A product dynamic scheduling method based on multiple constraints

Abstract

This invention relates to a dynamic product scheduling method based on multiple constraints, specifically targeting a high-temperature ham sausage production workshop. To ensure food quality and safety, balance production load between processes, and improve production efficiency and output, this method addresses the problem of dynamic scheduling between processes for multiple product varieties. The steps are as follows: Step 1: Establish multiple production models based on workshop data; Step 2: Establish multiple production scheduling rules based on the models in Step 1; Step 3: Dynamically select different production scheduling rules based on production execution; Step 4: When the production process triggers the redefinition conditions of the production scheduling rules, modify the model constraints and regenerate the production scheduling rules. This invention can quickly formulate corresponding local scheduling rules for the complex, multi-constraint production process in a high-temperature ham sausage production workshop, and dynamically select and adjust them based on the current production status, reducing potential product quality risks, ensuring relative balance of production capacity between processes, and improving production efficiency and output.

Description

Technical Field

[0001] This invention belongs to the field of production scheduling technology, and specifically relates to a product dynamic scheduling method based on multiple constraints. Background Technology

[0002] High-temperature refrigeration (HTGR) production lines for ham sausages are characterized by a wide variety of products, large order volumes, strict time constraints at each stage, and short delivery cycles. To cope with market competition and improve product quality, as well as enterprise sales revenue and profits, HTGR production lines for ham sausages often adopt a mixed-flow production model. This means that the same equipment unit can produce multiple types of products. While this production model provides enterprises with a wider market reach, it also makes the relationships between different equipment units more complex, rapidly increasing production constraints within the production line and further increasing the complexity of production process control. This leads to problems such as material waste, reduced production capacity, increased production costs, and increased potential product quality risks.

[0003] With the continuous improvement of automation and informatization in the manufacturing industry, scholars have conducted a large amount of theoretical research on production scheduling. However, research on production scheduling for high-temperature ham sausage production lines has mostly focused on production line design, without studying production scheduling under the complex production process conditions of high-temperature ham sausage production lines with multiple varieties and multiple constraints. This invention focuses on describing a scheduling method for the dynamic scheduling problem of high-temperature ham sausage production lines under multiple constraints. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a product dynamic scheduling method based on multiple constraints. This method aims to effectively and dynamically control the complex production scheduling process in the high-temperature production line of ham sausage, further balance the production load between processes, improve production efficiency and output, and ensure the quality and safety of the produced products.

[0005] The present invention employs the following technical solution to achieve the above objectives: a product dynamic scheduling method based on multiple constraints, comprising the following steps:

[0006] Step 1: Based on the production line tasks in the product manufacturing workshop, model the production unit, production product, production material, production order, and production process to form multiple production models. Establish a production constraint data table in the database and store the production constraints of each production model in the production constraint data table.

[0007] Step 2: Obtain the data in the production constraint data table, determine the production scheduling rules for different processes, form a subset of scheduling rules for each process; and determine the priority of the scheduling rules within each process based on the frequency of occurrence of production application scenarios on the production line.

[0008] Step 3: Sort the scheduling rules in the subset of scheduling rules for each process according to their priority to form the production scheduling rule execution queue for each process. Then, bind each production scheduling rule in the queue to its corresponding production application scenario to form the scheduling rule execution queue for the entire production line and save it.

[0009] Step 4: Select the highest priority production scheduling rule from the scheduling rule execution queue that matches the current production application scenario of each process, and send it to each production workstation in the production workshop to guide on-site production;

[0010] Step 5: When the production scheduling rule redefinition condition is received from the on-site production process, the production constraint conditions in the production constraint condition data table are updated, and the process returns to Step 2. Then, the process uses comprehensive production evaluation indicators to determine whether the regenerated production scheduling rule can be applied to the on-site production process. Finally, the production scheduling rule execution queue formed by the production scheduling rule that can be applied to the on-site production replaces the original production scheduling rule queue, and the process returns to Step 4.

[0011] The production model is a production unit model, used to model the physical information of the equipment units in the production workshop based on the physical layout of the equipment units; the production unit model includes at least one of the following: equipment unit production capacity, equipment unit model, equipment unit location information, equipment unit association relationship, and equipment process matching relationship;

[0012] The production model is a production product model, used to model the production products based on the production product information in the production workshop; the production product model includes at least one of the following: product type, product and production unit constraint matching relationship, and basic product information.

