CN114707748A - Intelligent mixed flow production line production scheduling method based on group immunity-genetic algorithm - Google Patents

Intelligent mixed flow production line production scheduling method based on group immunity-genetic algorithm Download PDF

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CN114707748A
CN114707748A CN202210415035.6A CN202210415035A CN114707748A CN 114707748 A CN114707748 A CN 114707748A CN 202210415035 A CN202210415035 A CN 202210415035A CN 114707748 A CN114707748 A CN 114707748A
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product
production line
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王纪章
高志恒
李锋军
张雷雷
高鸣
谢富明
赵桃艳
张尹松
张洁
张冰冰
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First Tractor Co Ltd
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Abstract

The invention provides a group immunity-genetic algorithm-based intelligent production scheduling method for a mixed flow production line, which is characterized in that a production plan multi-target model is established according to production rules of the production line, and the production plan multi-target model comprises the total processing time considering reloading time, the satisfaction degree of stored parts and the switching times of products; collecting delivery completion quantity of a production order, delivery date of the production order and inventory information of accessories, judging the priority of the multi-objective model, and determining an infected population and an optimization main objective in a population immunity-genetic algorithm; collecting production and processing time data of each station of different products in a period, and judging whether a basic data set needs to be updated or not; and inputting the updated basic data set and the optimized main target into a population immune-genetic algorithm for optimizing calculation, calculating an optimal solution according with the actual production condition, and outputting a production plan. The invention can change the scheduling target in a self-adaptive way, and can also realize the self-updating of scheduling basic data, so that the production plan is more suitable for the actual production capacity.

Description

Intelligent mixed flow production line production scheduling method based on group immunity-genetic algorithm
Technical Field
The invention belongs to the technical field of intelligent factory production plans, and particularly relates to a group immunity-genetic algorithm-based intelligent production scheduling method for a mixed flow production line.
Background
In order to realize efficient production of a flow shop, a scheduling method for performing a flow shop production plan by using an intelligent optimization algorithm is also receiving more and more attention.
For the optimization research of the flow shop production plan scheduling problem, one method is to solve the problem by establishing a simple mathematical model and applying a linear programming method, but the method cannot solve the complex workshop problem, and the other method is to solve a specific target by applying an intelligent optimization algorithm, because a production line is changeable, the theoretical data of production and processing is different from the actual production capacity of the current production line, the production scheduling cannot well meet the realistic situation; the main reason is that the optimization target is relatively fixed, and the production target should be flexibly selected according to the production condition.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent production scheduling method for a mixed flow production line based on a group immunity-genetic algorithm.
The present invention achieves the above-described object by the following technical means.
An intelligent production scheduling method for a mixed flow production line based on a colony immunity-genetic algorithm comprises the following steps:
collecting delivery completion quantity of a production order, delivery date of the production order and inventory information of accessories, judging the priority of the multi-objective model, and determining an infected population and an optimization main objective in a population immunity-genetic algorithm;
collecting production and processing time data of each station of different products in a period, and judging whether a basic data set needs to be updated or not;
and inputting the updated basic data set and the optimized main target into a population immune-genetic algorithm for optimizing calculation, calculating an optimal solution according with the actual production condition, and outputting a production plan.
In a further technical scheme, the judging of the priority of the multi-objective model, the determination of the infected population in the population immunity-genetic algorithm and the optimization of the main objective specifically comprise:
if the current production condition of the production line is that the product delivery date is urgent, the first population is an infected population, the first population is iterated preferentially, and the second and third populations are uninfected populations;
if the current production condition of the production line is insufficient supply of accessories, the second population is an infected population, the second population is iterated preferentially, and the first population and the third population are uninfected populations;
if the current production condition of the production line is normal, the population I and the population II are recovered or are not infected, and the population III is preferentially iterated;
the current production condition of the production line is that the product delivery date is urgent and the accessory supply is insufficient, infection is carried out according to the priority of two populations, wherein the first population is an infected population, and iteration is carried out until recovery is carried out preferentially, and the second population is a susceptible population, is infected after the first population recovers, and iteration is carried out preferentially until recovery is carried out preferentially;
the first population corresponds to the total processing time considering the reloading time, and the optimization main objective of the first population is to minimize the total processing time considering the reloading time;
the second population corresponds to the satisfaction degree of the inventory parts, and the optimization main target of the second population is the minimization of the satisfaction degree of the inventory parts;
the third population corresponds to normal production of a production line, and the optimization main target of the third population is to consider the minimization of the total processing time of the reloading time, the minimization of the satisfaction degree of the stored parts and the minimization of the switching times of products.
