CN109491344A - Intelligent coordinated dispatching method and system towards airspace engine development process - Google Patents

Abstract

The invention provides an intelligent collaborative scheduling method for the development process of an aerospace engine. The method includes: S100, setting algorithm parameters; S200, generating an initial firefly population according to the delivery date of each workpiece; S300, calculating each firefly population in the current firefly population. The fitness of an individual, the individual with the lowest fitness is taken as the first individual, the solution corresponding to the first individual is taken as the optimal solution of this iteration process, and the global optimal solution is updated according to the optimal solution S400, determine whether the current number of iterations reaches the maximum number of iterations: if so, output the current global optimal solution; otherwise, update each individual in the current firefly population according to the preset individual update strategy, and obtain the updated the firefly population, and return to step S300. The invention can improve the manufacturing and assembling efficiency of the aerospace engine.

Description

Intelligent collaborative scheduling method and system for aerospace engine development process

technical field

The invention relates to the technical field of industrial scheduling, in particular to an intelligent collaborative scheduling method, system and storage medium oriented to the development process of an aerospace engine.

Background technique

In aerospace, shipbuilding, military and other fields, the manufacturing process of equipment is complex and the industrial chain is long. In the process of its development, there is a common production process route of assembly line scheduling. Among them, the production process route of aerospace engine mainly includes: alloy material forging to form parts, parts assembly and product testing and other manufacturing processes. The development cycle of aerospace engines is long, the capital investment is huge, and they are the most important weapons of the country and the cutting-edge of aerospace equipment. Therefore, optimizing the development process of aerospace engines is of great significance to the construction of national defense and the improvement of comprehensive national strength.

SUMMARY OF THE INVENTION

(1) Technical problems solved

The invention provides an intelligent collaborative scheduling method, system and storage medium oriented to the development process of an aerospace engine, which can improve the manufacturing and assembling efficiency of the aerospace engine.

(2) Technical solutions

To achieve the above purpose, the present invention is achieved through the following technical solutions:

In a first aspect, the present invention provides an intelligent collaborative scheduling method for the development process of aerospace engines, including:

S100. Set algorithm parameters; the algorithm parameters include at least the number of workpieces required in the aerospace engine manufacturing and assembly, the number of required processes, the number of machines in each process, the delivery date of each workpiece, the maximum number of iterations, and the number of individuals in a firefly population;

S200, generating an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution includes a machine for processing each workpiece in each process;

S300. Calculate the fitness of each individual in the current firefly population, take the individual with the lowest fitness as the first individual, and take the solution corresponding to the first individual as the optimal solution of this iterative process, and according to the The optimal solution updates the global optimal solution;

S400. Determine whether the current number of iterations reaches the maximum number of iterations:

If so, output the current global optimal solution;

Otherwise, according to the preset individual update strategy, update each individual in the current firefly population to obtain an updated firefly population, and return to step S300.

In the second aspect, the present invention provides an intelligent collaborative scheduling system for the development process of aerospace engines, including:

The parameter setting module is used for executing S100 and setting algorithm parameters; the algorithm parameters include at least the number of workpieces required in the aerospace engine manufacturing and assembly, the number of required processes, the number of machines in each process, and the handover of each workpiece. Delivery period, maximum number of iterations and the number of individuals in the firefly population;

The population generation module is used to execute S200, and generate an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution includes each workpiece to be processed on each process respectively the machine;

The optimal solution calculation module is used for executing S300, calculating the fitness of each individual in the current firefly population, and taking the individual with the lowest fitness as the first individual, and taking the solution corresponding to the first individual as this iteration The optimal solution of the process, and update the global optimal solution according to the optimal solution;

The number of times judgment module is used to execute S400, and judge whether the current number of iterations reaches the maximum number of iterations: if so, output the current global optimal solution; otherwise, according to the preset individual update strategy, each individual in the current firefly population is Perform the update to obtain the updated firefly population, and return to the optimal solution calculation module to execute step S300.

