CN115115206A

CN115115206A - Dynamic production scheduling method, module and system based on TW-GA combination

Info

Publication number: CN115115206A
Application number: CN202210712602.4A
Authority: CN
Inventors: 朱洁; 张锦宇
Original assignee: Xiamen Zhiwen Technology Co ltd
Current assignee: Xiamen Zhiwen Technology Co ltd
Priority date: 2022-06-22
Filing date: 2022-06-22
Publication date: 2022-09-27

Abstract

The application provides a dynamic scheduling method, a module and a system based on TW-GA combination, which start with a scheduling service realization path planning and an optimal result solving algorithm and aim at maximally releasing the overall production capacity of a workshop. The following problems are solved: solving the problem of non-uniform input samples, adopting a time round algorithm to carry out scheduling task pretreatment, and unifying input samples of population inheritance; the problem of fine granularity scheduling is solved, the split of combining fine granularity levels based on equipment, worker skills and procedures is realized, and the unit regulation of fine granules is realized through a time window; the problem of simultaneous production scheduling of multiple categories is solved, and multiple time wheels are used for superposition projection to achieve simultaneous production scheduling of multiple categories; the problem that dynamic calculation cannot be achieved or calculation cost is high is solved, a local optimization iterative solution method based on time window internal allocation is achieved through dynamic plugging and unplugging of a time window, and second-level calculation response under ten-million-level data scale is achieved; and a multi-loop calculation process is introduced, so that the problem of low probability of obtaining a global optimal solution is solved.

Description

Dynamic production scheduling method, module and system based on TW-GA combination

Technical Field

The application relates to the technical field of flexible customized intelligent manufacturing, in particular to a dynamic scheduling method, module and system based on TW-GA combination.

Background

At present, the flexible customization industry of the printing and dyeing industry has low information degree on the whole, most of the flexible customization industry is started to use or is a simple planned production scheduling method in advance, and the flexible customization industry belongs to static scheduling. Generally, simple production wave scheduling is carried out according to the to-be-processed task and the actual capacity before production according to the productivity. Once the wave number of the production plan is determined, the production plan cannot be changed, the scheduling is only specific to a workshop or a production line, specific process arrangement cannot be detailed, and the workshop field production can be scheduled only through personnel experience.

Most of the existing flexible customized production scheduling plans are scheduled according to wave times, belong to prior production scheduling, and cannot adjust the production scheduling plans in real time according to changes. The scheduling algorithm mostly adopts a traditional heuristic algorithm, such as: simulated annealing, hill climbing algorithm, genetic algorithm, Tabu algorithm and the like. Most of the similar technologies use a certain algorithm or use a plurality of algorithms in combination to solve the optimal solution, and are affected by the regularity of input conditions and the complexity of the algorithms, so that the quality of calculation results is generally poor. The main problems of the prior similar technology are as follows:

input samples are not uniform, and scheduling results cannot be guaranteed: the calculation result of the heuristic algorithm is greatly influenced by the input samples and the disturbance factors. Most of similar products directly use the process flow configuration information of the products to be generated as samples, and carry out disturbance through a random feeding sequence, so that the accuracy of a calculation result is difficult to ensure under the condition. The fact that the input samples cannot be unified and standardized is an important reason that the similar technology is poor in scheduling effect.

Scheduling based on the flow coarse granularity: the production scheduling of equipment, worker skills and procedure combination fine granularity levels cannot be achieved, most of the existing technologies on the market are based on the production scheduling of procedure flow coarse granularity, and the situations that a large number of procedures are idle or waiting for the procedures commonly exist.

Scheduling based on single product categories: the production scheduling can only be carried out aiming at the process flow of a single product, the mixed production scheduling of a plurality of process flows of a plurality of products cannot be realized, the utilization rate of equipment and manpower is extremely low, and the workshops can only carry out the production in sequence of one type.

The given scheduling plan is not variable or the cost of variation is huge: the scheduling plan produced based on single or combined algorithms cannot be quickly adapted to abnormal conditions of a workshop (such as equipment failure, worker leave, material loss, low finished product qualification rate and the like), the algorithms need to re-sort input and conditions under the changing conditions and re-calculate, the scheduling calculation is extremely long in time consumption, various abnormal conditions occur frequently on a production site, the conditions cannot meet the requirements of actual conditions, and the basic reason why the real-time scheduling of the same type of products cannot be achieved is also shown.

Single calculation link, global optimization rate is low: the similar products are mostly designed in a single calculation link, a heuristic algorithm is adopted in a diversified flexible customization scene, and the probability of obtaining a global optimal solution through one-time calculation is not high, so that the similar products are mostly local optimal results.