According to a further technical scheme, the product delivery date meets the following requirements in an emergency mode:
Figure BDA0003605394020000021
wherein: sumnummEndNum for each type of product total in the ordermFor the finished quantity of each type of product in the order, CycProduction is the production cycle yield of each type of product in the order, SurCycle is the residual production cycle number of the order, and A is interferenceAnd (4) the coefficient.
According to a further technical scheme, the supply shortage of the accessories meets the following requirements:
Figure BDA0003605394020000027
wherein: needlenummIndicates the part demand, AllNum, for each type of productmIndicating the remaining amount of accessories in the storage area.
According to a further technical scheme, when the production condition is normal, the production plan is solved according to the multi-target problem.
In a further technical solution, the objective function of the total processing time considering the reloading time is as follows:
F1＝minE
the constraint conditions are as follows:
Figure BDA0003605394020000022
Figure BDA0003605394020000023
Figure BDA0003605394020000024
Figure BDA0003605394020000025
Figure BDA0003605394020000026
Figure BDA0003605394020000031
Figure BDA0003605394020000032
Figure BDA0003605394020000033
Figure BDA0003605394020000034
Figure BDA0003605394020000035
Figure BDA0003605394020000036
constraint one means that at the k position, there is only one product type m;
constraint two indicates that the input quantity of all product types in the current production is the same as the planned quantity;
constraint three indicates that the s-1 st station for the k-th position product must be processed after the s-th station;
constraint four indicates that the s 'th station for the k position product must be post-processed at the s' th station for the k-1 position product;
constraint five represents the processing end time of the first station at the first position;
constraint six represents the product processing end time with the production position of 1;
constraint seven represents the processing end time of the first station of all the products in production;
constraint eight represents the processing end time when the product station with the production position not 1 is not 1;
constraint nine represents a 0-1 decision variable for judging whether the reloading types are the same or not;
constraint ten means that the preparation time is not calculated for the first product;
constraint eleven represents a constraint of the objective function E and the processing end time of each operation at the last station;
wherein: x is the number ofk,mIs a 0-1 decision variable used for judging whether the k position is a product type m; k represents the total number of production locations;
Figure BDA0003605394020000037
is the processing start time;
Figure BDA0003605394020000038
representing the processing time of a product with a k production position on a station s; s represents the total number of stations;
Figure BDA0003605394020000039
representing the reloading time of the k-position product on the station s; theta(k-1)l,klJudging whether the product at the position k is the same as the product at the position k-1 in the reloading type for a 0-1 decision variable;
Figure BDA00036053940200000310
is the processing end time; l represents the total number of cycles.
In a further technical solution, the process of updating the basic data set comprises:
and judging whether the production processing time of each station of different products in one period has a significant change or not by utilizing T test, marking and recording the corresponding station if the significant change has occurred, and replacing the data of the corresponding station in the basic data set by using the data collected in one period.
In a further technical scheme, the process of optimizing calculation in the population immunity-genetic algorithm comprises the following steps:
setting parameters and initializing a population;
updating production line state variables according to the collected production data, determining infection states of the three populations, and determining an iterative main population in the production period according to the infection states of the populations;
calculating the fitness of the parent population, and selecting the particles with the highest current fitness value as a global optimal solution in the minimum cycle of a production line;
updating particles in various groups, and retaining the particles by adopting an elite retention strategy;
if the particle is died of illness, randomly selecting a particle from other populations to replace the particle died of illness;
and when the end condition is met, outputting the global optimal solution in the minimum production period and generating a production plan.
According to a further technical scheme, when the seed group is initialized, a workpiece-based coding mode is adopted to form the seed group particles, three HIS x n arrays are generated through a Tent mapping formula to serve as iterative initial seed groups, a scheduling basic data set is used as calculation initial data for calculating a target fitness value, and the production cycle number of a production line is increased by one.
According to the further technical scheme, the production order delivery completion quantity, the production order delivery date and the accessory inventory information are acquired and obtained by the MES system.
According to the further technical scheme, the production and processing time of each station of different products in one period is acquired by the RFID code scanning gun.