In a third aspect, the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above machine scheduling method can be implemented.

(3) Beneficial effects

The method, system and storage medium provided by the present invention are actually aimed at the flow shop problem in the development process of aerospace high-end equipment. First, the initial firefly population is generated on the basis of considering the delivery time, and then the individual with the lowest fitness value is selected as the The first individual, and then update the individuals in the population. After several iterations, the first individual gradually approaches the optimal solution for solving the scheduling problem, and then outputs it as the global optimal solution. In the process of its high-end equipment, it can make full use of its production resources, reduce production costs, improve manufacturing and assembly efficiency, and improve equipment manufacturing management level and manufacturing capacity. The invention adopts the improved firefly algorithm, which shows good performance in terms of convergence speed and search solution quality, solves the problem of flow shop scheduling in the case of multiple machines, and improves the equipment manufacturing production management level of equipment manufacturing enterprises .

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

FIG. 1 shows a schematic flowchart of an intelligent collaborative scheduling method for an aerospace engine development process in an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In a first aspect, the present invention provides an intelligent collaborative scheduling method for the development process of an aerospace engine. The machine scheduling method includes:

S100. Set algorithm parameters; the algorithm parameters include at least the number n of workpieces required in the aerospace engine manufacturing and assembly, the number of required processes O, the number M of machines in each process, the delivery date of each workpiece, the maximum The number of iterations I _max and the number of individuals in the firefly population Q;

Of course, the current iteration number I=1, the preset Euclidean distance, etc. are also set.

S200, generating an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution includes a machine for processing each workpiece in each process;

It is understandable that, since the firefly population is updated in each iteration process, the I-th iteration process corresponds to the I-generation firefly population.

For example, the jth individual of the first generation firefly population corresponds to a solution, and a solution is a machine scheduling scheme, which can be expressed as:

Wherein, _1≤I≤Imax , j=1,2,...,Q, i=1,2,...,n, o=1,2,...,O.

in, Represents the processing machine of the i-th workpiece in the j-th individual X ^Ij of the first-generation firefly population processed on the o-th process.

In practical applications, the process of generating an initial firefly population can include:

S201. Sort all the workpieces in a non-decreasing manner of delivery time to obtain a collection of workpieces;

It can be understood that, after the sorting, the delivery date of the workpieces in the front row in the workpiece set is earlier, and the delivery date of the workpieces in the back row is later.

S202, perform machine allocation to each workpiece in the workpiece collection one by one according to the arrangement order to obtain an individual;

The specific allocation process includes: allocating each workpiece to the corresponding machine in each process, and if there are multiple idle machines, it is allocated to the idle machine with the fastest processing speed. Of course, if all machines are in processing, the workpiece can be assigned to the machine that has received the least number of workpieces to be processed, but if the number of pending workpieces is the same, the machine with the fastest processing speed is selected to process the workpiece.

It is understandable that the machines are allocated to the workpieces in the workpiece collection one by one according to the arrangement order. The machines are allocated to the workpieces with earlier delivery dates first, and then the delivery dates are allocated to the workpieces with later delivery dates. It can be seen that the initial population is generated. When considering the delivery time factor, the first individual selected in the initial population is more in line with the actual situation.

For example, when the machine of the oth operation is allocated to the ith workpiece, there is a machine among the M machines in the oth operation that is idle at this time, and this a machine includes the new machine and the old machine, the new machine The processing speed of the machine is relatively fast, and the processing speed of the old machine is slow. At this time, the i-th workpiece can be assigned a new machine among the idle machines in the o-th operation.

S203, randomly generate Q-1 individuals; Q is the number of individuals in the population.

Here, an individual in the firefly population is generated in a heuristic manner, and the other individuals are randomly generated, which can improve the quality of the initial firefly population, and then select the first individual with good quality.

It is understandable that the functions of the M machines in each process are the same, that is, the processing effect is the same, and at most one workpiece can be processed at the same time.

