CN114066312A - Production scheduling method, system, equipment and storage medium based on discrete manufacturing - Google Patents

Production scheduling method, system, equipment and storage medium based on discrete manufacturing Download PDF

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CN114066312A
CN114066312A CN202111434127.0A CN202111434127A CN114066312A CN 114066312 A CN114066312 A CN 114066312A CN 202111434127 A CN202111434127 A CN 202111434127A CN 114066312 A CN114066312 A CN 114066312A
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刘冲
张桂林
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Chongqing Yunfeng Technology Co ltd
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Abstract

The invention provides a production scheduling method, a production scheduling system, production scheduling equipment and a storage medium based on discrete manufacturing, wherein the method comprises the following steps: constructing a production scheduling model based on the objective that the weighted sum of time and cost is minimum; solving the optimal scheduling solution of the scheduling model by adopting an improved genetic algorithm to obtain a pre-scheduling plan; and auditing the pre-dispatching plan, acquiring a dispatching plan after the auditing is passed, and dispatching tasks according to the dispatching plan. The invention can construct a multi-objective optimization model which takes production batches as units and is based on time and cost constraints aiming at the characteristics of the discrete manufacturing industry, and solves the optimal solution of production scheduling, thereby enabling the discrete manufacturing enterprises to carry out intelligent production scheduling management on the production field, improving the production efficiency and reducing the overall production cost.

Description

Production scheduling method, system, equipment and storage medium based on discrete manufacturing
Technical Field
The invention relates to the technical field of production scheduling, in particular to a production scheduling method, a production scheduling system, production scheduling equipment and a storage medium based on discrete manufacturing.
Background
Manufacturing industry has been the subject of national economy, and discrete manufacturing industry is more called "national economy pillar". With the increasingly competitive market, communication technology, information technology and the like are highly regarded and widely applied by discrete manufacturing enterprises, and more discrete manufacturing enterprises tend to lean on production and just-in-time production modes. However, the discrete manufacturing industry has the characteristics of highly customized orders, highly complex technical process and supply chain, highly discrete production and manufacturing, long production and manufacturing period and the like, so that the field production scheduling and dispatching are extremely complex.
In the prior art, most discrete manufacturing enterprises still stay in a manual scheduling management mode, even if a few discrete manufacturing enterprises have a scheduling system, the essential problem cannot be solved, and only a paper planning scheduling list is converted into an electronic-version planning scheduling list, so that the resource utilization efficiency cannot be improved from the perspective of planning scheduling, and the production efficiency is low and the market competitiveness is lacked.
Disclosure of Invention
In view of the above, it is necessary to provide a production scheduling method, system, device and storage medium based on discrete manufacturing.
A production scheduling method based on discrete manufacturing comprises the following steps: constructing a production scheduling model based on the objective that the weighted sum of time and cost is minimum; solving the optimal scheduling solution of the scheduling model by adopting an improved genetic algorithm to obtain a pre-scheduling plan; and auditing the pre-dispatching plan, acquiring a dispatching plan after the auditing is passed, and dispatching tasks according to the dispatching plan.
In one embodiment, before constructing the production scheduling model based on the goal that the weighted sum of the time and the cost is minimum, the method further includes: the following constraints were constructed: the same batch of processing technology, processing time and processing cost are the same, and the preparation time of feeding and the like of each workpiece is neglected; the field devices are all intact and have the function of processing all tasks, but the processing time and cost between the devices are different; one working procedure of one workpiece can only be processed on one device at the same time; one process in which only one workpiece can be processed by one apparatus at a time.
