CN117494886A - Production scheduling method based on genetic algorithm - Google Patents
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Abstract
The invention and the technical field of production management, and discloses a production scheduling method based on a genetic algorithm, wherein the production scheduling method considers the conditions of the downtime (such as factory calendar, namely the time arrangement of working days and rest days, machine maintenance downtime) in the algorithm, combines the related parameter configuration, automatically schedules the production task of a workshop by using the genetic algorithm, provides scientific basis for the daily operation planning decision of a workshop planner, fully considers the influence of machine downtime on the scheduling implementation process, enables the scheduling result to be closer to the actual production time point, solves the problem that the existing algorithm can only solve the scheduling problem of small scale, has slower convergence to the optimal solution and has larger problem apart from the actual application.
Description
Technical Field
The invention relates to the technical field of production management, in particular to a production scheduling method based on a genetic algorithm.
Background
In recent years, the technology of high-speed internet, big data, cloud computing, artificial intelligence and the like is rapidly developed through the retrieval of the prior art, and a new industrial revolution is rising, for example, the Germany industry 4.0. The German industry 4.0 advocates that an intelligent factory is molded by means of an information physical system (Cyber Physical Systems), intelligent manufacturing is further achieved, a plurality of deployed sensors are connected with control equipment and a software system by means of communication technology, intelligent process control and optimization are carried out, vertical integration of production, transverse cooperation of suppliers and markets is finally achieved, manufacturing flexibility of enterprises is continuously improved, states, working conditions and results of all links of the whole production flow are summarized and restored layer by layer based on sensor detail data, the production flow is simulated in a virtual production mode, the enterprise is helped to optimize the production flow by means of integrated analysis, the methods are all based on multi-source data fusion, the traditional industrial automation technology, an ERP system, the Internet of things, a big data technology, a visual technology and other commercial technologies are integrated, the production environment is further optimized and innovated into a realistic action, the enterprises can better pursue the optimal balance between flexible production and quick response, order satisfaction rate and customer satisfaction degree are improved, under the condition that a series of large-scale customization is often achieved, due to the fact that a loop on a supply chain is lost, the whole manufacturing cost is delayed, the whole production is prolonged, the whole production is carried out, and the product is difficult to be carried out in a low-scale, and the product is not satisfactory, and the product is satisfied, and the product is difficult to be carried out in a product is frequently and has low-down.
In addition, the workshop production scheduling is to carry out operation planning on workshop production processes, and is a core for realizing intellectualization, automation and informatization in the production manufacturing industry. The workshop scheduling problem has high complexity, has strong similarity with other combination optimization problems, and a large number of workshop scheduling problems belong to NP-hard problems, and the complexity of the workshop scheduling problem is considered, if the workshop scheduling problem only depends on the experience of a scheduler, the scheduling efficiency is low and the effect is poor due to the fact that the manual mode is adopted for production scheduling, the production management level and the automation level of an enterprise are seriously influenced, so that the labor productivity and the machine utilization rate of the enterprise are influenced, the manpower and the material resources of the enterprise are wasted, and the competitiveness of the enterprise is influenced. The research and application of the effective workshop scheduling method and the optimization technology can improve the resource utilization rate and the production benefit of enterprises, so that the efficient workshop scheduling algorithm is the core and key for solving workshop scheduling problems, simulates inheritance (genetic inheritance), evolution (genetic mutation) and winner and worse elimination (a great number of excellent genes are inherited and copied and inferior genes are less inherited) of the genetics in nature, namely, the superior (strong in adaptability to environment) group seeds are bred generation by generation, and the gradual evolution variation forms better group seeds, and the inferior group seeds are degenerated generation by generation and are less bred, thereby realizing the natural winner and worse elimination.
However, the genetic algorithm has some problems in application, the genetic algorithm needs to be improved, the randomness and blindness of the genetic algorithm search are reduced, the searching capacity of the genetic algorithm in a local small space is enhanced, and the probability of searching the optimal solution is improved;
for this reason, we propose a production scheduling method based on genetic algorithm.
Disclosure of Invention
The invention mainly solves the technical problems existing in the prior art and provides a production scheduling method based on a genetic algorithm.
In order to achieve the above purpose, the invention adopts the following technical scheme that the production scheduling method based on the genetic algorithm comprises the following steps:
s1: obtaining production process data after data analysis, and automatically converting the production process data into a standard calculation format;
s2: taking a new machine as an external cooperation factor, acquiring the corresponding processing time t of the new machine, adding the external cooperation machine number and the fixed processing time t into data, converting the data into a standard example format, and then setting the data in a coding module;
s3: adding machine unavailable time, and setting downtime in a decoding module;
s4: converting the parameter form of the solution of the optimization problem into a representation form of a gene code string to form a chromosome;
s5: and (3) carrying out a genetic algorithm, inputting the coded population into a solving module of the genetic algorithm, obtaining an optimal solution through preliminary ranking Cheng Heyao bundles of adjustment, and carrying the optimal solution into a decoding module of the genetic algorithm to obtain a scheduling Gantt chart and a scheduling schedule.