S300: Calculate the fitness of each individual in the current firefly population, take the individual with the lowest fitness as the first individual, and take the solution corresponding to the first individual as the optimal solution of this iteration process, and according to The optimal solution updates the global optimal solution;

Among them, the first individual is the individual with the best quality in the current firefly population, which can be used as the optimal solution of this iteration process.

In practical applications, the calculation process of the fitness value of each individual in the population may include:

S301. Perform feasibility transformation on the solution corresponding to the individual, so that the values of all dimensions of the solution are in the range of [1, M], and the transformation process includes: judging whether each element in the solution corresponding to the individual is in the interval Within the range of [1, M], if so, the element remains unchanged, otherwise the element is set to be M;

It is understandable that rounding up can ensure that the elements of each dimension of the optimal solution after the feasibility transformation are integers, and if the rounding exceeds [1, M], an integer is randomly selected within the range for replacement, so that All dimension elements of the optimal solution can be guaranteed to be in the range [1, M]. The reason for this is that there are only M machines in each process, so the value of each dimension element is in the range of [1, M].

S302. Determine the workpieces processed by each machine in each process according to the solution corresponding to the individual, and calculate the final completion time for each machine in the last process to process the workpieces, respectively, and each machine in the last process. The maximum value of the final completion time of workpiece processing is taken as the time required for aerospace engine manufacturing and assembly;

For example, the set of workpieces processed by the mth machine on the oth operation is will be assembled The workpieces in are sorted according to their non-decreasing delivery time, thus forming a sorted set of workpieces. like If it is an empty set, randomly select a workpiece from the set with the largest number of workpieces in each set corresponding to each machine on the oth process to be processed by the mth machine, thereby reducing the idle time of the mth machine.

then, Indicates the k-th workpiece processed by the m-th machine in the o-th process. If k=1, the processing completion time of the k-th workpiece on the m-th machine in the o-th process is: If k is greater than 1, the processing completion time of the k-th workpiece on the m-th machine of the o-th process is: Among them, p _om is the time required for the m-th machine of the o-th process to process the workpiece, is the processing completion time of the k-1th workpiece of the mth machine on the oth process, is the processing completion time of the kth workpiece of the mth machine on the oth process, is the processing completion time of the kth workpiece on the machine of the o-1th operation. By calculating the processing completion time of each workpiece of the m-th machine on the o-th process, the final completion time of the m-th machine on the o-th process can be obtained. express, The formula is as follows:

in, Represents a collection the number of workpieces in the is the final completion time of the mth machine on the oth process.

Further, according to the final completion time corresponding to each machine in each process, the maximum value of the final completion time of each machine in the last process for workpiece processing can be determined, and the maximum value can be expressed by _Cmax . The formula of _Cmax as follows:

It is understandable that the maximum value is the time required for the manufacture and assembly of the aerospace engine, that is, the time span from the start of processing the first workpiece in the first process to the completion of the processing of the last workpiece in the last process. The smaller it is, the higher the efficiency of aerospace engine manufacturing and assembly.

S303. According to the final completion time of each workpiece on the corresponding machine of the last process and the respective delivery date of each workpiece, determine the number of workpieces whose completion time on the corresponding machine of the last process is later than the delivery date, and This quantity is recorded as the number of workpieces that have not completed the processing task before the delivery date;

For example, in the workpiece set J={J ₁ ,...,J _i ,...,J _n }, the final completion time of each workpiece on the corresponding machine of the last process is compared with the delivery time of the workpiece , if the final completion time of the workpiece is later than the delivery date, it means that the workpiece cannot be processed before the delivery date, and all the workpieces that cannot be completed before the delivery date form a set J _d , |J _d | The number of workpieces, that is, the number of workpieces that have not completed the processing task by the delivery date.

S304. Calculate the fitness value corresponding to the individual according to the time required for manufacturing and assembling the aerospace engine and the number of workpieces that have not completed the processing task before the delivery date.