[0013] The production model is a production material model, used to model production materials based on material information in the production workshop. The production material model includes at least one of the following: material type, basic material information, material and product constraint matching relationship, and material and production unit constraint matching relationship.

[0014] The production model is a production order model, used to model production orders based on order information in the production workshop; the production order model includes at least one of the following: order quantity, order product matching relationship, order duration, order date matching relationship, and order inventory association relationship.

[0015] The production model is a production process model, used to model the production process based on the production process information of each product in the production workshop; the production process model includes at least one of the following: product production process route, process parameters, process constraints, product material consumption capacity, and production steps.

[0016] The production constraints include at least one of the following: order product process constraints, material inventory constraints, product inventory constraints, equipment production capacity constraints, product equipment process constraints, product on-site storage time constraints, and equipment relationship constraints, each characterized by parameters; the production constraints contained in each process are different from each other.

[0017] The process of updating the production constraints in the production constraint data table specifically involves using an electromagnetic-like algorithm to update the parameters in the production constraints, including the following steps:

[0018] By setting the parameters in the constraints as charged particles in an electromagnetic-like algorithm, and setting the upper and lower limits of the parameter changes as the range of charged particle movement, the net force on each charged particle is obtained by simulating the mutual attraction and repulsion of charged particles. This is used to change the position of the charged particles, i.e., change the constraint parameter values. The production constraint data table is then updated based on the changed constraint parameter values.

[0019] The process of determining whether the regenerated production scheduling rules can be applied to the on-site production process through comprehensive production evaluation indicators includes the following steps:

[0020] Based on on-site production, obtain the process load balance and the average utilization rate of process equipment;

[0021] If the process load balance and the average utilization rate of process equipment exceed the set thresholds, it means that the regenerated production scheduling rules can be applied to the on-site production process.

[0022] Otherwise, the regenerated production scheduling rules cannot be applied to the on-site production process, and the process returns to step 2.

[0023] The process load rate is:

[0024]

[0025] Where PF i TRW represents the process load factor of process i. i TT represents the actual production operation time of process i. i Indicates the production cycle time of process i;

[0026] The average utilization rate of the equipment in the aforementioned process is:

[0027]

[0028] TPF i This represents the average utilization rate of the equipment in process i. This represents the effective processing time of all equipment in process i. This represents the sum of the time all equipment in process i is occupied.

[0029] A product dynamic scheduling system based on multiple constraints includes:

[0030] The production model module is used to model production units, products, materials, orders, and processes based on the production line tasks in the production workshop, forming multiple production models. It also establishes a production constraint data table in the database and stores the production constraints of each production model in the production constraint data table.

[0031] The production scheduling rules module is used to acquire data from the production constraint data table, determine production scheduling rules for different processes, and form a subset of scheduling rules for each process; determine the priority of scheduling rules within each process based on the frequency of occurrence of production application scenarios on the production line; sort the scheduling rules within each process's scheduling rule subset according to their priority to form a production scheduling rule execution queue for each process, and bind each scheduling rule in the queue to its corresponding production application scenario, ultimately forming and saving the scheduling rule execution queue for the entire production line; select the highest priority production scheduling rule from the scheduling rule execution queue that matches the current production application scenario of each process, and send it to each production workstation in the production workshop to guide on-site production;

[0032] The production scheduling rule update module is used to update the production constraints in the production constraint data table when the production process on site triggers the redefinition of production scheduling rules, and then return to step 2. Then, it judges whether the regenerated production scheduling rules can be applied to the on-site production process through comprehensive production evaluation indicators. Finally, the production scheduling rule execution queue formed by the production scheduling rules that can be applied to on-site production replaces the original production scheduling rule queue.

[0033] The present invention has the following beneficial effects and advantages:

[0034] 1. This invention can fully consider all actual production constraints during production scheduling.

[0035] 2. This invention enables effective dynamic control of the complex production scheduling process in the high-temperature production line of ham sausage.

