 2 within the scheduled period;
length of production line Tl,dTo indicate a foaming line body LlAt schedule calendar DdThe production time of a day;
production capacity L _ capacity of line bodylTo indicate a linear foaming line LlMaximum yield per day;
clamp code Pl,p，p＝1,2，3……，P2,3Indicating line body L2The third clamp above;
the maximum die change times N of the die on the clamp is more than or equal to 2;
product model number code Xx，x＝1,2，3……；
Foaming mould code Mm，m＝1,2，3……；
Number of molds M _ nummDenotes a mold MmThe number available;
beat M _ beat of moldmDenotes a mold MmHourly production, unit: table/hour;
the model of the mould corresponds to the production state M2Xm,x∈[0,1]E.g. M2Xm,x1 indicates the mold MmCan produce model XxIf M2Xm,xIs 0, then M is representedmCan not produce type Xx；
Order code Ox,jJ-1, 2, 3 … … such as O2，3Indicating type X2The corresponding 3 rd order;
order quantity O _ qx,jIndicates order Ox,jThe corresponding required quantity;
order lead time status O _ dayx,j,dE.g. Ox，jAt DdThe daily delivery is 1, otherwise 0;
order delay penalty factor O _ penaltyx,j,dRepresents an order Oi，jAt DkA penalty factor for days. The penalty coefficient is to reflect the penalty degree of the order type and the penalty degree of the time delay. One possible example is a penalty of 0 if an order can be produced all at the lead time; assuming, for example, a delay of two days, the penalty is equal to the production on day 1 multiplied by 2 plus the production on day 4 multiplied by 4, where 2 and 4 are penalty factors. The orders with different priorities can be slightly adjusted on the basis of time penalty;
the set of mold change orders M _ sequence is determined because the number of mold changes n on the fixture per day is determined for a fixed algorithm operation. For example, if the mold changing sequence of the molds on each fixture is set to be at most 3, the kinds of the molds are 6, and the number of the elements in the set is 6 × 6 — 216, that is, the mold production sequence on a certain selected fixture is one of the cases in 216;
mold change loss time M _ change _ costeAnd e belongs to M _ sequence, and represents the loss of the mode change time corresponding to one element in M _ sequence.
Of course, the target constant may include other parameters, and the above example is only one specific example provided in the present application.
The parameters specifically included in the decision variables may also be set by the user according to the user's needs, for example, the decision variables include one or more of the following parameters:
the model arranging yield, the mould arranging yield, the order arranging yield, the total order delay amount, the total order delay state, the daily accumulated delay amount of the order, the mould production duration, the mould hanging state and the clamp mould changing sequence state;
specifically, the method comprises the following steps:
model row yield X _ Qx，l，dWherein x is 1,2, 3.; 1,2, 3.; d 1,2, 3, which represents the type XxOn-line body LlD thdDaily yield;
die row output M _ Qm，l，dWherein m is 1,2, 3.; 1,2, 3.; d 1,2, 3, denotes a mold MmOn-line body LlD thdDaily yield;
order line output O _ Qx，j，dWherein x is 1,2, 3.; j ═ 1,2, 3.; d 1,2, 3, which represents order Ox，jAt DdDaily yield;
total Delay O _ Delay of orderx，jWherein x is 1,2, 3.; j is 1,2, 3, which indicates order Ox，jThe total delay amount in the whole scheduling period;
total order postponement Status O _ Delay _ Statusx，jWherein x is 1,2, 3.; 1,2, 3, is a binary form [0, 1 ]]Variable, if O _ Delayx，jIf the value is more than 0, the value is 1, and the other conditions are 0;
cumulative Delay O _ Day _ Delay of order per Dayx，j，dWherein x is 1,2, 3.; j ═ 1,2, 3.; d 1,2, 3, which represents order Ox，jAt DdThe amount of delay in days;
mold production time M _ Tm，l，p，dWherein m is 1,2, 3.; 1,2, 3.; 1,2, 3.; d 1,2, 3, denotes a mold MmOn-line body LlUpper clamp PpYield on day Dd;
mould hanging state M _ Sm，l，p，d，iWherein m is 1, 2.; 1, 2.; 1, 2.; 1,2, 1, binary [0, 1 ]]Variable, representing the mold MmOn-line body LlGo to item DdWhether the ith order of day is hung on the clamp PpThe above step (1);
clamp apparatus(mode position) mode change order state P _ Sl，p，d，eWherein l is 1, 2.; 1, 2.; d 1, 2.; e ∈ M _ sequence represents possible string mode sequence enumeration values of the current mode position, and each enumeration value corresponds to a unique mode change loss time.