In practical applications, the fourth formula can be used to calculate the fitness value corresponding to the individual; the fourth formula includes:

In the formula, F is the fitness value corresponding to the individual, W ₁ and W ₂ are weight coefficients and W ₁ +W ₂ =1, C _max is the time required for the manufacture and assembly of the aerospace engine, and J _d is the non-delivery date The set of workpieces that have completed the processing tasks before, |J _d | is the number of workpieces that have not completed the processing tasks before the delivery date, d _f is the delivery date of the f-th workpiece, and P _f is the f-th workpiece in the last process. The final completion time on the corresponding machine.

It can be understood that, from the above formula, the calculation of the fitness value considers the time required for the manufacture and assembly of the aerospace engine and the number of workpieces that have not completed the processing task before the delivery date, so the fitness value is this The comprehensive embodiment of the two factors, the lower the fitness value, the higher the quality of the corresponding solution, so the solution corresponding to the individual with the lowest fitness value is taken as the optimal solution.

It can be seen from the above fourth formula that the present invention has two optimization objectives, wherein the first optimization objective is the time required to process the first workpiece in the first process to the completion of the processing of the last workpiece in the last process The span is shortened, another optimization goal is to reduce the number of workpieces that cannot be processed by the delivery date.

In the actual environment, due to the different life spans of the production machines, the production environment is complicated by mixing new and old production equipment on the production line. If only a single problem is considered, it is not in line with the actual situation, and the method provided by the present invention actually has two The optimization goal is to shorten the time span, and the other is to reduce the number of workpieces that cannot be processed before the delivery date. Considering that different equipment has different processing times, the scheduling scheme formulated is suitable for multiple machines in actual situations. scheduling problem.

In practical applications, the above-mentioned process of updating the global optimal solution according to the optimal solution may specifically include: comparing the optimal solution, that is, the first individual, with the fitness value of the current global optimal solution, if the optimal solution That is, the fitness value of the first individual is lower than the fitness value of the current global optimal solution, then the global optimal solution is updated to the optimal solution, that is, the first individual, otherwise, the global optimal solution remains unchanged.

S400. Determine whether the current number of iterations reaches the maximum number of iterations:

If so, output the current global optimal solution;

Otherwise, according to the preset individual update strategy, update each individual in the current firefly population to obtain an updated firefly population, and return to step S300.

It is understandable that through the iterative process, the solution space is continuously searched, and the global optimal solution is finally obtained. In practical applications, when the global optimal solution is output, the fitness value of the global optimal solution can also be output, so that the pros and cons of the global optimal solution can be known.

Here, the individual update strategy is set, local search is performed, and the search population can continuously improve the quality of the population through operations such as crossover and retention.

In practical applications, different individuals in the current firefly population can be updated in different ways. For example, the process of updating each individual in the current firefly population can include the following steps:

S401, calculating the Euclidean distance between each individual in the current firefly population and the first individual;

S402: Construct a first set of individuals whose Euclidean distance is less than a preset distance and greater than 0 in the current firefly population, and constitute a second individual in the current firefly population whose Euclidean distance is greater than or equal to the preset distance gather;

Understandably, the Euclidean distance between two individuals can be used as a basis for judging the similarity between two individuals.

The preset distance is R, the individuals with 0<r<R in the population constitute the first individual set S ^top , and the individuals with r>R and r=R constitute the second individual set S ^least .

S403. Use the first update strategy to update each individual in the first set of individuals, use the second update strategy to update each individual in the second set of individuals, and use the third update strategy to update each individual in the second set of individuals. The first entity is updated.

Wherein, the process of using the first update strategy to update each individual in the first individual set in S403 includes:

The following steps are performed for each individual in the first set of individuals:

S4031a, assign this individual to the first individual variable X ^temp in the first individual set;

It is understandable that the individual in the first individual set is assigned to the first individual variable X ^temp , so the first individual variable X ^temp records the dimension elements of the individual in the first individual set before the update. .