The beneficial effects of the invention are as follows: the invention uses the multi-particle iterative function in the coronavirus group immune optimization algorithm, can adaptively change the scheduling target according to the current requirement of a production line, and can also collect and count basic scheduling data, thereby realizing the self-updating of the scheduling basic data and leading the production plan to be more fit with the actual production capacity.
Drawings
FIG. 1 is a schematic structural diagram of a production line data acquisition module according to the present invention;
fig. 2 is a flow chart of the production line status data decision according to the present invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
Firstly, establishing a flow shop target model
Firstly, a production target influencing a production line needs to be found and a target model is established, and then the target is used for decision-making solution, wherein the production target influencing the production line at present mainly comprises: total processing time F taking into account change-over time1Inventory part satisfaction degree F2And the number of times of product switching F3。
1. Establishing a total machining time model considering reloading time
Because the production line needs to spend considerable time on adjusting the production conditions such as clamps of the production line when switching products of different subpackaging types, the minimum production time of the production line is solved by taking the fixture reloading time of the production line into consideration of the total processing time by the model.
Assuming that a certain hybrid assembly line needs to produce m types of products, the position of the product in production commissioning is represented by k, and s represents a station of the product on the hybrid assembly line; wherein M is 1,2, …, M representing the total number of product types; k is 1,2, …, K representing the total number of production locations; s is 1,2, …, S represents the total number of workstations.
Putting product type m with k in cycle l by yklIt is shown that,
Figure BDA0003605394020000051
represents the processing time of the product with the production position k on the station s,
Figure BDA0003605394020000052
the operation time of decomposing a product with the product type m to the station s is represented, so that the processing time of the product at the production position k on the station s is
Figure BDA0003605394020000053
Wherein xk,mIs a 0-1 decision variable used for judging whether the k position is the product type m.
Figure BDA0003605394020000054
Indicating the time of reloading of the product at position k, theta(k-1)l,klAnd judging whether the product at the position k is the same as the product reloading type at the position k-1 or not for a 0-1 decision variable, and supposing that the reloading time of the product at the first position at each station is 0, and not considering the processing time.
The processing starting time of the product with the production position 1 on the station 1 is
Figure BDA0003605394020000055
The end time of the processing is
Figure BDA0003605394020000056
The processing start time of the product with the production position 1 at other stations is
Figure BDA0003605394020000057
Figure BDA0003605394020000058
End time of working
Figure BDA0003605394020000059
i represents an intermediate amount.
When the product with the production position not 1 is at the working position 1, the processing starts
Figure BDA00036053940200000510
End time of working
Figure BDA00036053940200000511
Figure BDA00036053940200000512
Figure BDA00036053940200000513
Wherein K is 2,3, …, K; s is 1.
When the product with the production position not 1 is in the station not 1, whether the next station is in an idle state needs to be judged, so the processing start time
Figure BDA00036053940200000514
End time of working
Figure BDA00036053940200000515
Figure BDA00036053940200000516
Wherein K is 2,3, …, K; s is 2,3, …, S.
The total process time objective function considering the change-over time is:
F1＝min E
the corresponding constraints are:
Figure BDA0003605394020000061
Figure BDA0003605394020000062
Figure BDA0003605394020000063
Figure BDA0003605394020000064
Figure BDA0003605394020000065
Figure BDA0003605394020000066
Figure BDA0003605394020000067
Figure BDA0003605394020000068
Figure BDA0003605394020000069
Figure BDA00036053940200000610
Figure BDA00036053940200000611
wherein:
constraint one means that at the k position, there is only one product type m;
constraint two indicates that the input quantity of all product types in the current production is the same as the planned quantity;
constraint three indicates that the s-1 st station for the k-th position product must be processed after the s-th station;
constraint four indicates that the s station for the k position product must be post-processed at the s station for the k-1 position product;
constraint five represents the processing end time of the first station at the first position;
constraint six represents the product processing end time with the production position of 1;
constraint seven represents the processing end time of the first station of all the products in production;
constraint eight represents the processing end time when the product station with the production position not 1 is not 1;
constraint nine represents a 0-1 decision variable for judging whether the reloading types are the same or not;
constraint ten means that the preparation time is not calculated for the first product;
constraint eleven represents a constraint of the objective function E and the processing end time of each operation at the last station;
l represents the total number of cycles.
2. Satisfaction model of inventory parts
When the production line is assembled, the product types with sufficient parts in the processing storage area are preferentially processed, so that the inventory pressure can be effectively relieved, and the allocation time is reserved for allocating the parts with insufficient parts.