[0036] 3. When significant changes occur in the production process, this invention can use intelligent algorithms based on comprehensive production evaluation indicators to individually modify the constraints of each process and regenerate new suitable scheduling rules, ensuring the correctness of on-site production scheduling. Attached Figure Description

[0037] Figure 1 This is a flowchart of the product dynamic scheduling method in the high-temperature production workshop of ham sausage according to the present invention.

[0038] Figure 2 This is a class diagram illustrating the basic data relationships for production modeling in this invention.

[0039] Figure 3 This is an example diagram of the production scheduling rules for the finished product process obtained using the present invention;

[0040] Figure 4 This is an example diagram illustrating the production scheduling rules for finished product processes regenerated under the condition of triggering a redefinition of production scheduling rules, as described in this invention. Detailed Implementation

[0041] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0042] This invention relates to a dynamic product scheduling method based on multiple constraints, specifically targeting a high-temperature ham sausage production workshop. To ensure food quality and safety, balance production load between processes, and improve production efficiency and output, this method addresses the problem of dynamic scheduling between processes for multiple product varieties. The steps are as follows: Step 1: Establish multiple production models based on workshop data; Step 2: Establish multiple production scheduling rules based on the models in Step 1; Step 3: Dynamically select different production scheduling rules based on production execution; Step 4: When the production process triggers the redefinition conditions of the production scheduling rules, modify the model constraints and regenerate the production scheduling rules. This invention can quickly formulate corresponding local scheduling rules for the complex, multi-constraint production process in a high-temperature ham sausage production workshop, and dynamically select and adjust them based on the current production status, reducing potential product quality risks, ensuring relative balance of production capacity between processes, and improving production efficiency and output.

[0043] like Figure 1 The diagram shows the process flow of the product dynamic scheduling method in the high-temperature production workshop of ham sausage according to the present invention.

[0044] A product dynamic scheduling method based on multiple constraints includes the following steps:

[0045] Step 1: Based on the basic data of the high-temperature ham sausage production workshop, model the production units, products, materials, orders, and processes to create various production models. Establish a production constraint data table in the database and store all constraints from the models in this table.

[0046] Step 2: The production site scheduling service loads data from the production constraint data table, establishes production scheduling rules for different processes, and forms a subset of scheduling rules for each process. Each scheduling rule has its own production application scenario, and the priority of the scheduling rules within each process is determined based on the frequency of occurrence of the production application scenario on the production line. The subset of scheduling rules is represented as follows: Where R ijHere are the scheduling rules for each process, where m is the number of processes and n is the number of scheduling rules within each process.

[0047] Step 3: Sort the scheduling rules in the subset of scheduling rules for each process according to their priority to form the production scheduling rule execution queue for each process. Bind each scheduling rule in the queue to its corresponding production application scenario to form the scheduling rule execution queue for the entire production line. Store the information of the scheduling rule execution queue in the scheduling service at the production site.

[0048] Step 4: The scheduling service selects the highest priority production scheduling rule from the scheduling rule execution queue that matches the current production application scenario of each process. That is, the production scheduling rule that matches the production application scenario and is located at the top of the execution queue is sent to each production workstation on site through the scheduling instruction issuance interface to guide on-site production.

[0049] Step 5: When the scheduling service determines that the on-site production process triggers the redefinition condition of the production scheduling rule and the on-site application scenario changes, it adjusts the parameters of the production constraint conditions in the production constraint condition data table through intelligent algorithms, establishes new production scheduling rules, and judges whether the regenerated production scheduling rules can be applied to the on-site production process through comprehensive production evaluation indicators. Finally, the production scheduling rules that can be applied to on-site production form a new production scheduling rule execution queue to replace the original production scheduling rule queue in the scheduling service, and returns to step 4.

[0050] The intelligent algorithm specifically employs an electromagnetic-like mechanism algorithm. It sets the parameters in the constraints as charged particles in the algorithm, and sets the upper and lower limits of parameter changes as the movement range of these charged particles. By simulating the mutual attraction and repulsion characteristics of these charged particles, the algorithm calculates the net force acting on each particle to change its position, thus altering the constraint parameter values. Taking equipment production capacity constraints as an example, the equipment quantity parameter is set as charged particle 1, with its upper and lower limits being the maximum number of on-site equipment and the minimum number of equipment to be activated, respectively. The equipment production capacity parameter is set as charged particle 2, with its upper and lower limits being the maximum production capacity per unit and 0, respectively. The algorithm adjusts the parameter values ​​within their upper and lower limits to generate new production constraints. The electromagnetic-like mechanism algorithm is a simple stochastic intelligent optimization algorithm with superior search capabilities and speed, effectively handling complex problems.