S4032a. According to each dimension element of the first individual variable, each dimension element of the first individual and the number of machines in each process, use the first formula to calculate the Each dimension element of the individual after the update. Wherein, the first formula includes:

In the formula, is the k-th individual in the first individual set S ^top In the updated l-th dimension element, is the lth dimension element of the first individual, is the lth dimension element of the first individual variable X ^temp , M is the number of machines in each process, Indicates that x is rounded up, and x%y means that x takes the remainder of y.

It is understandable that the above subscript l is a positive integer less than or equal to the total dimension of the individual.

All dimensional elements of an individual in the first individual set are respectively updated using the first formula, so as to realize the updating of the individual in the first individual set. All individuals in the first individual set are respectively updated in the above manner, so as to realize the updating of the first individual set.

Wherein, the process of using the second update strategy to update each individual in the second set of individuals in S403 may include:

The following steps are performed for each individual in the second set of individuals:

S4031b, calculating the average fitness value of the current firefly population according to the fitness value of each individual in the current firefly population; and using the second formula to calculate the first fitness value according to the fitness value of the first individual and the average fitness value A parameter, the second formula includes:

In the formula, P is the first parameter, ^Favg is the average fitness value, and ^Ftop is the fitness value of the first individual;

It is understandable that when the current firefly population remains unchanged, its average fitness value remains unchanged, and the fitness value of the first individual is also a fixed value, so the first parameter is also a fixed value. When the population is updated and changed, the first parameter will also change accordingly.

S4032b, generating a first random number random1, and determining whether the first random number random1 is less than or equal to the first parameter P:

If so, execute S4033b;

Otherwise, skip to S4035b;

S4033b, according to the fitness value of the first individual and the fitness value of the individual in the second individual set, use a third formula to calculate the second parameter; the third formula includes:

where CR is the second parameter, is the fitness value of the kth individual in the second individual set S ^least ;

The above-mentioned second parameter may be referred to as a crossover probability parameter.

where k is a positive integer less than or equal to the total number of individuals in the second individual set S ^least .

The third formula is used to calculate the second parameter for each individual in the second individual set S ^least , and the second parameter is different for different individuals.

S4034b. For each dimension element of the individual in the second individual set, perform the steps of: generating a second random number random2, and judging whether the second random number random2 is less than or equal to the second parameter CR: if so, Then replace the dimension element of the individual in the second individual set with the corresponding dimension element of the first individual; otherwise, the dimension element of the individual in the second individual set remains unchanged;

It is understandable that by performing step S4034b for all elements of an individual in the second individual set, the substitution transformation of the individual in the second individual set can be implemented.

S4035b. Randomly generate two index subscripts, the two index subscripts include a first subscript and a second subscript, and assign the individual in the second individual set to the first subscript and the second subscript Elements between subscripts are exchanged symmetrically to achieve an update to that individual.

For example, if the generated two subscripts are 1 and 7, the symmetric transformation of the individual elements between the 1st dimension and the 7th dimension is performed: the element transformation of the 1st dimension and the 7th dimension, the 2nd dimension and the 6th dimension Element transformation, 3rd dimension and 5th dimension element transformation, 4th dimension element unchanged.

Performing the above steps for each individual in the second individual set can implement the update of the second individual set.

Here, performing operations such as exchange and mutation on the first set of individuals and/or the second set of individuals can improve the diversity of the population, ensure the quality of individuals, and at the same time help to improve the local search ability of the algorithm, and effectively avoid algorithm overloading. Convergence early.

Wherein, the process of using the third update strategy to update the first individual in S403 may include:

S4031c, performing substitution mutation on the first individual to obtain a first mutant individual;

The specific process of replacing mutation may include: defining a first mutation individual X ₁ , randomly generating three mutually different index subscripts, and moving the elements between the first two subscripts on the first individual to the last two indexes At the position between the subscripts, a first mutant individual X ₁ is obtained.

For example, if the first body has 5 elements, the three index subscripts are 1, 3, 4, and the element between 1 and 3 is 2, then move the 2nd dimension element on the first body to the 3rd dimension Between the element and the 4th dimension element, although the 2nd dimension element is lost, an element is added between the 3rd dimension and the 4th dimension, so the dimension of the first individual remains unchanged.