In view of this, it is very important to design a dynamic production scheduling method and system that can solve the problems of non-uniform input samples, fine-granularity production scheduling, simultaneous production scheduling of multiple categories, incapability of dynamic calculation, and the like.

Disclosure of Invention

The embodiment of the application provides a dynamic scheduling method, a module and a system based on TW-GA combination to solve the technical problems mentioned in the background technology section.

In a first aspect, an embodiment of the present application provides a dynamic scheduling method based on a TW-GA combination, including the following steps:

s1, constructing a time wheel group and a product processing time line;

s2, selecting a to-be-processed procedure task according to a procedure node of a product processing timeline by using a task selector in the time wheel set, and inlaying the to-be-processed procedure task to a time window of the time wheel set;

s3, repeating the step S2 until no task of the procedure to be processed exists, and outputting a basic scheduling sample which comprises information of a time window;

s4, sorting the processing tasks according to N sorting strategies (N is more than or equal to 1), and repeating the steps S1-S3 to obtain N basic production scheduling samples;

s5, randomly selecting two basic production scheduling samples from the N basic production scheduling samples, carrying out time window crossing aiming at basic time wheels or overlapped time wheels of the same type in the two basic production scheduling samples, carrying out fitness evaluation on the result of the time window crossing, and storing individuals with improved fitness as next generation basic production scheduling samples;

s6, repeating the step S5 until the N basic production scheduling samples complete the time window cross evolution; and

and S7, repeating the steps S5-S6 until a preset algebra is evolved or the evolution convergence reaches the evolution stop standard, and outputting a production scheduling sample with the highest fitness.

The method unifies the input sample standard of population inheritance based on the sample generation mode of the combination of the product processing time line and the time wheel, and ensures the stability of the optimal solution. And a multi-loop calculation process is introduced, so that the problem of low probability of obtaining a global optimal solution can be solved. Therefore, the method can greatly reduce the calculation and time cost of the scheduling, and can practically adjust the scheduling according to the workshop situation in real time.

In a specific embodiment, in step S1, the time wheel group includes a basic time wheel of the equipment bench, a basic time wheel of the manual skill, and an overlapping time wheel where an overlap occurs between the equipment bench and the manual skill; the product processing time line comprises the processing time of each procedure of the product and the arrangement sequence of each procedure of the product.

The system creates a basic time wheel for each equipment machine in the workshop and creates a time wheel for each worker on duty according to skills, namely the system creates a basic time wheel for each time-consuming process, and the system creates an overlapping time wheel by overlapping the manual work and the machine based on the basic time wheel. The method realizes the resolution of the combined fine granularity level based on equipment, worker skill and procedures, and realizes the unit regulation of fine particles through a time window.

In a particular embodiment, step S1 further includes the following sub-steps:

s11, constructing a basic time wheel and an overlapping time wheel;

s12, eliminating the basic time wheel with the overlapped time wheel;

s13, aligning the overlapped time wheel and the basic time wheel of the step S12; and

s14, starting the overlapped time wheel and the basic time wheel of the step S13, advancing one unit time, informing the task selector on the corresponding time wheel to select the task of the working procedure to be processed.

By the method, simultaneous production scheduling of multiple categories can be realized by adopting a method of overlapping projection of multiple time wheels.

In a specific embodiment, in step S2, the task selector in the time wheel group is used to select the corresponding task of the process to be processed according to the process node of the product processing timeline, and the following conditions are satisfied:

a. the current time window of the basic time wheel or the overlapping time wheel must meet the time-consuming requirement of task processing of the working procedure to be processed;

b. a base time round or an overlapping time round that has been used to prioritize tasks;

c. the usage rates of the same type of basic time wheels differ by a proportion not higher than 30%.

d. If the time wheel group includes overlapping time wheels, the base time wheel is selected with a minimum impact amount.

In a specific embodiment, in step S5, the time window crossing is performed for the same type of basic time wheel or overlapping time wheel in the two basic production samples, and the following conditions are satisfied:

a. the time window intersection needs to meet the constraint of the arrangement sequence of each procedure of the product;

b. the time windows after the intersection have surplus time;

c. time windows in the same basic time wheel or overlapped time wheels can be subjected to self-intersection;

d. only one task node of the same machining procedure exists after the time windows are crossed.

In a specific embodiment, in step S4, the processing tasks are sorted according to N (N is greater than or equal to 1) sorting strategies, where the sorting strategies include sorting by product brand, sorting by processing duration, sorting by equipment machine occupation duration proportion, sorting by manual occupation duration proportion, sorting by process quantity, and sorting by process section precedence heuristic.

The system generates corresponding sequencing combination aiming at each strategy, and different sequencing combinations influence the combination and supply sequence of the processing tasks of the procedures, which can result in completely different processing scheduling plans.