The decision variables are variable parameters, the specific values of the decision variables change along with the change of the values of the target constants in the decision variables, the values of the target constants are different, and the specific values of the decision variables are different.
In the specific design, specific parameters included in the target constant and the decision variable can be set by the user according to the requirements.
Step S102: determining specific values of the decision variables under each target constant;
in this scheme, in the decision variables, the values of the target constants are different, and the values of the decision variables are also different, and the values of the decision variables may be directly obtained from a production database, for example, the values of the target constants such as different foaming line body codes, scheduling calendar codes, line body production time, line body production capacity, clamp codes, and the like are different, and the values of the decision variables such as model production rate, mold production rate, order total delay amount, and the like are also different.
Step S103: solving an optimization target model based on the specific values of the decision variables under the target constants and preset constraint conditions to obtain the calculation results of the target optimization model under the combination of the decision variables and the specific values corresponding to the target constants;
the optimization objective model includes: the first model is used for calculating the total order delay number based on the specific values corresponding to the decision variables and the target constants, the second model is used for calculating the mode change times based on the specific values corresponding to the decision variables and the target constants, and the third model is used for calculating the delay penalty of the order every day based on the specific values corresponding to the decision variables and the target constants. Specifically, the first model is specifically used for calculating the total delay number of the refrigerator production orders based on specific values of the decision variables under each target constant; the second model is specifically used for calculating the mold changing times of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants, and the third model is specifically used for calculating the daily delay penalty of the order based on the specific values of the decision variables under the target constants.
The variable parameters in the optimization target model are decision variables, and different calculation results can be obtained by the optimization target model under the decision variables with different specific values. The optimization target model is used for calculating specific values corresponding to the decision variables and the target constants on the basis of preset constraint conditions to obtain a calculation result of the optimization target model under the combination of the specific values corresponding to the decision variables and the target constants.
Here, the preset constraint condition is a constraint condition that constrains the decision variables.
Step S104: and acquiring a calculation result of a target optimization model meeting preset conditions as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a fixture in a production thread in decision variables corresponding to the target calculation result as target variables.
In this scheme, on the basis of the preset constraint condition, each optimization target model generates a corresponding calculation result based on specific values corresponding to the decision variables and the target constants, the calculation results of the optimization target models obtained by combining different decision variable values are analyzed, the calculation result of the optimization target model meeting the preset condition is used as a target calculation result, values of the decision variables corresponding to the target calculation result are extracted, at this time, the values of the decision variables are a target production plan, and at this time, a production line can be scheduled according to the target production plan.