S4032c, performing M times of exchange mutation on the first individual to obtain M second mutant individuals;

The specific process of exchange mutation may include: defining a second mutant individual X ₂ , randomly generating two mutually different index subscripts, and exchanging the elements of the first individual at the two index subscript positions with each other to obtain a The second variant individual X ₂ . By repeating this step M times, M second mutant individuals X ₂ can be obtained.

S4033c, performing element movement mutation M times on the first individual to obtain M third mutant individuals;

The specific process of element movement mutation may include: defining a third mutation individual X ₃ , randomly generating two mutually different index subscripts, and moving the element at the position of one index subscript on the first individual to another index subscript position, get a third mutant individual X ₃ . By repeating this step M times, M third mutant individuals X ₃ can be obtained.

For example, if the subscripts of the two indices are 1 and 3, then the 1st dimension element of the first individual is moved to the 3rd dimension, and the positions of the original 3rd dimension and subsequent elements are moved one position backward. This way Nor does it change the total dimensionality of the first individual.

S4034c: Calculate the fitness value of each mutant individual respectively, and determine whether the highest fitness value is greater than the fitness value of the first individual:

If so, replace the first individual with the mutant individual with the highest fitness value, and execute S4035c;

Otherwise, execute S4035c;

S4035c: Select an individual with the highest fluorescence brightness from the updated first individual set and the updated second individual set, and perform cross processing on the individual with the highest fluorescence brightness and the first individual to obtain an update The first individual after treatment.

In practical applications, the specific process of cross processing can include:

1. Define a position variable rand, and rand satisfies the condition 1≤rand≤n, rand∈Z ⁺ ;

2. Randomly generate a value within the range that satisfies the condition of the position variable rand, and assign the value to the variable rand;

3. Swap all elements behind rand in the two individuals according to their original relative positions, thereby obtaining two new individuals.

Through the above mutation methods such as replacement, exchange, and insertion, the movement of the first individual is realized, thereby realizing the update of the first individual.

Here, the movement of the first individual is realized by performing multiple mutation operations on the original first individual, which is beneficial to improve the global search ability of the algorithm and effectively avoid the algorithm from falling into a local optimal solution.

The method provided by the invention is actually aimed at the flowshop problem in the development process of aerospace high-end equipment. First, the initial firefly population is generated on the basis of considering the delivery time, and then the individual with the lowest fitness value is selected as the first individual, and then The individuals in the population are updated, and after multiple iterations, the first individual is gradually approached to the optimal solution for solving the scheduling problem, and then it is output as the global optimal solution. It can make full use of its production resources to the greatest extent, reduce production costs, and improve equipment manufacturing management level and manufacturing capacity. The invention adopts the improved firefly algorithm, which shows good performance in terms of convergence speed and search solution quality, solves the problem of flow shop scheduling in the case of multiple machines, and improves the equipment manufacturing production management level of equipment manufacturing enterprises .

In a second aspect, the present invention provides an intelligent collaborative scheduling system for the development process of aerospace engines, the system comprising:

The parameter setting module is used for executing S100 and setting algorithm parameters; the algorithm parameters include at least the number of workpieces required in the aerospace engine manufacturing and assembly, the number of required processes, the number of machines in each process, and the handover of each workpiece. Delivery period, maximum number of iterations and the number of individuals in the firefly population;

The population generation module is used to execute S200, and generate an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution includes each workpiece to be processed on each process respectively the machine;

The optimal solution calculation module is used for executing S300, calculating the fitness of each individual in the current firefly population, and taking the individual with the lowest fitness as the first individual, and taking the solution corresponding to the first individual as this iteration The optimal solution of the process, and update the global optimal solution according to the optimal solution;

The number of times judgment module is used to execute S400, and judge whether the current number of iterations reaches the maximum number of iterations: if so, output the current global optimal solution; otherwise, according to the preset individual update strategy, each individual in the current firefly population is Perform the update to obtain the updated firefly population, and return to the optimal solution calculation module to execute step S300.