In one embodiment, the constructing the production scheduling model based on the objective with the minimum weighted sum of time and cost specifically includes: based on the constraint conditions, all parts to be processed in the workshop are formed into l workpiece sets Ji＝{j1,j2,j3…jliIn which liRepresenting a number of workpieces of the workpiece set; workpiece set JiFrom k to kiThe process task is composed of a set
Figure BDA0003381103560000026
Wherein, OijA j-th process task representing an i-th workpiece; assuming that a production line has M devices, the device set is M ═ M1，m2，m3…mn}，tijnThe time consumed for the j process task of the ith workpiece to be processed on the equipment n is shown; cijnAnd representing the cost consumed by the j process task of the ith workpiece in the processing of the equipment n, wherein the production scheduling model is as follows:
Figure BDA0003381103560000021
Figure BDA0003381103560000022
in order to ensure that each process task can be allocated only once and only to a certain device, the following steps are provided:
Figure BDA0003381103560000023
in order to ensure that the processing sequence of tasks is in accordance with the process requirements, the following steps are provided:
Figure BDA0003381103560000024
in order to ensure the consistency of time and cost dimensions in the objective function, the method comprises
Figure BDA0003381103560000025
Wherein alpha is1In units of minutes, α2The unit of (a) is element; wherein i is more than or equal to 0 and less than or equal to l, and k is more than or equal to 0 and less than or equal to ki，0≤n≤m。
In one embodiment, the solving the optimal scheduling solution of the scheduling model by using an improved genetic algorithm to obtain a pre-scheduling plan specifically includes: coding based on equipment, and expressing the sequence of the process tasks and the start of the tasks distributed to the equipment by using chromosomes; taking the minimum value in the production scheduling model as a fitness function, namely g (x) minf (x); according to the fitness function, selecting operation is executed in a proportional selection operator mode, and a plurality of first target individuals are obtained; performing cross operation on the gene positions representing the equipment numbers in the plurality of first target individuals, and randomly generating the gene positions representing the task sequence in the plurality of first target individuals to obtain a second target individual; and performing independent variation on the second target individual according to the equipment number and the task sequence respectively to obtain an optimal solution as a pre-dispatching plan.
In one embodiment, the performing, according to the fitness function, a selection operation in a manner of a proportional selection operator to obtain a plurality of first target individuals specifically includes: calculating the fitness of each individual in the group according to the fitness function, and executing selection operation in a proportional selection operator mode, wherein the size of the group is m, the fitness of the individual is g (i), and the probability P of the individual being selected isiComprises the following steps:
Figure BDA0003381103560000031
as can be seen from the equation (5), the higher the fitness, the higher the probability that the individual is selected, and conversely, the lower the fitness, the lower the probability that the individual is selected; and (5) carrying out repeated iteration according to the formula (5), screening out individuals with the selected probability higher than the preset probability, and obtaining a plurality of first target individuals.
In one embodiment, the performing a crossover operation on the loci representing the device numbers in the first target individuals, and randomly generating the loci representing the task order in the first target individuals to obtain the second target individuals specifically includes: acquiring fitness corresponding to the plurality of first target individuals according to the equipment number, and calculating the total fitness of the plurality of first target individuals; taking the ratio of the individual preset fitness to the total fitness as probability, selecting an individual with the fitness higher than the individual preset fitness as a male parent and a female parent to carry out cross operation, and obtaining a new individual; and randomly generating genes in the new individual according to the task sequence to obtain a second target individual.
In one embodiment, the performing individual variation on the second target individual according to the device number and the task order to obtain an optimal solution as the pre-scheduling plan specifically includes: carrying out mutation on the second target individual according to the equipment number, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until the group meets a first preset condition; carrying out mutation on the second target individual according to the task sequence, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until the group meets a second preset condition; and comparing the individuals respectively obtained by the variation operation iteration, and screening out an optimal solution as a pre-dispatching plan.
A discrete manufacturing-based production scheduling system, comprising: the production scheduling model building module is used for building a production scheduling model based on the target with the minimum weighted sum of time and cost; the scheduling optimal solution solving module is used for solving the scheduling optimal solution of the scheduling model by adopting a genetic algorithm to obtain a pre-scheduling plan; and the task dispatching module is used for auditing the pre-dispatching plan, acquiring the dispatching plan after the auditing is passed, and dispatching the tasks according to the dispatching plan.
An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the discrete manufacturing-based production scheduling method described in the various embodiments above when executing the program.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the discrete manufacturing-based production scheduling method described in the various embodiments above.
Compared with the prior art, the invention has the advantages and beneficial effects that: the invention can construct a multi-objective optimization model which takes production batches as units and is based on time and cost constraints aiming at the characteristics of the discrete manufacturing industry, and solves the optimal solution of production scheduling, thereby enabling the discrete manufacturing enterprises to carry out intelligent production scheduling management on the production field, improving the production efficiency and reducing the overall production cost.