In a second aspect, an embodiment of the present application provides a dynamic scheduling module based on a TW-GA combination, including:

the construction unit is used for constructing a time wheel set and a product processing time line;

the task selector of the time wheel set is used for selecting a to-be-processed procedure task according to a procedure node of a product processing timeline, and embedding the to-be-processed procedure task onto a time window of the time wheel set;

the acquisition unit is used for repeating the operation on the task selection unit until no task of the procedure to be processed exists, and outputting a basic scheduling sample which comprises information of a time window;

the sample production unit is used for sequencing the processing tasks according to N (N is more than or equal to 1) sequencing strategies, repeating the operations on the construction unit, the task selection unit and the acquisition unit to obtain N basic scheduling samples,

the evolution unit is used for randomly selecting two basic production scheduling samples from the N basic production scheduling samples, carrying out time window crossing aiming at basic time wheels or overlapped time wheels of the same type in the two basic production scheduling samples, carrying out fitness evaluation on the result of the time window crossing, and storing individuals with improved fitness as next generation basic production scheduling samples;

the iteration unit is used for repeating the operation on the evolution unit until the N basic production scheduling samples complete the time window cross evolution; and

and the output unit is used for repeating the operations on the evolution unit and the iteration unit until the evolution reaches a preset algebra or the evolution convergence reaches an evolution stop standard, and outputting a production scheduling sample with the highest fitness.

In a third aspect, the application provides a dynamic scheduling system based on a TW-GA combination, which includes an order task preprocessing module, a workshop situation awareness module, and the dynamic scheduling module, where the order task preprocessing module provides a processing task for the dynamic scheduling module, and the workshop situation awareness module provides basic data support for the order task preprocessing module and the dynamic scheduling module.

In a specific embodiment, the workshop situation perception module comprises a basic data unit, a whole-process data acquisition unit and a state updating notification management unit;

the basic data unit is used for establishing a workshop material storage base, a production equipment machine base, a skill requirement and time consumption base of a production process, a production process flow base of various products and a worker skill base;

the whole-process data acquisition unit is used for acquiring worker on-duty information, equipment information, logistics information, product processing information and product quality inspection information, and is provided with a risk assessment model;

and the state updating notification management unit is used for event publishing service of message subscription.

In a specific embodiment, the order task preprocessing module comprises a client ordering unit, a workshop order supplementing unit and an order to-be-processed task management unit;

the client ordering unit and the workshop order supplementing unit push the latest order information to the order to-be-processed task management unit in real time;

and the to-be-processed task management unit carries out capacity evaluation, judges whether a new workshop production frequency is generated or not, and carries out task disassembly at a working procedure level on the order of the workshop production frequency if the new workshop production frequency is generated.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs a method as in any one of the above.

The dynamic scheduling method, module and system based on the TW-GA combination, provided by the invention, realize scheduling of fine granularity level based on equipment, worker skills and procedure combination. The method has the following beneficial effects: (1) the simultaneous production scheduling of multiple categories is realized; (2) the vacancy rate of workshop production elements is greatly reduced, and the utilization rate of the workshop production elements is improved. (3) The sample generation mode based on the product processing time line and time wheel combination unifies the input standard of the sample and ensures the stability of the optimal solution. (4) The calculation and time cost of scheduling are greatly reduced, and scheduling adjustment is practically realized according to the workshop situation in real time. (5) Through two times of heuristic evolution, the hit rate of the global optimal solution is improved to a great extent.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings in which:

FIG. 1 is a flow chart of a dynamic scheduling method based on TW-GA combinations according to the present application;

FIG. 2 is a flowchart illustrating an overall implementation of the TW-GA combining method according to an embodiment of the present application;

FIG. 3 is a schematic diagram of the basic structure of a time wheel according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a product processing timeline according to an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating a process task to be processed embedded in a time window of a time wheel set according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of the structure of population evolution according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a dynamic scheduling module based on TW-GA combination according to the present application;

FIG. 8 is a schematic structural diagram of a dynamic scheduling system based on TW-GA combination in the present application;

FIG. 9 is a flowchart illustrating the operation of a plant dynamics awareness module according to an embodiment of the present disclosure;

FIG. 10 is a flow chart of the operation of an implement order task preprocessing module according to the present application;

FIG. 11 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 shows a flowchart of a dynamic scheduling method based on TW-GA combination according to the present application, and fig. 2 shows an overall flowchart of an implementation of the TW-GA combination method according to an embodiment of the present application. Referring to fig. 1 and 2 in combination, the method includes:

s1, building a time wheel group and a product processing time line.