As a further limitation of the above scheme, in the step S1, the regression model is used to analyze the historical data of the production process to obtain the numerical value of the scheduling parameter, and the numerical value of the scheduling parameter is automatically converted into the standard calculation format.
As a further limitation of the above, in the step S1, the values of the schedule parameters include the number of workpieces to be processed, the number of workpieces, the number of steps of the workpieces, the number of current processing machines, the processing feed rate, the processing start time, and the processing completion time
As a further limitation of the above scheme, in the step S2, the encoding module adopts a double-layer encoding mechanism, so that two kinds of information including the processing sequence of the workpiece and the allocation of the machine can be simultaneously described; the first part of the double-layer coding mechanism is a coding OS based on working procedures, the second part is a machine coding MS, and the two parts are included, wherein the first part is the MS for coding according to the processing sequence of the OS, the second part is the MS for coding according to the natural sequence of all working procedures, and a real number coding mode is used for both coding modes
As a further limitation of the above, in the step S3, the process sequence part is decoded into the active schedule of the machine selection part.
As a further limitation of the above solution, in the step S3, if the current machine time is not available, the unavailable time is t; if the current machine is machining the workpiece, the end time of the current machining of the workpiece is followed by t, and so on, so as to obtain a final result.
As a further limitation of the above scheme, in step S4, a specific problem is first encoded to form a chromosome, then fitness of the chromosome is evaluated according to an objective function, selection, crossover, and mutation are performed according to the evaluation result, and the process is repeated until a termination condition is satisfied.
As a further limitation of the above solution, the step S5 includes a preliminary rank Cheng Heyao bundle adjustment two part, wherein:
the preliminary schedule is: (1) Inputting scheduling parameters N, T, pc and Pm, reading working calendar information in a basic setting module, reading tasks and priority information, processing technology information, processing resource information and inventory information thereof in a data management module, coding the tasks, and randomly generating an initial population A with a scale of N;
(2) Calculating the moderate function of each chromosome in the initial population A, taking the shortest total processing time as an evaluation standard, and taking the result obtained by taking the reciprocal of the total processing time of each chromosome as the moderate value of the chromosome;
(3) Different selection probabilities Ps are given to the moderate value of each chromosome, the calculation formula is shown as follows, wherein i represents a certain chromosome, the moderate value is marked as fi, N chromosomes are selected from the initial population A by adopting a roulette mode, and a new population A' is generated;
(4) According to the input crossing probability Pc, parent chromosomes in the population A' are crossed pairwise, legal detection is required to be carried out on the crossed chromosomes, legal chromosomes are reserved, and illegal chromosomes are discarded;
(5) According to the input mutation probability Pm, carrying out gene segment rearrangement on the parent chromosome, and after generating a new chromosome, carrying out legality test;
(6) Repeating the process T times to obtain an optimal solution of the preliminary scheduling;
constraint adjustment is as follows:
when the bill insertion, delay or material supply changes, tasks are required to be correspondingly increased, task priority information, processing technology information, processing resource information and inventory information are required to be adjusted, and scheduling results conforming to the current production state are required to be rescheduled.
As a further limitation of the above, the computer program when executed by a processor implements the steps of the method of any of claims 1-8.
As a further limitation of the above solution, a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method according to any one of claims 1-8 when said program is executed.
Advantageous effects
The invention provides a production scheduling method based on a genetic algorithm. The beneficial effects are as follows:
(1) According to the production scheduling method based on the genetic algorithm, the downtime (such as a factory calendar, namely the schedule of workdays and rest days; machine maintenance downtime) is considered in the algorithm, the influence of machine downtime on the scheduling implementation process is fully considered, the scheduling result is closer to the actual production time point, the problem that the existing algorithm can only solve small-scale scheduling, and the problem that the optimal solution is converged is slow and is larger than the actual application is solved.