The inventory part satisfaction objective function is:
Figure BDA0003605394020000071
the corresponding constraints are:
Figure BDA0003605394020000072
Figure BDA0003605394020000073
Figure BDA0003605394020000074
Figure BDA0003605394020000075
Figure BDA0003605394020000076
constraint one indicates that at the k position, there is only one product type m;
constraint two indicates that the production quantity of each type of product is consistent with the planned quantity;
constraint three represents Y as the number of types of accessories that are inadequate;
constraint IV represents a 0-1 decision variable for judging whether m types of product accessories are sufficient;
constraint five represents a 0-1 decision variable to determine whether the product type at location k is m.
The symbols in the formula:
ym: a 0-1 decision variable is adopted to judge whether m types of product accessories are sufficient;
y: the number of types of products with insufficient accessories;
dm: m typeThe number of products put into production.
3. Product switching frequency target model
In the actual production process, the assembly time is shorter and the assembly efficiency is more stable by continuously assembling the products of the same type, and the products of the same type are put together and assembled under the condition of meeting other conditions.
Therefore, the objective function for minimizing the switching times per day is as follows:
Figure BDA0003605394020000081
the corresponding constraints are:
Figure BDA0003605394020000082
Figure BDA0003605394020000083
Figure BDA0003605394020000084
Figure BDA0003605394020000085
constraint one means that at the k position, there is only one product type m;
constraint two indicates that the production quantity of each type of product is consistent with the planned quantity;
constraint three is a 0-1 decision variable, and whether the product type at the position k is the same as the product type at the position k +1 is judged;
and the constraint IV is a decision variable from 0 to 1, and whether the product at the position k is of the m type is judged.
The symbol in the formula:
Figure BDA0003605394020000086
and (5) a 0-1 decision variable is used for judging whether the types of the products at the k position in the production period l are the same as the types of the products at the k +1 position.
Secondly, collecting and deciding production line data
In order to enable the solving result of the objective function to be more consistent with the actual production capacity, a production line data acquisition module is established, and meanwhile, decision is made on the selection of the objective model according to the information acquired by the production line data acquisition module.
1. Acquisition module assembly
The production line data acquisition module mainly comprises an MES system, an RFID code scanning gun, a PLC and a computer. The MES system can acquire the delivery completion degree of a production order, the delivery date of the production order and the inventory information of accessories, the RFID code scanning gun can acquire the production and processing time of each station of different products in a period, and the PLC serves as a controller to read, write and receive the data of the IO equipment. As shown in figure 1, the RFID code scanning gun is hung on a bus as IO equipment, the PLC is used as an IO controller to read, write and receive IO equipment data, the acquired data is sent to an upper computer to be processed, and data in the MES system is directly sent to the upper computer through Ethernet communication. The IP addresses of all devices in the acquisition module are ensured to be in the same network segment, and the subnet masks are the same to ensure the successful communication.
2. Production line state data decision main target
The production data collected by the production line data collection module for decision making comprises the following steps: accessory inventory information: required quantity of accessories of various types of products, needlenummAnd the spare part allowance AllNum in the storage aream(ii) a Secondly, the residual production cycle number Surcycle of the order in the batch; ③ SumNum of each type of products in the ordermCompletion EndNummProduction cycle yield CycProduction and production order delivery date; the data is acquired by the MES system. Judging the current production condition of the production line according to the collected production data, thereby deciding the priority of three target models:
if it is not
Figure BDA0003605394020000091
Judging that the production line task cannot be completed in the delivery date, and setting a total processing time minimization target considering the reloading time as a first priority target of the current production plan; wherein the interference coefficient is 0<A≤1；
If it is not
Figure BDA0003605394020000092
If the storage area accessories cannot meet the requirements, preferentially arranging products meeting the storage area accessories to be processed preferentially, and taking the satisfaction degree of the stored parts as a first priority target;
if the former two conditions occur simultaneously, the total processing time of the product is preferably ensured to be shortest, then the product is arranged according to the satisfaction degree of the stored parts in the production period after the delivery period is satisfied, and if the production line has no abnormal state, the production plan is according to normal multiple targets (simultaneously including F)1、F2、F3) The problem is solved, and the decision flow is shown in fig. 2; and carrying out preferential judgment on the multi-target problem solution through a Pareto preferential strategy.