The time wheel group comprises a basic time wheel of the equipment machine table, a basic time wheel of the artificial skill and an overlapped time wheel which is overlapped between the equipment machine table and the artificial skill; the product processing time line comprises the processing time of each procedure of the product and the arrangement sequence of each procedure of the product.

In the present embodiment, the building of the time wheel group in step S1 includes the following sub-steps:

s11, constructing a basic time wheel and an overlapping time wheel;

the system creates a basic time wheel for each equipment machine table in the workshop, creates a basic time wheel for each worker on duty according to skills, and the time period of all basic time wheels is standard on-off hours (4 hours in the morning, 5 hours in the afternoon and 4 hours in the evening). The time wheel organization is shown in fig. 3, the minimum time scale of the basic time wheel is second, and the minimum time window is fixed to be 1 second.

And constructing a product processing time line, namely constructing a time line required by product processing for each product according to the product process flow. The product processing time line only defines the constraints of the processing time length and the sequence of each procedure of the product. The product processing timeline structure is shown in fig. 4.

The system creates a base time wheel for each time consuming process and the system creates an overlap time wheel based on the base time wheel for overlap of the human and machine tools. The specific creation rules are shown in table 1:

TABLE 1

Wherein the base time wheel is an atomic time wheel and is inseparable. The overlapping time wheel is a special directly-usable time wheel for binding worker skills and machine stations when machine station equipment needs the worker skills for operation, the overlapping time wheel has mutual exclusivity, the overlapping time wheel takes effect immediately when the worker skills take effect on a certain machine station, and other overlapping time wheels occupying the specific worker lose effect immediately.

Such as: directly spout the workshop and have 3 and directly spout printing and dyeing board, 2 drying machine platforms, 2 sewing machine platforms, 6 staff (wherein have the picture to confirm technical skill 1 people, directly spout printing and dyeing machine operation technical skill 1 people, drying machine operation 1 people, quality inspection 1 people, master simultaneously and directly spout 2 people of printing and dyeing machine operation and sewing machine platform), it is shown as table 2 to establish the time wheel condition under this condition:

TABLE 2

Serial number	Time wheel name	Time wheel type	Number of time rounds created
				1	Direct-injection printing and dyeing machine table time wheel	Basic time wheel	3
2	Time wheel of drying machine	Basic time wheel	2
				3	Time wheel of sewing machine table	Basic time wheel	2
4	Worker picture confirmation skill time wheel	Basic time wheel	1
				5	Worker direct-injection printing and dyeing machine operation skill time wheel	Basic time wheel	3
6	Worker drying machine platform operation skill time wheel	Basic time wheel	1
				7	Worker quality inspection skill time wheel	Basic time wheel	1
8	Worker sewing machine platform skill time wheel	Basic time wheel	2
				9	Combining the time wheel 1 and the time wheel 5	Overlapping time wheel	9
10	Combining the time wheel 2 and the time wheel 6	Overlapping time wheel	2
				11	Combining the time wheel 3 and the time wheel 8	Overlapping time wheel	4

By the method, the problem of fine-granularity scheduling can be solved, the separation of fine-granularity levels based on equipment, worker skills and procedures is realized, and the unit arrangement of fine granules is realized through a time window; the problem of multi-product simultaneous scheduling is solved, and the method of overlapping projection of a plurality of time wheels is adopted to realize the simultaneous scheduling of the multi-product.

The building of the time wheel group in step S1 further includes the sub-steps of:

s12, eliminating the basic time wheel with the overlapped time wheel;

s13, aligning the overlapped time wheel and the basic time wheel in the step S12, and specifically operating as follows: and pointing the time pointers of all the remaining time wheels after being eliminated to 0 point.

S14, starting the overlapped time wheel and the basic time wheel in the step S13, advancing one unit time, and informing the task selector on the corresponding time wheel to select the task of the working procedure to be processed.

With continuing reference to fig. 1 and 2, the TW-GA combination-based dynamic scheduling method further includes the steps of:

s2, selecting the to-be-processed process tasks according to the process nodes of the product processing time line by using the task selector in the time wheel set, and inlaying the to-be-processed process tasks to the time window of the time wheel set.

Namely, the product processing time line is according to the time wheel group on the process node. The selected task of the procedure to be processed and the selected time wheel set are combined with the product processing time line, the procedure node is taken as an atom, the procedure on the product processing time line is embedded into a time window suitable for the time wheel set, and the structure schematic is shown in fig. 5.

And sequentially performing the operation of the step S2 by all the time wheel sets until all the time wheel sets have performed the selection of the process task to be processed or no process task to be processed is required.