In a third aspect, the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above scheduling method can be implemented.

It can be understood that the system provided in the second aspect and the storage medium provided in the third aspect correspond to the method provided in the first aspect, and the explanations, examples, implementations, etc. of the relevant content may refer to the corresponding content in the first aspect. part, which will not be repeated here.

It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. An intelligent cooperative scheduling method for a development process of an aerospace engine is characterized by comprising the following steps:

s100, setting algorithm parameters; the algorithm parameters at least comprise the number of workpieces required in the manufacturing and assembling of the space engine, the number of required working procedures, the number of machines on each working procedure, the delivery date of each workpiece, the maximum iteration number and the individual number in the firefly population;

s200, generating an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution comprises a machine for processing each workpiece in each process;

s300, calculating the fitness of each individual in the current firefly population, taking the individual with the lowest fitness as a first individual, taking a solution corresponding to the first individual as an optimal solution of the iteration process, and updating a global optimal solution according to the optimal solution;

s400, judging whether the current iteration number reaches the maximum iteration number:

if so, outputting the current global optimal solution;

otherwise, updating each individual in the current firefly population according to a preset individual updating strategy to obtain an updated firefly population, and returning to the step S300.

2. The method of claim 1, wherein said generating an initial population of fireflies comprises:

s201, sequencing all workpieces according to a non-decreasing delivery date mode to obtain a workpiece set;

s202, performing machine allocation on each workpiece in the workpiece set one by one according to the arrangement sequence to obtain an individual;

s203, randomly generating Q-1 individuals; q is the number of individuals in the population.

3. The method of claim 1, wherein updating each individual of the current firefly population according to a preset individual update strategy to obtain an updated firefly population comprises:

s401, calculating Euclidean distances between each individual in the current firefly population and the first individual respectively;

s402, forming individuals with Euclidean distances smaller than a preset distance and larger than 0 in the current firefly population into a first individual set, and forming individuals with Euclidean distances larger than or equal to the preset distance in the current firefly population into a second individual set;

and S403, updating each individual in the first individual set by adopting a first updating strategy, updating each individual in the second individual set by adopting a second updating strategy, and updating the first individual by adopting a third updating strategy.

4. The method of claim 3, wherein said updating each individual of the first set of individuals using a first update policy comprises:

performing, for each individual of the first set of individuals, the steps of:

s4031a, assigning the individual in the first individual set to a first individual variable;

s4032a, calculating each updated dimension element of the first individual in the first individual set according to each dimension element of the first individual variable, each dimension element of the first individual, and the number of machines in each process, using a first formula, where the first formula includes:

in the formula,for the first individual set S^topMiddle k individualIn the updated l-th dimension element,is the i-th dimension element of the first individual,is the first volume variable X^tempThe l-th dimension element, M is the number of machines in each process,representing the rounding up of x, x% y represents the remainder of x taking y.

5. The method of claim 3, wherein said updating each individual of the second set of individuals using a second update policy comprises:

performing the following steps for each individual of the second set of individuals:

s4031b, calculating the average fitness value of the current firefly population according to the fitness value of each individual in the current firefly population; and calculating a first parameter by using a second formula according to the fitness value of the first individual and the average fitness value, wherein the second formula comprises:

wherein P is a first parameter, F^avgAs said average fitness value, F^topA fitness value for the first individual;

s4032b, generating a first random number, and determining whether the first random number is less than or equal to the first parameter:

if yes, go to S4033 b;

otherwise, go to S4035 b;

s4033b, calculating a second parameter by using a third formula according to the fitness value of the first individual and the fitness value of the individual in the second individual set; the third formula includes:

wherein CR is a second parameter,set S for the second individual^leastFitness value of the kth individual;

s4034b, for each dimension element of the individual in the second set of individuals, executing the steps of: generating a second random number, and judging whether the second random number is less than or equal to the second parameter: if yes, replacing the dimension element of the individual in the second individual set with the corresponding dimension element of the first individual; otherwise, the dimension element of the individual in the second individual set remains unchanged;

s4035b, randomly generating two index subscripts, wherein the two index subscripts include a first subscript and a second subscript, and symmetrically exchanging elements of the individual in the second individual set between the first subscript and the second subscript.