3. Scheduling base dataset self-update
The production-scheduled basic data set is M matrixes of A multiplied by B
Figure BDA0003605394020000093
Figure BDA0003605394020000094
Element x in the matrixabmThe processing time required by processing the B (1,2, …, B) th station in the long data is represented by the processing time of each station of the a (a-1, 2, …, A) th group of products in the m-th type;
the RFID code scanning gun is used for collecting the production and processing time of each station of different products in a period, and a single sample T test is used for judging whether the processing time of a certain station of a production line in the period is changed remarkably or not, namely calculation is carried out
Figure BDA0003605394020000095
If t is greater than the significance level c, then this indicates thatAnd if the change is not obvious, marking the corresponding station and recording, and replacing the data of the corresponding station in the basic data set by using the data acquired in one period, wherein D is the difference between the mean value of the data set acquired in one period and the target test value, S' is the variance of the basic data set, and n is the number of samples of the data set acquired in one period.
Dynamic solution of multi-target model of flow shop
Step (1): collecting processing information of each workstation, and analyzing production line stations;
step (2): establishing a production plan multi-target model according to production rules of a production line;
and (3): the production order delivery completion quantity, the production order delivery date, the accessory inventory information and the production processing time of each station of different products in a period are acquired by the data acquisition module and uploaded to a computer database through a network;
and (4): judging the priority of the multi-objective model according to the delivery completion quantity of the production order, the delivery date of the production order and the inventory information of the accessories, which are acquired in the step (3), and determining an infected population and an optimization main target in a group immunity-genetic algorithm;
setting: considering a total processing time population of the reloading time, and corresponding to a population one, wherein the optimization main objective of the population one is to minimize the total processing time considering the reloading time; the satisfaction degree population of the inventory parts corresponds to the second population, and the optimization main target of the second population is the minimization of the satisfaction degree of the inventory parts; the production line normally produces a population, corresponds to a population III, and the optimization main objective of the population III is to consider the minimization of the total processing time of the reloading time, the minimization of the satisfaction degree of stored parts and the minimization of the switching times of products;
the production data is collected through the data collection module, and the current production condition of the production line is distinguished: the current production condition of the production line is that the delivery date of the product is urgent, the first population is an infected population, the first population is iterated preferentially, and the second and third populations are uninfected populations; if the current production condition of the production line is insufficient supply of accessories, the second population is an infected population, the second population is iterated preferentially, and the first population and the third population are uninfected populations; if the current production condition of the production line is normal, the first population and the second population are recovered or are not infected, and the third population is preferentially iterated; the current production condition of the production line is that the product delivery date is urgent and the supply of accessories is insufficient, infection is carried out according to the priority of two populations, wherein the first population is an infected population and is preferentially iterated until recovery, the second population is a susceptible population and is infected after the first population is recovered and is preferentially iterated until recovery;
and (5): according to the production and processing time data of each station of different products in one period collected in the step (3), judging whether the processing time of a certain station of a production line in one period is remarkably changed or not by utilizing single sample T inspection, and whether the basic data set needs to be updated or not;
and (6): inputting the updated basic data set in the step (5) and the optimized main target decided in the step (4) into the population immune-genetic algorithm for optimizing calculation, calculating an optimal solution according with the actual production condition, and outputting a production plan;
the group immunity-genetic algorithm is used for optimizing calculation, and comprises the following specific steps:
step 1): setting parameters, including: the method comprises the following steps of (1) obtaining an initial population size HIS, a production line state variable LineState, a population infection state variable PopState, a maximum iteration number MaxItr, a maximum production line production Cycle number MaxCypleNum, a production Cycle number CycleNum, a minimum production Cycle, an infection generation Age, a maximum infection generation number MaxAgage, a cross probability crossState, and a variation probability InvRate; iterative target fitness value: total processing time F1Inventory part satisfaction degree F2Product switching times F3，
Step 2): initializing a population, forming population particles by adopting a coding mode based on a workpiece, and mapping the population particles by a Tent mapping formula:
Figure BDA0003605394020000111
three arrays of HIS × n were generated as iterative initial populations:
Figure BDA0003605394020000112
Figure BDA0003605394020000113
meanwhile, taking a scheduling basic data set as initial calculation data for calculating a target fitness value, and adding one to the production cycle number of a production line;
wherein:
Figure BDA0003605394020000114
the z (i ═ 1,2, …, n) th element indicating the e (e ═ 1,2, …, HIS) th particle in the g (g ═ 1,2, …, HIS) th population, g indicates the population number, e indicates the particle number, z indicates the element number, n indicates the maximum number of elements in the particle, and rand indicates a random number.