[0051] The production unit model models the physical information of the equipment units in the production workshop, such as their location and attributes, based on the physical layout of the equipment units. Specifically, the production model includes information such as the production capacity of the equipment units, the model of the equipment units, the location information of the equipment units, the association relationship of the equipment units, and the matching relationship of the equipment processes.

[0052] The production product model is used to model the production products based on the production product information in the production workshop. The production model includes information such as product types, product and production unit constraint matching relationship, and basic product information.

[0053] The production material model models production materials based on material information in the production workshop. The production model includes information such as material types, basic material information, material and product constraint matching relationships, and material and production unit constraint matching relationships.

[0054] The production order model models production orders based on order information within the production workshop. This production model includes information such as order quantity, order product matching relationship, order duration, order date matching relationship, and order inventory association relationship.

[0055] The production process model is used to model the production process based on the production process information of each product in the production workshop. The model mainly includes information such as product production process route, process parameters, process constraints, product material consumption capacity, and production steps.

[0056] The various production constraints mainly include order product process constraints, material inventory constraints, product inventory constraints, equipment production capacity constraints, product equipment process constraints, product on-site storage time constraints, and equipment relationship constraints. The production constraints contained in each process are different from each other.

[0057] The priority of scheduling rules within each process is determined by the frequency of occurrence of the production application scenario. The higher the frequency of the production application scenario, the higher the priority of the scheduling rule, and the higher its position in the production scheduling rule execution queue. If multiple scheduling rules under the same production application scenario have the same priority, the rule selection method is based on the results of the comprehensive production evaluation index. The scheduling rule with better comprehensive production evaluation index results will be positioned higher in the production scheduling rule execution queue.

[0058] The redefinition conditions for the production scheduling rules mainly include order completion, order insertion, order splitting, order cancellation, critical equipment failure, insufficient material inventory, large differences in capacity between processes, and no remaining capacity in product inventory.

[0059] The comprehensive production evaluation index for judging the generation scheduling rules mainly includes two items: process load balance and average equipment utilization rate.

[0060] The aforementioned process load balance is determined by a comprehensive assessment of the process load rate of each process. The process load rate is the percentage of the actual production time of a process to its production cycle time, expressed as:

[0061]

[0062] Where PF i TRW represents the process load factor of process i. i TT represents the actual production operation time of process i. i This represents the production cycle time of process i.

[0063] The average utilization rate of equipment in the process is the ratio of the effective processing time of all equipment in the process to the sum of the time all equipment is occupied. This time is the time span from the start to the end of production for each piece of equipment in the process, and it is expressed as:

[0064]

[0065] TPF i This represents the average utilization rate of the equipment in process i. This represents the effective processing time of all equipment in process i. This represents the sum of the time all equipment in process i is occupied.

[0066] The dynamic product scheduling process in the high-temperature production workshop for ham sausages includes the following steps:

[0067] (1) Based on the basic data of the high-temperature production workshop of ham sausage, model the production unit, production product, production material, production order and production process to form a variety of production models;

[0068] (2) Based on the production constraints of each production model in (1), establish production scheduling rules for different processes, form a subset of scheduling rules for each process, and determine the priority of scheduling rules within each process based on the frequency of different production application scenarios in the high-temperature production workshop of ham sausage.

[0069] (3) The scheduling rules in each process scheduling rule subset are sorted according to their priority to form a production scheduling rule execution queue belonging to each process. Each production scheduling rule in the queue is bound to its corresponding production application scenario, and finally a scheduling rule execution queue for the entire production line is formed.