6. The method of claim 3, wherein the updating the first individual with a third update policy comprises:

s4031c, performing substitution variation on the first individual to obtain a first variant individual;

s4032c, performing crossover variation on the first individuals for M times to obtain M second variant individuals;

s4033c, performing element movement variation on the first individual for M times to obtain M third variant individuals;

s4034c, calculating fitness values of the variant individuals, and determining whether the highest fitness value is greater than the fitness value of the first individual:

if yes, replacing the first individual with the variant individual with the highest fitness value, and executing S4035 c;

otherwise, go to S4035 c;

s4035c, selecting the individuals with the highest fluorescence brightness from the updated first individual set and the updated second individual set, and performing cross processing on the individuals with the highest fluorescence brightness and the first individual to obtain the updated first individual.

7. The method of claim 1, wherein calculating the fitness of each individual in the current firefly population comprises:

s301, performing feasibility transformation on the solution corresponding to the individual to enable the values of all dimensions of the solution to be in the range of [1, M ], wherein the transformation process comprises the following steps: judging whether each element in the solution corresponding to the individual is in the range of the interval [1, M ], if so, keeping the element unchanged, otherwise, taking the value of the element as M;

s302, determining the workpieces processed by each machine in each process according to the solution corresponding to the individual, calculating the final finishing time for each machine in the last process to process the workpieces respectively, and taking the maximum value of the final finishing time for each machine in the last process to process the workpieces respectively as the time required by manufacturing and assembling the space engine;

s303, determining the number of the workpieces of which the finishing time is later than the delivery date on the corresponding machine of the last process according to the final finishing time of each workpiece on the corresponding machine of the last process and the delivery date of each workpiece, and recording the number as the number of the workpieces of which the processing task is not finished before the delivery date;

and S304, calculating the corresponding fitness value of the individual according to the time required by manufacturing and assembling the space engine and the number of the workpieces which do not finish the processing task before the delivery date.

8. The method of claim 7, wherein the fitness value corresponding to the individual is calculated using a fourth formula; the fourth formula includes:

wherein F is the fitness value corresponding to the individual, W₁And W₂Is a weight coefficient and W₁+W₂＝1，C_maxTime required for the manufacturing assembly of an aerospace engine, J_dFor collections of workpieces that do not complete processing tasks prior to delivery, | J_dI is the number of workpieces which have not completed a processing task before delivery date, d_fFor delivery of the f-th workpiece, P_fThe f-th workpiece corresponds to the final finish time on the machine in the last process.

9. An intelligent cooperative scheduling system for a space engine development process is characterized by comprising:

the parameter setting module is used for executing S100 and setting algorithm parameters; the algorithm parameters at least comprise the number of workpieces required in the manufacturing and assembling of the space engine, the number of required working procedures, the number of machines on each working procedure, the delivery date of each workpiece, the maximum iteration number and the individual number in the firefly population;

the population generation module is used for executing S200 and generating an initial firefly population according to the delivery date of each workpiece; each individual in the firefly population corresponds to a solution, and each solution comprises a machine for processing each workpiece in each process;

the optimal solution calculation module is used for executing S300, calculating the fitness of each individual in the current firefly population, taking the individual with the lowest fitness as a first individual, taking the solution corresponding to the first individual as the optimal solution of the iteration process, and updating the global optimal solution according to the optimal solution;

a number judging module, configured to execute S400, and judge whether the current iteration number reaches the maximum iteration number: if so, outputting the current global optimal solution; otherwise, updating each individual in the current firefly population according to a preset individual updating strategy to obtain an updated firefly population, and returning to the optimal solution calculation module to execute the step S300.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.