In this embodiment, a task selector in a time wheel set is used to select a corresponding task of a procedure to be processed according to a procedure node of a product processing timeline, and the following conditions are required to be satisfied:

c. the utilization rate of the same type of basic time wheels is not different by more than 30%.

d. If the time wheel group includes overlapping time wheels, the base time wheel is selected with a minimum impact amount. For example: the overlapping time wheel is formed by overlapping 3 equipment machine tables and 2 worker skills, and then only 2 time wheels are selected according to the 2 worker skills.

And S3, repeating the step S2 until no task of the procedure to be processed exists, and outputting a basic scheduling sample which comprises information of a time window.

By the method, the problem that input samples are not uniform can be solved, the scheduling task is preprocessed by adopting the time round algorithm, and the input samples of population inheritance are unified.

S4, sorting the processing tasks according to N (N is more than or equal to 1) sorting strategies, and repeating the steps S1-S3 to obtain N basic production scheduling samples.

In this embodiment, in step S4, a feeding initialization phase is further included, specifically, the processing tasks are sorted according to N (N is greater than or equal to 1) sort strategies, where the sort strategies are established strategies and are multiple, and at least include sorting according to product brand, sorting according to processing duration, sorting according to equipment machine occupation duration proportion, sorting according to manual occupation duration proportion, sorting according to process quantity, and heuristic sorting according to process section sequence.

The system generates corresponding sequencing combination aiming at each strategy, and different sequencing combinations influence the combination and supply sequence of the processing tasks of the procedures, which can result in completely different processing scheduling plans. Namely, for N different sorts generated in the feeding initialization stage, steps S1 to S3 are repeated to obtain N basic scheduling strategies.

S5, randomly selecting two basic production scheduling samples from the N basic production scheduling samples, carrying out population evolution aiming at basic time wheels or overlapping time wheels of the same type in the two basic production scheduling samples, wherein the specific implementation mode is time window crossing, carrying out fitness evaluation on the result of the time window crossing, and storing individuals with improved fitness as next generation basic production scheduling samples.

In this embodiment, in step S5, the time window crossing is performed for the same type of basic time wheel or overlapping time wheel in the two basic production samples, and the following conditions are satisfied:

b. the time windows after the intersection have surplus time;

c. time windows in the same basic time wheel or the same overlapped time wheel can be subjected to self-intersection, namely self-evolution;

And evaluating the fitness of the result of the crossing of the time windows, wherein the fitness evaluation model relates to five aspects of total processing time, total goods moving times, total goods moving distance, total machine switching times and total manual switching times. The individuals with improved fitness after corresponding mutation enter the next generation, and the schematic diagram is shown in fig. 6.

The method can solve the problems that dynamic calculation cannot be achieved or the calculation cost is high, a local optimization iterative solution method based on time wheel grid internal allocation is achieved through dynamic plugging and unplugging of time wheel grids (time windows), and second-level calculation response under ten-million-level data scale is achieved.

and S7, repeating the steps S5-S6 until the evolution reaches a preset algebra or the evolution convergence reaches the evolution stopping standard, and outputting a production scheduling sample with the highest fitness.

The overall process of the method can be roughly divided into two parts, namely normalized sample set generation and sample evolution. Where the time wheel set generator is responsible for the generation of a set of normalized samples and the ethnicity generator handles the evolution of the samples. The TW-GA hybrid algorithm comprises three parts, namely a feeding initialization phase, a basic scheduling plan sample generation phase (TW algorithm) and a scheduling plan optimal solution solving phase (GA algorithm). The method unifies the input sample standard of population inheritance based on the sample generation mode of the combination of the product processing time line and the time wheel, and ensures the stability of the optimal solution. And a multi-loop calculation process is introduced, so that the problem of low probability of obtaining a global optimal solution can be solved. Therefore, the method can greatly reduce the calculation and time cost of the scheduling, and can practically adjust the scheduling according to the workshop situation in real time.

Fig. 7 is a schematic structural diagram of a dynamic scheduling module based on TW-GA combination according to an embodiment of the present application, and as shown in the drawing, the module 200 includes:

a construction unit 210, configured to construct a time wheel group and a product processing timeline;

the task selection unit 220 is used for the task selector of the time wheel set to select the process tasks to be processed according to the process nodes of the product processing timeline, and the process tasks to be processed are embedded into the time windows of the time wheel set;

an obtaining unit 230, configured to repeat operations in the task selecting unit 220 until there is no to-be-processed procedure task, and output a basic scheduling sample, where the basic scheduling sample includes information of a time window;

the sample production unit 240 is used for sequencing the processing tasks according to N (N is more than or equal to 1) sequencing strategies, repeating the operations of the construction unit 210, the task selection unit 220 and the acquisition unit 230 to obtain N basic production scheduling samples,

the evolution unit 250 is used for randomly selecting two basic production scheduling samples from the N basic production scheduling samples, performing time window crossing aiming at basic time wheels or overlapped time wheels of the same type in the two basic production scheduling samples, performing fitness evaluation on the result of the time window crossing, and storing individuals with improved fitness as next generation basic production scheduling samples;

an iteration unit 260, configured to repeat operations in the evolution unit 250 until the N basic production scheduling samples complete time window cross-evolution; and

and the output unit 270 is configured to repeat operations of the evolution unit 250 and the iteration unit 260 until evolution reaches a preset algebra or evolution convergence reaches an evolution stop standard, and output a production scheduling sample with the highest fitness.