Step 3): acquiring production data acquired by the data acquisition module, updating production line state variables (including whether the product delivery date is urgent and whether the accessory supply is sufficient), determining infection states of the three populations, and determining an iterative main population (an objective of the iterative main population is an optimization main objective) in the production period according to the infection states of the populations;
step 4): calculating the fitness of the parent population, and selecting the particles with the highest current fitness value (namely the objective function value) as the global optimal solution in the minimum period of the production line;
step 5): updating particles in various groups through a social distance formula among the particles, wherein a particle retention mechanism adopts an elite retention strategy, namely calculating that the fitness value of offspring is better than that of parent, replacing parent particles with offspring particles, updating the infection algebra of the particles, and adding one to the infection algebra if the parent particles of the current round are not replaced and the fitness value is lower than the average value;
step 6): judging whether the infection algebra of each particle in the population exceeds the maximum infection algebra, if so, judging that the particle is ill-fatted, and randomly selecting one particle from other populations to replace the particle which is ill-fatted;
step 7): carrying out partial mapping intersection and variation search operation on the particles in the population according to the intersection probability and the variation probability to further optimize the particles, enhancing the local search capability and the global search capability of the algorithm, and updating the particles by applying the elite retention strategy;
step 8): judging whether the iteration termination condition of the current round is met, namely whether the iteration is iterated to the maximum algebra, if the iteration termination condition is met, outputting the global optimal solution in the minimum production period and generating a production plan, and then turning to the step 2) to carry out the iterative optimization in the next production period; otherwise, returning to the step 4) to continue the iterative optimization.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims (10)

1. An intelligent production scheduling method for a mixed flow production line based on a colony immunity-genetic algorithm is characterized by comprising the following steps:
collecting delivery completion quantity of production orders, delivery date of the production orders and inventory information of accessories, judging the priority of the multi-objective model, determining infected populations in a group immunity-genetic algorithm and optimizing main objectives;
collecting production and processing time data of each station of different products in a period, and judging whether a basic data set needs to be updated or not;
and inputting the updated basic data set and the optimized main target into a population immune-genetic algorithm for optimizing calculation, calculating an optimal solution according with the actual production condition, and outputting a production plan.
2. The mixed flow production line intelligent production scheduling method based on the swarm immunity-genetic algorithm as claimed in claim 1, wherein the priority of the multi-objective model is evaluated to determine the infected swarm and optimize the main objective in the swarm immunity-genetic algorithm, specifically:
if the current production condition of the production line is that the product delivery date is urgent, the first population is an infected population, the first population is iterated preferentially, and the second and third populations are uninfected populations;
if the current production condition of the production line is insufficient supply of accessories, the second population is an infected population, the second population is iterated preferentially, and the first population and the third population are uninfected populations;
if the current production condition of the production line is normal, the population I and the population II are recovered or are not infected, and the population III is preferentially iterated;
the current production condition of the production line is that the product delivery date is urgent and the accessory supply is insufficient, infection is carried out according to the priority of two populations, wherein the first population is an infected population, and iteration is carried out until recovery is carried out preferentially, and the second population is a susceptible population, is infected after the first population recovers, and iteration is carried out preferentially until recovery is carried out preferentially;
the first population corresponds to the total processing time considering the reloading time, and the optimization main objective of the first population is to minimize the total processing time considering the reloading time;
the second population corresponds to the satisfaction degree of the inventory parts, and the optimization main target of the second population is the minimization of the satisfaction degree of the inventory parts;
the third population corresponds to normal production of a production line, and the optimization main target of the third population is to consider the minimization of the total processing time of the reloading time, the minimization of the satisfaction degree of the stored parts and the minimization of the switching times of products.
3. The group immunity-genetic algorithm-based mixed flow production line intelligent production scheduling method of claim 2, wherein the product delivery time meets the following requirements in emergency:
Figure FDA0003605394010000011
wherein: sumnummEndNum for each type of product total in the ordermFor the finished quantity of each type of product of the order in the batch, CycProduction is the production cycle yield of each type of product of the order in the batch, SurCycle is the residual production cycle number of the order in the batch, and A is an interference coefficient.