In the technical solution disclosed in the embodiment of the present application, a specific constraint object in the constraint condition may be selected by a user on the basis of a user requirement, for example, the constraint object in the constraint condition may include:
O_Delay_Statusx，jwherein x represents the model number of the order, j represents the code of the order, and O _ Delay _ Statusx，jRepresenting the total delay status of the order with the model number x coded as j;
O_penaltyx，j，dd represents the D thdDay, O _ dependencyx，j，dOrder O with the representation type x coded as jx，jAt DdA penalty factor for a day;
O_Day_Delayx，j，d，O_Day_Delayx，j，dorder O with the representation type x coded as jx，jAt DdThe amount of delay in days;
M_Sm，l，p，d，im represents a foaming mold code, l represents a foaming line code, i represents a sequence, M _ Sm，l，p，d，iRepresenting a code as MmIn-line body L of foaming mouldlGo to item DdWhether the ith order of day is hung on the clamp PpWhen hung on the clamp PpWhen above, M _ Sm，l，p，d，iThe value of (a) is 1, otherwise, the value of (b) is 0;
the constraints include a combination of one or more of:
the required capacity of the order is equal to the actual capacity of the order plus the delay of the order:
wherein, the O _ Qx，j，dIs order Ox，jAt DdActual daily output, the O _ Delayx，jIs order Ox，jThe delay amount of O _ q, thex，jIs order Ox，jThe production needs to be discharged;
the sum of the accumulated planned amount and the accumulated delay of the orders ending to a certain day is not less than the accumulated demand of the orders;
O_Qx，j，d1is order Ox，jAt Dd1Daily order cumulative plan amount, said O _ Day _ Delayx，j，dIs order Ox，jAt DdCumulative delay of day
Is order Ox，jThe cumulative demand of (2);
the order row output is equal to the model row output;
wherein, X _ Qx，l，dRepresentation of type XxOrder online body LlD thdDaily yield of said O-Qx，j，dIndicates order Ox，jAt DdDaily yield;
the model row output is equal to the mould row output;
wherein, M-Qm，l，dIndicating the mould MmOn-line body LlD thdDaily yield, said M2Xm，xIndicating the corresponding production state of the mould type, X-Qx，l，dIndicating type XxOn-line body LlD thdDaily yield;
the mold row output is equal to the mold production time multiplied by the beat;
the single-day yield of the line body cannot exceed the upper limit of the line body;
the mold throughput cannot exceed the theoretical throughput upper limit;
M_Qm，l，d≤M_numm*M_beatm*Tl，d；
the actual production time and the mould changing time loss of the mould on the single-day single clamp cannot exceed the upper limit of the production time of the day;
the mould hanging sequence on the single-day single clamp is unique;
the incidence relation between the mold hanging sequence of the single-day single clamp and the mold hanging state on the sequence is as follows:
only one mould can be mounted on a single-day single clamp in a single sequence;
the mold hanging states of the molds between two adjacent days are forced to be consistent;
M_Sm，l，p，d-1，N＝M_Sm，l，p，d，0，d≥2；
the total using time of the die cannot exceed the theoretical total upper limit;
in the technical solution disclosed in the embodiment of the present application, corresponding to the above-mentioned constraint object, a specific calculation formula for optimizing the objective model is also disclosed, specifically,
the first model is
The second model is a model of,
the third model is a model of a model,
in this scheme, corresponding to the above optimized target model, in the technical scheme disclosed in this embodiment of the present application, a calculation result that meets a preset condition is obtained as a target calculation result, where the type of the preset condition may be set according to a user requirement, and in the technical scheme disclosed in this embodiment, obtaining a calculation result that meets the preset condition as a target calculation result specifically may include: and multiplying the calculation results of the first model and the second model by corresponding weight coefficients, calculating the sum of the calculation results of the first model and the second model after the weight coefficients are adjusted to be used as a candidate result, and using the candidate result of the minimum value as a target calculation result. Or multiplying the calculation results of the first model, the second model and the third model by the corresponding weight coefficients, calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted to be a candidate result, and taking the candidate result of the minimum value as a target calculation result.
In a technical solution disclosed in another embodiment of the present application, in order to facilitate machine processing, the present application discloses a specific calculation formula of a preset condition, specifically, the acquiring a calculation result satisfying the preset condition as a target calculation result specifically includes:
substituting the calculation results of the optimization target model corresponding to each decision variable into a formula
obj_total＝c1*obj_order_delay+c2*obj_order_day_delay+c3*obj_mould_exchange_count
When the value of the obj _ total is minimum, the calculation result of the corresponding optimization target model is used as the target calculation result, wherein c is1、c2、c3Is a preset weight coefficient. C is mentioned1、c2、c3The value of (A) can be set according to specific requirements.
In this embodiment, corresponding to the above method, a refrigerator industry production scheduling data processing apparatus is also disclosed, and the specific work content of each unit in the apparatus is described below with reference to the content of the above method embodiment.