Fig. 8 shows a schematic structural diagram of the dynamic scheduling system based on TW-GA combination according to the present application. As shown in fig. 8, the system includes an order task preprocessing module, a workshop situation awareness module, and the above dynamic scheduling module, where the order task preprocessing module provides a processing task for the dynamic scheduling module, and the workshop situation awareness module provides basic data support for the order task preprocessing module and the dynamic scheduling module.

FIG. 9 is a flow chart illustrating the operation of the plant dynamics awareness module according to an embodiment of the present disclosure. With combined reference to fig. 8 and 9, the plant situation awareness module includes a basic data unit, an overall process data acquisition unit, and a status update notification management unit.

the specific operation is as follows: the modeling of workshop staff, production equipment and production materials (man-machine materials) is carried out through the basic data unit. The method comprises the steps of establishing a workshop material library (aiming at monitoring the condition of processed materials in real time and helping managers to allocate the materials), establishing a production equipment machine library (aiming at acquiring key operation parameter indexes of the equipment), establishing a skill requirement and time consumption library of production procedures (aiming at acquiring the skill and time consumption parameters required by a single minimum procedure), establishing a production procedure flow library of various products (by taking the products as a main line, combining the procedures required by product processing and defining the sequence constraint of the required procedures), and establishing a worker skill library (aiming at acquiring the working skills of workers).

the specific operation is as follows: by means of the basic data unit, the data acquisition unit is integrated with external systems in the whole workshop process to acquire real-time conditions of human, machine, material and processing tasks. And on the aspect of personnel, the on-duty condition of workers is collected by butting the ERP system and the station card punching system. In the aspect of the equipment of the machine station, information such as real-time operation, maintenance and repair of the equipment is acquired by butting a TPM (equipment management System) of a factory. And in the aspect of materials, the actual logistics information of a front warehouse of the workshop is obtained through a butt WMS (warehouse management system). The real-time status of the processing task is obtained by a MES (manufacturing execution system) of a docking factory. Finished product quality inspection information is obtained through the butt joint product control system, so that the product with high defective rate is arranged and optimized.

And moreover, the whole-process data acquisition unit is provided with a risk evaluation model, and dynamic scoring of the observation items is carried out based on a strategy of scoring the items. Any change in the data triggers risk assessment work on the corresponding project, affecting the scoring of the project.

Specifically, the status update notification management unit provides an event publication service based on a message subscription, and provides a message subscription service based on an observation item, a score, and a change item. In the invention, the dynamic scheduling system can automatically subscribe event information in two aspects of equipment failure, personnel on-off post and risk scoring information for evaluating the delay condition of the processing task.

FIG. 10 is a flow chart illustrating the operation of the order task preprocessing module according to an embodiment of the present invention. Referring to fig. 8 and 10 in combination, the order task preprocessing module includes a customer placing unit, a workshop replenishment unit and an order to-be-processed task management unit. The order preprocessing flow mainly comprises the step of disassembling the processing procedure based on the content of the customized product in the order. The original order information records the contents of the types of the products to be processed, the quantity of the products to be processed, the customized projects of the products and the like. Because the processing work of the workshop is based on a single process and is not directly related to the order, the original order information cannot be directly used by the workshop, and the disassembly based on the process granularity is required to be carried out, so that a task pool of the process to be processed is generated.

The system comprises a client ordering unit, a workshop order supplementing unit, an order waiting processing task management unit and an order sending unit, wherein the client ordering unit and the workshop order supplementing unit push the latest order information to the order waiting processing task management unit in real time;

and the to-be-processed task management unit carries out capacity evaluation, specifically, whether a new workshop production frequency is generated or not can be judged by judging whether residual products exist in the workshop or not, and if the new workshop production frequency is generated, the task dismantling of the working procedure level is carried out on the order of the workshop production frequency.

The task disassembling of the process dimension takes the minimum process node as a core, and combines information such as product class processing process flow, process configuration, personnel skills and the like in the order to form a final process processing required resource combination to form a process processing task pool.

The process machining task pool list information items are shown in table 3.