(2) According to the production scheduling method based on the genetic algorithm, the scheduling problem of the production workshops of aviation enterprises is solved by utilizing the mutation and population evolution characteristics in the genetic algorithm and gradually generating an optimal solution in a potential solution population through an advanced planning scheduling technology based on the genetic algorithm; meanwhile, the processing capacity is also considered in an algorithm by an external cooperation factor (under the control of organization, the external cooperation unit uses the own site, tool and other factors to produce and provide products and services according to the raw materials, drawings, inspection rules, acceptance criteria and the like provided by the organization), so that the method has great help to the improvement of the productivity of factories;
(3) According to the production scheduling method based on the genetic algorithm, a new production plan can be added under the existing scheduling result to obtain a new scheduling result, for example, under the condition of locking the current row Cheng Gongshan, new production requirements are added to obtain a real-time scheduling result; different scheduling plans are provided for different production demands, for example, the current optimal scheduling result is provided under the conditions that the demand targets are the shortest production time, the machine load is balanced, the goods delivery period is the target, and the like.
(4) The production scheduling method based on the genetic algorithm fully considers the conditions of various resource contents (such as equipment and equipment groups in a basic setting module, a working calendar and the like), orders, processing technology, processing resources, inventory and the like in a data management module, and utilizes the genetic algorithm to automatically schedule production tasks of workshops by combining with related parameter configuration, so that scientific basis is provided for daily operation plan decision of workshop planners. The genetic algorithm is utilized to keep the searched information through the interaction among groups, which is incomparable with the optimization method based on a single search process, and in addition, three operators of selection, intersection and mutation used by the genetic algorithm are all random operations, and are not specified determination rules.
Detailed Description
A production scheduling method based on genetic algorithm, comprising the steps of:
s1: obtaining production process data after data analysis, and automatically converting the production process data into a standard calculation format;
s2: taking a new machine as an external cooperation factor, acquiring the corresponding processing time t of the new machine, adding the external cooperation machine number and the fixed processing time t into data, converting the data into a standard example format, and then setting the data in a coding module;
s3: adding machine unavailable time, and setting downtime in a decoding module;
s4: converting the parameter form of the solution of the optimization problem into a representation form of a gene code string to form a chromosome;
s5: and (3) carrying out a genetic algorithm, inputting the coded population into a solving module of the genetic algorithm, obtaining an optimal solution through preliminary ranking Cheng Heyao bundles of adjustment, and carrying the optimal solution into a decoding module of the genetic algorithm to obtain a scheduling Gantt chart and a scheduling schedule.
In the step S1, the regression model is adopted to analyze the historical data of the production process, obtain the numerical value of the scheduling parameter, and automatically convert the numerical value of the scheduling parameter into the standard calculation format.
In the step S1, the values of the schedule parameters include the number of workpieces to be processed, the number of workpieces, the number of steps of the workpieces, the number of current processing machines, the processing feed rate, the processing start time, and the processing completion time
In the step S2, the encoding module adopts a double-layer encoding mechanism, so that two kinds of information of the processing sequence of the workpiece and the distribution of the machine can be simultaneously described; the first part of the double-layer coding mechanism is a coding OS based on working procedures, the second part is a machine coding MS, and the two parts are included, wherein the first part is the MS for coding according to the processing sequence of the OS, the second part is the MS for coding according to the natural sequence of all working procedures, and a real number coding mode is used for both coding modes
In step S3, the process sequence part is decoded into an active schedule of the machine selection part.
In the step S3, if the current machine time is not available, the unavailable time is t; if the current machine is machining the workpiece, the end time of the current machining of the workpiece is followed by t, and so on, so as to obtain a final result.
In step S4, a specific problem is encoded to form a chromosome, then fitness of the chromosome is evaluated according to an objective function, and selection, crossover and mutation are performed according to the evaluation result, and the process is repeated until a termination condition is satisfied.
The step S5 comprises a preliminary row Cheng Heyao beam adjustment two parts, wherein:
the preliminary schedule is: (1) Inputting scheduling parameters N, T, pc and Pm, reading working calendar information in a basic setting module, reading tasks and priority information, processing technology information, processing resource information and inventory information thereof in a data management module, coding the tasks, and randomly generating an initial population A with a scale of N;
(2) Calculating the moderate function of each chromosome in the initial population A, taking the shortest total processing time as an evaluation standard, and taking the result obtained by taking the reciprocal of the total processing time of each chromosome as the moderate value of the chromosome;
(3) Different selection probabilities Ps are given to the moderate value of each chromosome, the calculation formula is shown as follows, wherein i represents a certain chromosome, the moderate value is marked as fi, N chromosomes are selected from the initial population A by adopting a roulette mode, and a new population A' is generated;
(4) According to the input crossing probability Pc, parent chromosomes in the population A' are crossed pairwise, legal detection is required to be carried out on the crossed chromosomes, legal chromosomes are reserved, and illegal chromosomes are discarded;
(5) According to the input mutation probability Pm, carrying out gene segment rearrangement on the parent chromosome, and after generating a new chromosome, carrying out legality test;
(6) Repeating the process T times to obtain an optimal solution of the preliminary scheduling;
constraint adjustment is as follows:
when the bill insertion, delay or material supply changes, tasks are required to be correspondingly increased, task priority information, processing technology information, processing resource information and inventory information are required to be adjusted, and scheduling results conforming to the current production state are required to be rescheduled.