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FIG. 1 is a schematic flow chart diagram illustrating a discrete manufacturing-based production scheduling method in one embodiment;
FIG. 2 is a schematic diagram of a discrete manufacturing-based production scheduling system in one embodiment;
fig. 3 is a schematic diagram of the internal structure of the apparatus in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings by way of specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method and the device are suitable for the situation that discrete manufacturing enterprises cannot improve the resource utilization efficiency from the perspective of planned scheduling, so that the production efficiency is low and the market competitiveness is lacked. According to the method, a production scheduling model is constructed through set constraint conditions, an equipment-based coding mode is adopted, each chromosome represents a sequence of a process task and a task start distributed to equipment, a minimum value in the production scheduling model is used as a fitness function, the fitness of each individual in a group is calculated according to the fitness function, a selection operation is executed in a proportional selection operator mode, selected individuals with the probability higher than a preset probability are screened out, and a plurality of first target individuals are obtained; acquiring fitness corresponding to a plurality of first target individuals according to the equipment number, calculating the total fitness of the plurality of first target individuals, selecting an individual with fitness higher than the individual fitness as a male parent and a female parent to perform cross operation by taking the ratio of the individual fitness to the total fitness as probability, acquiring a new individual, randomly generating genes in the new individual according to the task sequence, and acquiring a second target individual; respectively carrying out individual variation on a second target individual according to the equipment number and the task sequence to obtain corresponding individuals, and screening out the optimal solution in the individuals to be used as a pre-dispatching plan; the pre-dispatching plan is audited, the dispatching plan is obtained after the auditing is passed, tasks are dispatched according to the dispatching plan, characteristics and enterprise requirements of a multi-variety and small-batch production model of the discrete manufacturing industry are analyzed, a multi-objective optimization model which takes a production batch as a unit and is based on time and cost constraints is constructed, and an optimal solution of production dispatching is solved, so that the discrete manufacturing enterprise can carry out intelligent production dispatching management of a production field, production efficiency is improved, and overall production cost is reduced.
In one embodiment, as shown in fig. 1, there is provided a production scheduling method based on discrete manufacturing, comprising the steps of:
step S101, a production scheduling model is constructed based on the objective that the weighted sum of time and cost is minimum.
Specifically, according to actual requirements of a production field of a discrete manufacturing enterprise, production data preparation and processing plan scheduling are carried out according to a production plan, and when the processing plan scheduling is carried out, a production scheduling model is constructed on the basis of a target with the minimum weighted sum of time and cost. When the production data is prepared, whether the production data is sufficient or not is detected, and if the production data is insufficient, a purchase command is issued. And outputting a scheduling result according to the production scheduling model to obtain a pre-scheduling production plan.
And S102, solving the optimal scheduling solution of the scheduling model by adopting an improved genetic algorithm to obtain a pre-scheduling plan.
Specifically, when the scheduling model is solved, an improved genetic algorithm is adopted to solve an optimal scheduling solution, a scheduling task is coded, a population is initialized, the individual fitness in the population is evaluated, the steps of selection, crossing and variation are sequentially performed, and the individual fitness, selection, crossing and variation in the population are repeatedly and iteratively evaluated, so that the optimal scheduling solution is obtained and is used as a pre-scheduling plan.
And S103, auditing the pre-dispatching plan, acquiring the dispatching plan after the auditing is passed, and dispatching tasks according to the dispatching plan.
Specifically, the acquired pre-scheduling plan is checked, after the check is passed, the pre-scheduling plan is used as a scheduling plan, and a task dispatcher is generated according to the scheduling plan, so that the task scheduling method with the smallest time and cost weighting sum can be implemented.
In this embodiment, a production scheduling model is constructed based on the objective of minimum weighted sum of time and cost, a genetic algorithm is adopted to solve the scheduling optimal solution of the scheduling model, a pre-scheduling plan is obtained, the pre-scheduling plan is audited, after the audit is passed, the scheduling plan is obtained, and task dispatching is performed according to the scheduling plan.
Before step S101, the method further includes: the following constraints were constructed: the same batch of processing technology, processing time and processing cost are the same, and the preparation time of feeding and the like of each workpiece is neglected; the field devices are all intact and have the function of processing all tasks, but the processing time and cost between the devices are different; one working procedure of one workpiece can only be processed on one device at the same time; one process in which only one workpiece can be processed by one apparatus at a time.
Specifically, in order to facilitate the solution of the workshop scheduling problem, a normative assumption is made, and the following constraint conditions are constructed: the same batch of processing technology, processing time and processing cost are the same, and the preparation time of feeding and the like of each workpiece is neglected; the field devices are all intact and have the function of processing all tasks, but the processing time and cost between the devices are different; one working procedure of one workpiece can only be processed on one device at the same time; one process in which only one workpiece can be processed by one apparatus at a time.