Referring to fig. 4, the refrigerator industrial production schedule data processing apparatus disclosed in the embodiment of the present application may include:
a parameter obtaining unit 100, corresponding to step S101 in the method, configured to obtain a target constant and a decision variable of a refrigerator production schedule, where the target constant is production process constant data, and the decision variable is located in variable data in a production process matched with order data;
a parameter calculating unit 200, corresponding to step S102 in the above method, for determining specific values of the decision variables under each target constant;
a model calculating unit 300, corresponding to step S103 in the foregoing method, configured to obtain an optimization objective model created based on a mapping relationship between the decision variable and the total number of delays and the number of times of mode change of the order, and substitute specific values of the decision variable under each objective constant into the optimization objective model, where the optimization objective model includes: and the second model is used for calculating the number of times of mold changing of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants.
A decision variable selection unit 400, corresponding to step S104 in the above method, configured to solve an optimized objective model based on specific values of the decision variables under each objective constant and a preset constraint condition, so as to obtain a calculation result of the objective optimized model under a combination of the specific values of the decision variables under each objective constant; and acquiring a calculation result meeting preset conditions as a target calculation result, acquiring a calculation result meeting the preset conditions as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a mold in a production thread in decision variables corresponding to the target calculation result as target variables.
The optimization objective model further comprises:
and the third model is used for calculating the delay penalty of the order every day based on the specific values of the decision variables under the various target constants.
In the above apparatus, the specific value of the decision variable corresponding to the target calculation result is used as the target variable for outputting, and it should be noted that the scheme disclosed in the above embodiment of the present application may also be applied to not only the scheduling plan of refrigerator production, but also the scheduling plan of other products.
The detailed operation of each unit in the data processing device for refrigerator industrial production schedule is described with reference to the above method, and a detailed description thereof is omitted here.
For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A refrigerator industry production scheduling data processing method is characterized by comprising the following steps:
acquiring a target constant and a decision variable of refrigerator production scheduling, wherein the target constant is production process constant data, and the decision variable is positioned in variable data in a production process matched with order data;
determining specific values of the decision variables under each target constant;
obtaining an optimization target model established based on the mapping relation between the decision variables and the total delay number and the number of times of model changing of the order;
substituting specific values of the decision variables under each target constant into an optimization target model, wherein the optimization target model comprises: the first model is used for calculating the total delay number of the refrigerator production orders based on the specific values of the decision variables under the target constants, and the second model is used for calculating the mold changing times of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants;
solving an optimization target model based on the specific values of the decision variables under the target constants and preset constraint conditions to obtain the calculation results of the target optimization model under the combination of the specific values of the decision variables under the target constants;
and acquiring a calculation result meeting a preset condition as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a fixture in a production thread in decision variables corresponding to the target calculation result as target variables.
2. The method as claimed in claim 1, wherein the optimization objective model further comprises:
and the third model is used for calculating the delay penalty of the order every day based on the specific values of the decision variables under the various target constants.
3. The method as claimed in claim 2, wherein the data processing unit is further configured to,
the first model is specifically used for calculating the delay number of the total orders based on the delay state of each order;
the second model is specifically used for calculating the total die change times of the die based on the absolute value of the difference value of the on-hook states of the die on two adjacent days;
the third model is specifically used for calculating the total daily delay penalty of the orders based on the corresponding daily order delay penalty coefficient of each order and the daily accumulated delay of the orders.
4. The method as claimed in claim 3, wherein the data processing unit is further configured to,
the acquiring of the calculation result meeting the preset condition as the target calculation result includes:
and multiplying the calculation results of the first model, the second model and the third model by corresponding weight coefficients, calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted to be a candidate result, and taking the candidate result of the minimum value as a target calculation result.