TABLE 3

When the state updating notification management unit notifies the concerned workshop situation event, the scheduling system can carry out dynamic adjustment based on the production planning time wheel. Different policies may be supported depending on the notification event size.

The major adjustment event mainly comprises the following steps: equipment damage, insufficient raw materials, temporary leave for workers, newly increased production frequency and adjusted production frequency events. After receiving the large adjustment event, the system stops the operation of the time wheel pointer, and workers and the operating machine can not get a new processing task (which is equivalent to stopping the issuing of a workshop production task) after the stop. After stopping the time wheel pointer, the time wheel turntable automatically releases tasks which are not processed yet, and the TW-GA combination method is restarted to generate a new scheduling plan.

The small adjustment event mainly comprises the following steps: temporarily shutting down equipment for a certain time, temporarily adding small-batch products, and temporarily adjusting the production priority of the small-batch products. After receiving the small adjustment event, the system will not stop the operation of the time wheel pointer, and the system will perform task rearrangement of the corresponding strategy according to the time window after the current pointer, and the strategy mainly includes: replacement of a task time window of a machining procedure and overall migration (forward and backward) of a procedure time tight line time window.

The Flexible customized intelligent Scheduling method based on the TW-GA combination and improved by the data of the whole production process of the printing and dyeing industry is a dynamic Scheduling method, and is effective practice of Flexible Job Scheduling Problem (FJSP) in the printing and dyeing industry.

The flexible job shop scheduling problem is a special case of the classic single production shop scheduling problem (JSP), and the problems of multiple product types, nonstandard products, complex production procedures, various required worker skills and high processing flow allocation degree in a flexible customization scene are solved. FJSP is more challenging than traditional JSP because it needs to consider not only the problem of cross-product type operation path optimization but also the problem of cross-process flow process combination, is a typical problem integrating path optimization and combination optimization, and belongs to the NP-HARD problem solving category.

The TW-GA combined flexible customized intelligent production scheduling method based on the improvement of the whole production process data of the printing and dyeing industry workshop, provided by the application, takes the whole workshop as a whole, models the equipment and personnel in the workshop, and collects the equipment, personnel and production state data in real time by combining a dynamic workshop current situation sensing system on the basis of modeling, so that a whole process database is formed. The improved TW-GA combined method is analyzed and processed based on the whole process database, and corresponding scheduling adjustment is made.

The population Genetic Algorithm (GA) is designed and proposed according to the evolution rule of organisms in the nature, is a calculation model of a biological evolution process for simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. Through the iteration of continuous selection, crossing and variation of population individuals, the optimal individual is found. The genetic algorithm has strong global search capability and poor local search capability, which has higher requirements on the quality of the basic population and the disturbance factors. Therefore, the basic population is generated by using the time-round algorithm, the individual quality is guaranteed to a great extent, and the premature convergence condition is avoided to a great extent.

The time-round algorithm is a time-based carousel scheduling model algorithm. In the time wheel algorithm system, time wheels are created according to processes, and each process has a scheduling time wheel belonging to the process. All time wheels are modeled by taking the production working time as the time wheel length and the represented process single piece processing time as the time grid. And after modeling, overlapping and aligning each process time wheel. Filling the time round grids with the scattered process machining requirements according to a set strategy, and thus forming an initialization scheduling plan. In a specific processing process, such as sending equipment or manual abnormity, the time slots within the time wheel influence range are automatically closed, tasks in the time slots are removed, and rearrangement is carried out. The time round algorithm not only can fully exert the advantage of time round time windowing, but also can ensure the advantage of task scheduling based on the queue.

The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the method of any one of the above.

As shown in fig. 11, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a Liquid Crystal Display (LCD) and the like and a speaker and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311. The computer program performs the above-described functions defined in the method of the present application when executed by the Central Processing Unit (CPU) 301. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, an analysis module, and an output module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements in which any combination of the features described above or their equivalents does not depart from the spirit of the invention disclosed above. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A dynamic scheduling method based on TW-GA combination is characterized by comprising the following steps:

s1, constructing a time wheel set and a product processing time line;

s2, selecting a to-be-processed procedure task according to a procedure node of the product processing time line by using a task selector in the time wheel set, and inlaying the to-be-processed procedure task to a time window of the time wheel set;

s3, repeating the step S2 until the task of the working procedure to be processed does not exist, and outputting a basic scheduling sample which comprises the information of the time window;

s5, randomly selecting two basic production scheduling samples from the N basic production scheduling samples, carrying out time window crossing on the basic time wheels or the overlapped time wheels of the same type in the two basic production scheduling samples, carrying out fitness evaluation on the result of the time window crossing, and storing the individuals with improved fitness as next generation basic production scheduling samples;

s6, repeating the step S5 until the time window cross evolution of the N basic production samples is completed; and

and S7, repeating the steps S5-S6 until the evolution reaches a preset algebra or the evolution convergence reaches the evolution stop standard, and outputting the scheduling sample with the highest fitness.