Wherein, step S101 specifically includes: based on constraint conditions, all parts to be processed in a workshop are formed into a workpiece set Ji＝{j1,j2,j3…jliIn which liRepresenting a number of workpieces of the workpiece set; workpiece set JiFrom k to kiThe process task is composed of a set
Figure BDA0003381103560000066
Wherein, OijA j-th process task representing an i-th workpiece; assuming that a production line has M devices, the device set is M ═ M1，m2，m3…mn}，tijnThe time consumed for the j process task of the ith workpiece to be processed on the equipment n is shown; cijnAnd representing the cost consumed by the j process task of the ith workpiece in the processing of the equipment n, wherein the production scheduling model is as follows:
Figure BDA0003381103560000061
Figure BDA0003381103560000062
in order to ensure that each process task can be allocated only once and only to a certain device, the following steps are provided:
Figure BDA0003381103560000063
in order to ensure that the processing sequence of tasks is in accordance with the process requirements, the following steps are provided:
Figure BDA0003381103560000064
in order to ensure the consistency of time and cost dimensions in the objective function, the method comprises
Figure BDA0003381103560000065
Wherein alpha is1In units of minutes, α2The unit of (a) is element; wherein i is more than or equal to 0 and less than or equal to l, and j is more than or equal to 0 and less than or equal to ki，0≤n≤m。
Specifically, a production scheduling model is constructed according to constraint conditions and scheduling targets, each process task is required to be allocated only once in the construction process, only one device is allocated, and in addition, the processing sequence of the tasks is required to be performed according to process requirements.
Wherein, step S102 specifically includes: coding based on equipment, and expressing the sequence of the process tasks and the start of the tasks distributed to the equipment by using chromosomes; taking the minimum value in the production scheduling model as a fitness function, namely g (x) minf (x); according to the fitness function, selecting operation is executed in a proportional selection operator mode, and a plurality of first target individuals are obtained; performing cross operation on the gene positions representing the equipment numbers in the first target individuals, and randomly generating the gene positions representing the task sequence in the first target individuals to obtain second target individuals; and respectively carrying out independent variation on the second target individual according to the equipment number and the task sequence to obtain an optimal solution as a pre-dispatching plan.
Specifically, an equipment-based coding mode is adopted, each chromosome represents a sequence of a process task and a task start distributed to equipment, a population is initialized, and all individuals of a first generation population are randomly generated according to the characteristics of each population; and taking the minimum value in the production scheduling model as a fitness function, calculating the fitness of each individual according to the fitness function, executing selection operation in a proportional selection operator mode, selecting a plurality of first target individuals, and performing intersection and mutation operation. For example, the individual with the highest fitness is selected, the individual is copied to a new population, the copy number accounts for 1/4 of the first generation population, then the individuals of 3/4 in the new population are selected in a roulette mode, the selection probability is randomly generated, and the individuals with the selected probability greater than or equal to the random probability are added to the new population. Performing cross operation on gene positions representing equipment numbers in a plurality of first target individuals, pairing individuals in a group pairwise, exchanging partial chromosome genes, producing new individuals, and completing the cross operation; the loci representing the order of the tasks are then randomly generated. For example, two individuals are randomly selected, and the gene positions are exchanged in a certain probability interval to complete the cross operation.
According to the fitness function, selecting operation is executed in a mode of a proportion selection operator to obtain a first target individual, and the method specifically comprises the following steps: calculating the fitness of each individual in the group according to the fitness function, and executing selection operation in a proportional selection operator mode, wherein the size of the group is m, the fitness of the individual is g (i), and the probability P of the individual being selected isiComprises the following steps:
Figure BDA0003381103560000071
as can be seen from the equation (5), the higher the fitness, the higher the probability that the individual is selected, and conversely, the lower the fitness, the lower the probability that the individual is selected; and (5) carrying out repeated iteration according to the formula (5), screening out individuals with the selected probability higher than the preset probability, and obtaining a plurality of first target individuals.
Specifically, the fitness function is a standard for evaluating the quality of the individual, and the higher the fitness is, the closer the individual is to the optimal solution, and the lower the fitness is, the farther the individual is from the optimal solution. The individual is selected by adopting a roulette method, the fitness of all individuals in a group is solved according to a fitness function, the individual is reselected by adopting the roulette method, repeated iteration is carried out according to the formula, the iteration can be controlled to stop by adopting the preset number of selected individuals or the preset number of iterations, the selected individual with the probability higher than the preset probability is screened out, and a plurality of first target individuals are obtained.