A computer-readable storage medium having stored thereon a computer program, characterized by: a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program implementing the steps of the method of any one of claims 1-8 when executed by the processor, characterized by: the processor, when executing the program, implements the steps of the method according to any one of claims 1-8.
The working principle of the invention is as follows:
the foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (10)
1. A production scheduling method based on a genetic algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1: obtaining production process data after data analysis, and automatically converting the production process data into a standard calculation format;
s2: taking a new machine as an external cooperation factor, acquiring the corresponding processing time t of the new machine, adding the external cooperation machine number and the fixed processing time t into data, converting the data into a standard example format, and then setting the data in a coding module;
s3: adding machine unavailable time, and setting downtime in a decoding module;
s4: converting the parameter form of the solution of the optimization problem into a representation form of a gene code string to form a chromosome;
s5: and (3) carrying out a genetic algorithm, inputting the coded population into a solving module of the genetic algorithm, obtaining an optimal solution through preliminary ranking Cheng Heyao bundles of adjustment, and carrying the optimal solution into a decoding module of the genetic algorithm to obtain a scheduling Gantt chart and a scheduling schedule.
2. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in the step S1, the regression model is adopted to analyze the historical data of the production process, obtain the numerical value of the scheduling parameter, and automatically convert the numerical value of the scheduling parameter into the standard calculation format.
3. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in the step S1, the numerical values of the scheduling parameters include the number of workpieces to be processed, the number of workpieces, the number of processes of the workpieces, the number of current processing machines, the processing feed rate, the processing start time, and the processing completion time.
4. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in the step S2, the encoding module adopts a double-layer encoding mechanism, so that two kinds of information of the processing sequence of the workpiece and the distribution of the machine can be simultaneously described; the first part of the double-layer coding mechanism is a coding OS based on working procedures, the second part is a machine coding MS, and the two coding modes are included, wherein the first part is the MS for coding according to the processing sequence of the OS, the second part is the MS for coding according to the natural sequence of all working procedures, and real coding modes are used for both coding modes.
5. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in step S3, the process sequence part is decoded into an active schedule of the machine selection part.
6. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in the step S3, if the current machine time is not available, the unavailable time is t; if the current machine is machining the workpiece, the end time of the current machining of the workpiece is followed by t, and so on, so as to obtain a final result.
7. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: in step S4, a specific problem is encoded to form a chromosome, then fitness of the chromosome is evaluated according to an objective function, and selection, crossover and mutation are performed according to the evaluation result, and the process is repeated until a termination condition is satisfied.
8. The production scheduling method based on genetic algorithm as claimed in claim 1, wherein: the step S5 comprises a preliminary row Cheng Heyao beam adjustment two parts, wherein:
the preliminary schedule is: (1) Inputting scheduling parameters N, T, pc and Pm, reading working calendar information in a basic setting module, reading tasks and priority information, processing technology information, processing resource information and inventory information thereof in a data management module, coding the tasks, and randomly generating an initial population A with a scale of N;
(2) Calculating the moderate function of each chromosome in the initial population A, taking the shortest total processing time as an evaluation standard, and taking the result obtained by taking the reciprocal of the total processing time of each chromosome as the moderate value of the chromosome;
(3) Different selection probabilities Ps are given to the moderate value of each chromosome, the calculation formula is shown as follows, wherein i represents a certain chromosome, the moderate value is marked as fi, N chromosomes are selected from the initial population A by adopting a roulette mode, and a new population A' is generated;
(4) According to the input crossing probability Pc, parent chromosomes in the population A' are crossed pairwise, legal detection is required to be carried out on the crossed chromosomes, legal chromosomes are reserved, and illegal chromosomes are discarded;
(5) According to the input mutation probability Pm, carrying out gene segment rearrangement on the parent chromosome, and after generating a new chromosome, carrying out legality test;
(6) Repeating the process T times to obtain an optimal solution of the preliminary scheduling;
constraint adjustment is as follows:
when the bill insertion, delay or material supply changes, tasks are required to be correspondingly increased, task priority information, processing technology information, processing resource information and inventory information are required to be adjusted, and scheduling results conforming to the current production state are required to be rescheduled.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the steps of the method of any one of claims 1 to 8 when executed by a processor.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the program, implements the steps of the method according to any one of claims 1-8.
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