5. The method as claimed in claim 1, wherein the constraint condition includes one or more of the following:
the required capacity of the order is equal to the actual capacity of the order plus the delay of the order:
the sum of the accumulated planned amount and the accumulated delay of the orders ending to a certain day is not less than the accumulated demand of the orders;
the order row output is equal to the model row output;
the model row output is equal to the mould row output;
the mold row output is equal to the mold production time multiplied by the beat;
the single-day yield of the line body cannot exceed the upper limit of the line body;
the mold throughput cannot exceed the theoretical throughput upper limit;
the sum of the actual production time of the die on the single-day single clamp and the die change loss time cannot exceed the upper limit of the production time of the day;
the mould hanging sequence on the single-day single clamp is unique;
the incidence relation between the mold hanging sequence of the single-day single clamp and the mold hanging state on the sequence is as follows:
only one mould can be mounted on a single-day single clamp in a single sequence;
the mold hanging states of the molds between two adjacent days are forced to be consistent;
the total length of time the mould is used must not exceed the theoretical total upper limit.
6. A refrigerator industry production schedule data processing device, comprising:
the system comprises a parameter acquisition unit, a decision-making unit and a control unit, wherein the parameter acquisition unit is used for acquiring a target constant and a decision-making variable of refrigerator production scheduling, the target constant is production process constant data, and the decision-making variable is positioned in variable data in a production process matched with order data;
the parameter calculation unit is used for determining specific values of the decision variables under each target constant;
a model calculation unit, configured to obtain an optimized target model created based on a mapping relationship between the decision variable and the total number of delays in the order and the number of times of mode conversion, and substitute a specific value of the decision variable under each target constant into the optimized target model, where the optimized target model includes: the first model is used for calculating the total delay number of orders based on the specific values of the decision variables under the target constants, and the second model is used for calculating the mold changing times of the mold on the refrigerator foaming process based on the specific values of the decision variables under the target constants;
the decision variable selection unit is used for solving an optimized target model based on the specific values of the decision variables under the target constants and preset constraint conditions to obtain a calculation result of the target optimized model under the combination of the specific values of the decision variables under the target constants; and acquiring a calculation result meeting preset conditions as a target calculation result, acquiring a calculation result meeting the preset conditions as a target calculation result, and outputting specific values of a mold hanging state and a mold changing sequence state of a mold in a production thread in decision variables corresponding to the target calculation result as target variables.
7. The data processing device for refrigerator industrial production schedule of claim 6,
the optimization objective model further comprises:
and the third model is used for calculating the delay penalty of the order every day based on the specific values of the decision variables under the various target constants.
8. The data processing device for refrigerator industrial production schedule of claim 7,
the first model is specifically used for calculating the delay number of the total orders based on the delay state of each order;
the second model is specifically used for calculating the total die change times of the die based on the absolute value of the difference value of the on-hook states of the die on two adjacent days;
the second model is specifically used for calculating the total daily delay penalty of the orders based on the corresponding daily order delay penalty coefficient of each order and the daily accumulated delay of the orders.
9. The data processing device for refrigerator industrial production schedule of claim 8,
the decision variable selection unit is specifically configured to:
and multiplying the calculation results of the first model, the second model and the third model by corresponding weight coefficients, calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted to be used as a candidate result, and using the candidate result of the minimum value as a target calculation result.
10. The apparatus of claim 6, wherein the constraints comprise one or more of the following:
the required capacity of the order is equal to the actual capacity of the order plus the delay of the order:
the sum of the accumulated planned amount and the accumulated delay of the orders ending to a certain day is not less than the accumulated demand of the orders;
the order row output is equal to the model row output;
the model row output is equal to the mould row output;
the mold row output is equal to the mold production time multiplied by the beat;
the single-day yield of the line body cannot exceed the upper limit of the line body;
the mold throughput cannot exceed the theoretical throughput upper limit;
the sum of the actual production time of the die on the single-day single clamp and the die change loss time cannot exceed the upper limit of the production time of the day;
the mould hanging sequence on the single-day single clamp is unique;
the incidence relation between the mold hanging sequence of the single-day single clamp and the mold hanging state on the sequence is as follows:
only one mould can be mounted on a single-day single clamp in a single sequence;
the mold hanging states of the molds between two adjacent days are forced to be consistent;
the total length of time the mould is used must not exceed the theoretical total upper limit.
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| CN116748447B (en) * | 2023-08-16 | 2023-10-13 | 武汉新威奇科技有限公司 | Quick die changing method and system for full-automatic forging production line |
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