[0070] (4) Select the highest priority production scheduling rule from the scheduling rule execution queue that conforms to the current production application scenario of each process, and send it to each production station in the finished product area of ​​the ham sausage high-temperature production workshop to guide on-site production;

[0071] (5) When the production process triggers the redefinition condition of the production scheduling rule, the parameters in the production constraint condition are adjusted by the intelligent algorithm to generate new production constraint conditions. Then, return to step 2 and judge whether the regenerated production scheduling rule can be applied to the on-site production process through the comprehensive production evaluation index. Finally, the production scheduling rule execution queue formed by the production scheduling rule that can be applied to the on-site production replaces the original production scheduling rule queue.

[0072] like Figure 2 The diagram shown is a class diagram of the basic data relationships for production modeling in this invention.

[0073] Modeling of production units, products, materials, orders, and processes based on the relationships between basic workshop data:

[0074] 1. Based on the physical layout of equipment units in the high-temperature ham sausage production workshop, a model is created for the physical information of the equipment units, such as their location and attributes. This production model mainly includes information such as equipment unit production capacity, equipment unit model, equipment unit location information, equipment unit association, and equipment process matching relationship.

[0075] 2. Based on the production product information in the high-temperature ham sausage production workshop, a production model is created. This production model mainly includes information such as the types of products produced, the constraint matching relationship between products and production units, and basic product information.

[0076] 3. Model the production materials based on the production material information in the high-temperature production workshop of ham sausage. The production model includes information such as the types of production materials, basic material information, material and product constraint matching relationship, and material and production unit constraint matching relationship.

[0077] 4. Based on the plans and orders in the high-temperature ham sausage production workshop, the production plan information is used to model the production orders. This production model includes information such as the quantity of production orders, the matching relationship between order products, the order duration, the matching relationship between order dates, and the relationship between order inventory.

[0078] 5. Model the production process based on the production process information of each product in the production workshop. The model mainly includes information such as product production process route, process parameters, process constraints, product material consumption capacity, and production steps.

[0079] Based on production constraints such as order product process constraints, material inventory constraints, product inventory constraints, equipment production capacity constraints, product equipment process constraints, product on-site storage time constraints, and equipment relationship constraints in each production model, a subset of production scheduling rules for each process is generated, and a production scheduling rule execution queue is formed according to the priority of the scheduling rules. Each process dynamically selects production scheduling rules from the production scheduling rule execution queue to guide the production process based on the production execution status. When situations arise in the production process of a process, such as order completion, order insertion, order splitting, order cancellation, key equipment failure, insufficient material inventory, large differences in capacity between processes, or no remaining product inventory capacity, intelligent algorithms are used to modify the parameters in the constraints of each process based on comprehensive production evaluation indicators, generating new production constraints, and thus creating a new production scheduling rule execution queue to guide the on-site production process.

[0080] Specific implementation examples:

[0081] (1) Model the ham sausage products and materials. Taking the three specifications of ham sausage products, namely A, B and C, as an example, each ham sausage contains two materials: casing and aluminum buckle.

[0082] (2) Process modeling for ham sausages of various specifications. The ham sausage production process mainly includes three steps: tying and filling, high-temperature sterilization, and product packaging.

[0083] (3) Model the physical parameters of the production equipment units in the production workshop. The high-temperature production workshop for ham sausage mainly includes the ham sausage tying and filling production unit, the ham sausage raw product racking unit, the ham sausage raw product buffering unit, the ham sausage sterilizer production unit, the ham sausage cooked product buffering unit, the ham sausage cooked product turning unit, the ham sausage packaging line unit, and the inter-process ham sausage transfer unit.

[0084] (4) Model the production orders, break them down into daily orders based on weekly orders, obtain the processing time and sequential relationship of different specifications of products in each production unit, and determine the completion time of the daily plan.

[0085] (5) Based on the production constraints for each process in the production model established in (1), (2), (3), and (4), establish production scheduling rules for different processes. Each process dynamically selects the production scheduling rules from its respective subset of scheduling rules to guide the production process. The production scheduling rules and their priorities for each process are as follows: Figure 3 As shown.

[0086] 1) In the tying and filling process, there are constraints on the upper limit of the storage time of raw ham sausages during the tying and filling process, the upper limit of the time for raw ham sausages to be put into the pot, the mapping relationship between tying and sterilization equipment, and the constraint of preventing blockage of the raw ham sausage grate unit. Therefore, the following rules are formulated: empty grate priority scheduling rule, full grate priority scheduling rule, full grate priority to pot rule, full grate priority to ham sausage buffer unit rule, full grate priority to pot rule, overdue full grate early pot rule, and full grate early pot rule at the end of the order.