2. The TW-GA combination-based dynamic scheduling method of claim 1, wherein in step S1, the time wheel group includes a basic time wheel of the equipment machine, a basic time wheel of the manual skill, and an overlapping time wheel where an overlap occurs between the equipment machine and the manual skill; the product processing time line comprises the processing time of each procedure of the product and the arrangement sequence of each procedure of the product.

3. The TW-GA combination-based dynamic scheduling method of claim 1, wherein the step S1 further comprises the following sub-steps:

s11, constructing a basic time wheel and an overlapping time wheel;

s12, eliminating the basic time wheel which has already created the overlapped time wheel;

s13, aligning the overlapping time wheel and the base time wheel of step S12; and

s14, starting the overlapped time wheel and the basic time wheel of the step S13, and informing the task selector on the corresponding time wheel to select the to-be-processed process task every unit time advances.

4. The TW-GA combination-based dynamic scheduling method of claim 1, wherein in step S2, a task selector in the time wheel group is used to select a corresponding to-be-processed task according to a process node of the product processing timeline, and the following conditions are satisfied:

a. the current time window of the basic time wheel or the overlapping time wheel can meet the time-consuming requirement of task processing of the working procedure to be processed;

b. the base time round or the overlapping time round that has been used preferentially selects tasks;

c. the usage rates of the basic time wheels of the same type differ by a proportion not higher than 30%.

d. If the time wheel group comprises overlapping time wheels, selecting a basic time wheel according to the minimum influence quantity.

5. The TW-GA combination-based dynamic scheduling method of claim 1, wherein in step S5, time window crossing is performed for the basic time wheel or the overlapping time wheel of the same type in the two basic scheduling samples, and the following condition is satisfied:

b. the time windows after the intersection all have surplus time;

c. time windows within the same base time wheel or overlapping time wheel may be self-intersecting;

d. and only one processing procedure task node exists after the time windows are crossed.

6. The TW-GA combination-based dynamic production scheduling method of claim 1, wherein in step S4, the processing tasks are ranked according to N (N ≧ 1) ranking strategies, which include ranking by product brand class, ranking by processing duration, ranking by equipment board occupancy duration proportion, ranking by manual occupancy duration proportion, ranking by number of processes, and ranking by process section precedence heuristic.

7. A dynamic scheduling module based on a TW-GA combination, the module comprising:

the building unit is used for building a time wheel group and a product processing time line;

the task selector of the time wheel set is used for selecting a to-be-processed procedure task according to a procedure node of the product processing timeline, and inlaying the to-be-processed procedure task to a time window of the time wheel set;

the acquisition unit is used for repeating the operation on the task selection unit until the task of the procedure to be processed does not exist, and outputting a basic scheduling sample which comprises the information of the time window;

the evolution unit is used for randomly selecting two basic production scheduling samples from the N basic production scheduling samples, carrying out time window crossing on the basic time wheels or the overlapped time wheels of the same type in the two basic production scheduling samples, carrying out fitness evaluation on the result of the time window crossing, and storing the individuals with improved fitness as the next generation basic production scheduling samples;

and the output unit is used for repeating the operation on the evolution unit and the iteration unit until the evolution reaches a preset algebra or the evolution convergence reaches the evolution stop standard, and outputting the scheduling sample with the highest fitness.

8. A TW-GA-combination-based dynamic scheduling system, comprising an order task preprocessing module, a workshop situation awareness module, and the dynamic scheduling module of claim 7, wherein the order task preprocessing module provides processing tasks for the dynamic scheduling module, and the workshop situation awareness module provides basic data support for the order task preprocessing module and the dynamic scheduling module.

9. The TW-GA combination-based dynamic scheduling system of claim 8, wherein the plant situation awareness module comprises a basic data unit, a full process data acquisition unit, and a status update notification management unit;

the whole-process data acquisition unit is used for acquiring worker on-duty information, equipment information, logistics information, product processing information and product quality inspection information, and is provided with a risk evaluation model;

10. The TW-GA combination-based dynamic scheduling system of claim 8, wherein the order task preprocessing module comprises a customer order placing unit, a shop order supplementing unit, and an order to-be-processed task management unit;

the client order placing unit and the workshop order supplementing unit push the latest order information to the order to-be-processed task management unit in real time;

and the to-be-processed task management unit carries out capacity evaluation, judges whether a new workshop production frequency is generated or not, and carries out task dismantling at a process level on an order of the workshop production frequency if the new workshop production frequency is generated.

11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.