The method specifically includes the following steps of performing cross operation on gene sites representing equipment numbers in a plurality of first target individuals, randomly generating gene sites representing task sequences in the plurality of first target individuals, and acquiring a second target individual: acquiring fitness corresponding to a plurality of first target individuals according to the equipment number, calculating the total fitness of the plurality of first target individuals, taking the ratio of the preset fitness of the individuals to the total fitness as probability, and selecting the individuals with the fitness higher than the preset fitness as male parents and female parents to perform cross operation to acquire new individuals; and randomly generating genes in the new individual according to the task sequence to obtain a second target individual.
Specifically, the corresponding fitness of a plurality of first target individuals is obtained according to the equipment number, the total fitness of the plurality of first target individuals is calculated and obtained, the ratio of the preset fitness of the individuals to the total fitness is used as the probability, the individuals with the fitness higher than the preset fitness are selected as the male parent and the female parent, cross operation is carried out, corresponding filial generations, namely new individuals, are obtained, genes in the new individuals are randomly generated according to the task sequence, and a second target individual is obtained. For example, screening out individuals with fitness higher than preset fitness from the first target individuals, pairing the individuals meeting the requirement in pairs, crossing the two successfully-paired individuals as the male parent and the female parent to randomly generate two different gene sites, wherein the filial generation 1 inherits the gene segments between the female parent and the male parent, the rest of the genes inherits the genes which are not repeated in the male parent in sequence, the filial generation 2 inherits the gene segments between the male parent and the female parent in sequence, and the rest of the genes inherits the genes which are not repeated in the female parent in sequence.
The method comprises the following steps of performing independent variation on a second target individual according to an equipment number and a task sequence to obtain an optimal solution as a pre-dispatching plan, and specifically comprises the following steps: carrying out mutation on a second target individual according to the equipment number, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until a group meets a first preset condition; carrying out mutation on a second target individual according to the task sequence, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until the group meets a second preset condition; and comparing the individuals respectively obtained by the variation operation iteration, and screening out an optimal solution as a pre-dispatching plan.
Specifically, when the adaptive value of the offspring generated by the crossover operation is not evolved any more and does not reach the optimum, it means premature convergence of the algorithm, and at this time, a certain degree of overcoming can be performed through the mutation operation, the mutation operation firstly needs to randomly select an individual in the second target population, and randomly changes the value of a certain character in the gene string with a certain probability, for example, randomly exchanges 25% to 50% of the genes of the mutated individual, thereby avoiding permanent loss of certain information caused by the duplication and crossover operators, and ensuring the effectiveness of the genetic algorithm.
And in the variation process, performing variation respectively according to the equipment number and the task sequence. When the second target individual is changed according to the equipment number, in the process of changing, genes corresponding to a plurality of elements of the changed individual belonging to the same scheduling set are randomly exchanged, and change operation is iteratively performed, so that new individuals are generated until a group meets a first preset condition, for example, the number of the new individuals reaches a preset number or the number of iterations reaches the number of iterations, and corresponding setting can be performed according to enterprise requirements. When the second target individual is changed according to the task sequence, in the process of changing, genes corresponding to a plurality of elements belonging to the same scheduling set in the changed individual are changed randomly, and the change operation is performed iteratively to generate new individuals until the group meets a second preset condition. And comparing the two individuals obtained by the variation operation respectively in an iteration mode, and screening out an optimal solution to be used as a pre-dispatching plan.
In one embodiment, for example, 5 workpiece sets are reasonably allocated to 5 devices for processing, wherein each workpiece set comprises 3 process tasks, and table 1 and table 2 show the time and cost consumed by the process tasks on different devices, respectively.
TABLE 1 time consumption for different equipment to process different tasks
Figure BDA0003381103560000091
TABLE 2 cost consumption of different equipment for different tasks
Figure BDA0003381103560000101
The actual production parameters are set as follows: alpha is alpha1＝1，α2The initial population is 20, the probability of crossover and mutation is 0.7 and 0.08 respectively, and the number of iterations is 500. Then, the simulation is performed to obtain that the time and cost consumption is the minimum, about 149.1, when the iteration is performed for about 132 times, and finally, the optimal solution of the task scheduling of the field process is obtained, as shown in table 3, task allocation is performed on each device according to table 3, and worker dispatching is performed correspondingly.