[0087] 2) The sterilization process is subject to constraints such as premature shut-off of raw ham sausages that have exceeded their time limit, premature shut-off at the end of an order, empty sterilizers, and compatibility between the sterilizer's sterilization process and the product. Therefore, the following scheduling rules were established: premature shut-off of raw ham sausages that have exceeded their time limit, premature shut-off at the end of an order, priority scheduling of empty sterilizers, and prohibition of product entry into the sterilizer if the sterilizer's process and product are incompatible.

[0088] 3) The packaging process involves constraints such as prohibiting different products from being mixed into the ham sausage packaging line unit, fully utilizing the cooked ham sausage buffer unit, and the mapping relationship between sterilization and packaging equipment. Therefore, scheduling rules were developed to prohibit different products from being mixed into the packaging line production units, prioritize full ham sausages to the cooked ham sausage turning unit, prioritize full ham sausages to the cooked ham sausage buffer unit, and prioritize empty ham sausages.

[0089] When the production scheduling rule redefinition condition is triggered on the production floor, an intelligent algorithm is used to modify the parameters in the constraints of each process to generate new production constraints. The scheduling rules within the subset are regenerated and prioritized. The new scheduling rules are then verified for applicability to the on-site production process using two comprehensive production evaluation indicators: process load balance and average equipment utilization. For example, if a sterilization machine in the sterilization process malfunctions and stops working, affecting production capacity and altering the mapping constraint between the ligation and sterilization equipment, the production scheduling rules in the corresponding ligation process are regenerated and prioritized according to the modified equipment mapping constraint. The rule prioritizing full combs for entering the ham sausage raw product buffer unit is given higher priority. The regenerated production scheduling rules and their priorities for each process are as follows: Figure 4 As shown, the shaded boxes represent the modified production constraints and adjusted production scheduling rules.

Claims

1. A product dynamic scheduling method based on multiple constraints, characterized in that, Includes the following steps: Step 1: Based on the production line tasks in the product manufacturing workshop, model the production unit, production product, production material, production order, and production process to form multiple production models. Establish a production constraint data table in the database and store the production constraints of each production model in the production constraint data table. Step 2: Obtain the data in the production constraint data table, determine the production scheduling rules for different processes, form a subset of scheduling rules for each process; and determine the priority of the scheduling rules within each process based on the frequency of occurrence of production application scenarios on the production line. Step 3: Sort the scheduling rules in the subset of scheduling rules for each process according to their priority to form the production scheduling rule execution queue for each process. Then, bind each production scheduling rule in the queue to its corresponding production application scenario to form the scheduling rule execution queue for the entire production line and save it. Step 4: Select the highest priority production scheduling rule from the scheduling rule execution queue that matches the current production application scenario of each process, and send it to each production workstation in the production workshop to guide on-site production; Step 5: When the production scheduling rule redefinition condition is received from the on-site production process, the production constraint conditions in the production constraint condition data table are updated, and the process returns to Step 2. Then, the comprehensive production evaluation index is used to determine whether the regenerated production scheduling rule can be applied to the on-site production process. Finally, the production scheduling rule execution queue formed by the production scheduling rule that can be applied to the on-site production replaces the original production scheduling rule queue, and the process returns to Step 4. The redefinition conditions for the production scheduling rules mainly include order completion, order insertion, order splitting, order cancellation, critical equipment failure, insufficient material inventory, large differences in capacity between processes, and no remaining capacity in product inventory; The process of updating the production constraints in the production constraint data table specifically involves using an electromagnetic-like algorithm to update the parameters in the production constraints, including the following steps: By setting the parameters in the constraints as charged particles in an electromagnetic-like algorithm, and setting the upper and lower limits of the parameter changes as the range of charged particle movement, the net force on each charged particle is obtained by simulating the mutual attraction and repulsion of charged particles. This is used to change the position of the charged particles, i.e., change the constraint parameter values. The production constraint data table is then updated based on the changed constraint parameter values.

2. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The production model is a production unit model, used to model the physical information of the equipment units in the production workshop based on the physical layout of the equipment units; the production unit model includes at least one of the following: equipment unit production capacity, equipment unit model, equipment unit location information, equipment unit association relationship, and equipment process matching relationship; The production model is a production product model, used to model the production products based on the production product information in the production workshop; the production product model includes at least one of the following: product type, product and production unit constraint matching relationship, and basic product information.

3. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The production model is a production material model, used to model production materials based on material information in the production workshop. The production material model includes at least one of the following: material type, basic material information, material and product constraint matching relationship, and material and production unit constraint matching relationship.

4. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The production model is a production order model, used to model production orders based on order information within the production workshop; The production order model includes at least one of the following: order quantity, order product matching relationship, order duration, order date matching relationship, and order inventory association relationship.

5. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The production model is a production process model, used to model the production process based on the production process information of each product in the production workshop; The production process model includes at least one of the following: product production process route, process parameters, process constraints, product material consumption capacity, and production steps.

6. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The production constraints include at least one of the following: order product process constraints, material inventory constraints, product inventory constraints, equipment production capacity constraints, product equipment process constraints, product on-site storage time constraints, and equipment relationship constraints, each characterized by parameters; the production constraints contained in each process are different from each other.

7. The product dynamic scheduling method based on multiple constraints according to claim 1, characterized in that, The process of determining whether the regenerated production scheduling rules can be applied to the on-site production process through comprehensive production evaluation indicators includes the following steps: Based on on-site production, obtain the process load balance and the average utilization rate of process equipment; If the process load balance and the average utilization rate of process equipment both exceed the set thresholds, it means that the regenerated production scheduling rules can be applied to the on-site production process. Otherwise, the regenerated production scheduling rules cannot be applied to the on-site production process, and the process returns to step 2.

8. The product dynamic scheduling method based on multiple constraints according to claim 7, characterized in that, The process load rate is: Where PF i TRW represents the process load factor of process i. i TT represents the actual production operation time of process i. i Indicates the production cycle time of process i; The average utilization rate of the equipment in the aforementioned process is: TPF i This represents the average utilization rate of the equipment in process i. This represents the effective processing time of all equipment in process i. This represents the sum of the time all equipment in process i is occupied.

9. A product dynamic scheduling system based on multiple constraints, characterized in that, include: The production model module is used to model production units, products, materials, orders, and processes based on the production line tasks in the production workshop, forming multiple production models. It also establishes a production constraint data table in the database and stores the production constraints of each production model in the production constraint data table. The production scheduling rules module is used to acquire data from the production constraint data table, determine production scheduling rules for different processes, and form a subset of scheduling rules for each process; determine the priority of scheduling rules within each process based on the frequency of occurrence of production application scenarios on the production line; sort the scheduling rules within each process's scheduling rule subset according to their priority to form a production scheduling rule execution queue for each process, and bind each scheduling rule in the queue to its corresponding production application scenario, ultimately forming and saving the scheduling rule execution queue for the entire production line; select the highest priority production scheduling rule from the scheduling rule execution queue that matches the current production application scenario of each process, and send it to each production workstation in the production workshop to guide on-site production; The production scheduling rule update module is used to update the production constraints in the production constraint data table when the production process on site triggers the redefinition of production scheduling rules, and then return to step 2. Then, it judges whether the regenerated production scheduling rules can be applied to the on-site production process through comprehensive production evaluation indicators. Finally, the production scheduling rule execution queue formed by the production scheduling rules that can be applied to the on-site production replaces the original production scheduling rule queue. The redefinition conditions for the production scheduling rules mainly include order completion, order insertion, order splitting, order cancellation, critical equipment failure, insufficient material inventory, large differences in capacity between processes, and no remaining capacity in product inventory; The update of production constraints in the production constraint data table specifically involves using an electromagnetic-like algorithm to update the parameters in the production constraints. By setting the parameters in the constraints as charged particles in an electromagnetic-like algorithm, and setting the upper and lower limits of the parameter changes as the range of charged particle movement, the net force on each charged particle is obtained by simulating the mutual attraction and repulsion of charged particles. This is used to change the position of the charged particles, i.e., change the constraint parameter values. The production constraint data table is then updated based on the changed constraint parameter values.

Images