4. The group immunity-genetic algorithm-based intelligent production line scheduling method for mixed flow production line according to claim 2, wherein the supply shortage of accessories is satisfied:
Figure FDA0003605394010000021
wherein: needlenummIndicating the part demand, AllNum, for each type of productmIndicating the remaining amount of accessories in the storage area.
5. The group immunity-genetic algorithm-based intelligent production scheduling method for the mixed flow production line according to claim 2, wherein when the production condition is normal, the production plan is solved according to a multi-objective problem.
6. The group immunity-genetic algorithm-based intelligent production scheduling method for mixed flow production lines as claimed in claim 2, wherein the total processing time objective function considering reloading time is as follows:
F1＝min E
the constraint conditions are as follows:
Figure FDA0003605394010000022
Figure FDA0003605394010000023
Figure FDA0003605394010000024
Figure FDA0003605394010000025
Figure FDA0003605394010000026
Figure FDA0003605394010000027
Figure FDA0003605394010000028
Figure FDA0003605394010000029
Figure FDA00036053940100000210
Figure FDA00036053940100000211
Figure FDA00036053940100000212
constraint one means that at the k position, there is only one product type m;
constraint two indicates that the input quantity of all product types in the current production is the same as the planned quantity;
constraint three indicates that the s-1 st station for the k-th position product must be processed after the s-th station;
constraint four indicates that the s station for the k position product must be post-processed at the s station for the k-1 position product;
constraint five represents the processing end time of the first station at the first position;
constraint six represents the product processing end time with the production position of 1;
constraint seven represents the processing end time of the first station of all the products in production;
constraint eight represents the processing end time when the product station with the production position not 1 is not 1;
constraint nine represents a 0-1 decision variable for judging whether the reloading types are the same or not;
constraint ten means that the preparation time is not calculated for the first product;
constraint eleven represents a constraint of the objective function E and the processing end time of each operation at the last station;
wherein: x is the number ofk,mIs a 0-1 decision variable used for judging whether the k position is a product type m; k represents the total number of production locations;
Figure FDA0003605394010000031
is the processing start time;
Figure FDA0003605394010000032
representing the processing time of a product with a k production position on a station s; s represents the total number of stations;
Figure FDA0003605394010000033
representing the reloading time of the k-position product on the station s; theta(k-1)l,klJudging whether the product at the position k is the same as the product at the position k-1 in the reloading type for a 0-1 decision variable;
Figure FDA0003605394010000034
is the processing end time; l represents the total number of cycles.
7. The group immunity-genetic algorithm-based mixed flow production line intelligent production scheduling method of claim 1, wherein the process of updating the basic data set is as follows:
and judging whether the production processing time of each station of different products in one period has a significant change or not by utilizing T test, marking and recording the corresponding station if the significant change has occurred, and replacing the data of the corresponding station in the basic data set by using the data collected in one period.
8. The group immunity-genetic algorithm-based intelligent production line scheduling method for the mixed flow production line according to claim 1, wherein the group immunity-genetic algorithm comprises the following steps:
setting parameters and initializing a population;
updating production line state variables according to the collected production data, determining infection states of the three populations, and determining an iteration main population in the production period according to the population infection states;
calculating the fitness of the parent population, and selecting the particles with the highest current fitness value as a global optimal solution in the minimum cycle of a production line;
updating the particles in various groups, and adopting an elite retention strategy to retain the particles;
if the particle is died of illness, randomly selecting a particle from other populations to replace the particle died of illness;
and when the end condition is met, outputting the global optimal solution in the minimum production period and generating a production plan.
9. The group immunity-genetic algorithm-based mixed flow production line intelligent production scheduling method of claim 8, wherein during initializing the population, a workpiece-based coding mode is adopted to form population particles, three HIS x n arrays are generated through a Tent mapping formula to serve as an iterative initial population, a production scheduling basic data set is used as calculation initial data for calculating a target fitness value, and the production line production cycle number is increased by one.
10. The group immunity-genetic algorithm-based mixed flow production line intelligent production scheduling method of claim 1, wherein the production order delivery completion amount, the production order delivery date and the accessory inventory information are acquired by an MES system; and the production and processing time of each station of different products in one period is acquired by the RFID code scanning gun.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116090676A (en) * 2023-04-10 2023-05-09 武汉益模科技股份有限公司 Multi-objective optimization-based APS (automatic generation system) scheduling method and system
CN116090676B (en) * 2023-04-10 2023-07-04 武汉益模科技股份有限公司 Multi-objective optimization-based APS (automatic generation system) scheduling method and system

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