TABLE 3 task assignment sequence
Figure BDA0003381103560000102
As shown in fig. 2, there is provided a discrete manufacturing based production scheduling system 20, comprising: the system comprises a production scheduling model building module 21, a scheduling optimal solution solving module 22 and a task dispatching module 23, wherein:
a production scheduling model construction module 21 configured to construct a production scheduling model based on a target for which a weighted sum of time and cost is minimum;
the scheduling optimal solution solving module 22 is used for solving the scheduling optimal solution of the scheduling model by adopting a genetic algorithm to obtain a pre-scheduling plan;
and the task dispatching module 23 is configured to audit the pre-scheduling plan, obtain the scheduling plan after the audit is passed, and dispatch the task according to the scheduling plan.
In one embodiment, the scheduling optimal solution solving module 22 is specifically configured to: coding based on equipment, and expressing the sequence of the process tasks and the start of the tasks distributed to the equipment by using chromosomes; taking the minimum value in the target production scheduling model as a fitness function, namely g (x) minf (x); according to the fitness function, selecting operation is executed in a proportional selection operator mode, and a plurality of first target individuals are obtained; performing cross operation on the gene positions representing the equipment numbers in the first target individuals, and randomly generating the gene positions representing the task sequence in the first target individuals to obtain second target individuals; and respectively carrying out independent variation on the second target individual according to the equipment number and the task sequence to obtain an optimal solution as a pre-dispatching plan.
In one embodiment, the system further comprises: the selection module, the crossing module and the variation model are respectively used for executing selection operation, crossing operation and variation operation.
In one embodiment, a device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 3. The device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the device is configured to provide computing and control capabilities. The memory of the device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the device is used for storing configuration templates and also can be used for storing target webpage data. The network interface of the device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a discrete manufacturing based production scheduling method.
Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the devices to which the present application may be applied, and that a particular device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a storage medium may also be provided, the storage medium storing a computer program comprising program instructions which, when executed by a computer, may be part of the above-mentioned discrete manufacturing based production scheduling system, cause the computer to perform the method according to the preceding embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disks, optical disks) and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1. A production scheduling method based on discrete manufacturing is characterized by comprising the following steps:
constructing a production scheduling model based on the objective that the weighted sum of time and cost is minimum;
solving the optimal scheduling solution of the scheduling model by adopting an improved genetic algorithm to obtain a pre-scheduling plan;
and auditing the pre-dispatching plan, acquiring a dispatching plan after the auditing is passed, and dispatching tasks according to the dispatching plan.
2. The discrete manufacturing-based production scheduling method of claim 1, wherein before constructing the production scheduling model based on the objective of minimizing the weighted sum of time and cost, further comprising:
the following constraints were constructed: the same batch of processing technology, processing time and processing cost are the same, and the preparation time of feeding and the like of each workpiece is neglected; the field devices are all intact and have the function of processing all tasks, but the processing time and cost between the devices are different; one working procedure of one workpiece can only be processed on one device at the same time; one process in which only one workpiece can be processed by one apparatus at a time.
3. The discrete manufacturing-based production scheduling method according to claim 2, wherein the building of the production scheduling model based on the objective with the minimum weighted sum of time and cost specifically comprises:
based on the constraint conditions, all parts to be processed in the workshop are formed into l workpiece sets Ji＝{j1,j2,j3…jliIn which liRepresenting a number of workpieces of the workpiece set;
workpiece set JiFrom k to kiThe process task is composed of a set
Figure FDA0003381103550000011
Wherein, OijA j-th process task representing an i-th workpiece;
assuming that a production line has M devices, the device set is M ═ M1，m2，m3…mn}，tijnIndicates that the j process task of the i-th workpiece is consumed for processing on the equipment nA (c) is added; cijnAnd representing the cost consumed by the j process task of the ith workpiece in the processing of the equipment n, wherein the production scheduling model is as follows:
Figure FDA0003381103550000012
Figure FDA0003381103550000013
in order to ensure that each process task can be allocated only once and only to a certain device, the following steps are provided:
Figure FDA0003381103550000014
in order to ensure that the processing sequence of tasks is in accordance with the process requirements, the following steps are provided:
Figure FDA0003381103550000015
in order to ensure the consistency of time and cost dimensions in the objective function, the method comprises
Figure FDA0003381103550000021
Wherein alpha is1In units of minutes, α2The unit of (a) is element;
wherein i is more than or equal to 0 and less than or equal to l, and j is more than or equal to 0 and less than or equal to ki，0≤n≤m。
4. The discrete manufacturing-based production scheduling method according to claim 3, wherein the solving of the scheduling optimal solution of the scheduling model by using the improved genetic algorithm to obtain the pre-scheduling plan specifically comprises:
coding based on equipment, and expressing the sequence of the process tasks and the start of the tasks distributed to the equipment by using chromosomes;
taking the minimum value in the production scheduling model as a fitness function, namely g (x) minf (x);
according to the fitness function, selecting operation is executed in a proportional selection operator mode, and a plurality of first target individuals are obtained;
performing cross operation on the gene positions representing the equipment numbers in the plurality of first target individuals, and randomly generating the gene positions representing the task sequence in the plurality of first target individuals to obtain a second target individual;
and performing independent variation on the second target individual according to the equipment number and the task sequence respectively to obtain an optimal solution as a pre-dispatching plan.
5. The discrete manufacturing-based production scheduling method according to claim 4, wherein the performing, according to the fitness function, a selection operation by using a proportional selection operator to obtain a plurality of first target individuals specifically comprises:
calculating the fitness of each individual in the group according to the fitness function, and executing selection operation in a proportional selection operator mode, wherein the size of the group is m, the fitness of the individual is g (i), and the probability P of the individual being selected isiComprises the following steps:
Figure FDA0003381103550000022
as can be seen from the equation (5), the higher the fitness, the higher the probability that the individual is selected, and conversely, the lower the fitness, the lower the probability that the individual is selected;
and (5) carrying out repeated iteration according to the formula (5), screening out individuals with the selected probability higher than the preset probability, and obtaining a plurality of first target individuals.
6. The discrete manufacturing-based production scheduling method according to claim 4, wherein the performing a crossover operation on the loci representing the device numbers in the first target individuals and performing a random generation on the loci representing the task sequences in the first target individuals to obtain the second target individuals specifically comprises:
acquiring fitness corresponding to the plurality of first target individuals according to the equipment number, and calculating the total fitness of the plurality of first target individuals;
taking the ratio of the individual preset fitness to the total fitness as probability, selecting an individual with the fitness higher than the individual preset fitness as a male parent and a female parent to carry out cross operation, and obtaining a new individual;
and randomly generating genes in the new individual according to the task sequence to obtain a second target individual.
7. The discrete manufacturing-based production scheduling method according to claim 4, wherein the individually varying the second target individual according to the device number and the task order to obtain an optimal solution as a pre-scheduling plan specifically comprises:
carrying out mutation on the second target individual according to the equipment number, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until the group meets a first preset condition;
carrying out mutation on the second target individual according to the task sequence, carrying out random exchange on genes corresponding to a plurality of elements of the mutated individual belonging to the same scheduling set in the mutation process, and carrying out mutation operation iteratively to generate new individuals until the group meets a second preset condition;
and comparing the individuals respectively obtained by the variation operation iteration, and screening out an optimal solution as a pre-dispatching plan.
8. A discrete manufacturing-based production scheduling system, comprising:
the production scheduling model building module is used for building a production scheduling model based on the target with the minimum weighted sum of time and cost;
the scheduling optimal solution solving module is used for solving the scheduling optimal solution of the scheduling model by adopting a genetic algorithm to obtain a pre-scheduling plan;
and the task dispatching module is used for auditing the pre-dispatching plan, acquiring the dispatching plan after the auditing is passed, and dispatching the tasks according to the dispatching plan.
9. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 7.
CN202111434127.0A 2021-11-29 2021-11-29 Production scheduling method, system, equipment and storage medium based on discrete manufacturing Pending CN114066312A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115438970A (en) * 2022-09-13 2022-12-06 韶关液压件厂有限公司 Large-scale production scheduling method suitable for discrete manufacturing of workpieces

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115438970A (en) * 2022-09-13 2022-12-06 韶关液压件厂有限公司 Large-scale production scheduling method suitable for discrete manufacturing of workpieces
CN115438970B (en) * 2022-09-13 2023-05-02 韶关液压件厂有限公司 Large-scale production scheduling method suitable for discrete manufacturing of workpieces

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