Discrete manufacturing factory production scheduling strategy determination system and method

Abstract

The invention relates to a discrete manufacturing factory production scheduling strategy determination system and method, and the system comprises a data collection unit, an analysis processing unit, a scheduling strategy generation unit, a simulation strategy operation unit, an execution control unit, and an adjustment optimization unit which are in communication connection with a user interface unit. According to the method, an optimized scheduling strategy can be quickly generated, waiting time and shutdown time in the production process are reduced, and therefore the overall production efficiency is improved, R1, the future order quantity is analyzed and predicted through big data; r2, applying an artificial intelligence algorithm to generate a preliminary scheduling strategy plan list; r3, selecting a required scheduling strategy plan list; and R4, the progress is monitored in the production process, the production time table is automatically adjusted according to the actual situation, it is ensured that changes are responded in time, and the complexity of production scheduling is simplified by defining the operation process of each step, so that the production scheduling is more standardized.

Description

Technical Field

[0001] The present invention belongs to the technical field of factory production management, and in particular relates to a system and method for determining a production scheduling strategy for a discrete manufacturing factory. Background Art

[0002] In the current discrete manufacturing industry, production scheduling is a complex and critical process that directly affects production efficiency, cost control and customer satisfaction. Traditional production scheduling usually relies on manual experience, which often seems inadequate in the face of diverse product demands, changing market environments and complex supply chains. The following are some of the problems and challenges in existing technologies:

[0003] 1. Traditional production scheduling relies on manual decision-making, which is inefficient and error-prone when dealing with large amounts of data and complex production tasks;

[0004] 2. In many manufacturing companies, production data, equipment status, and order information are often scattered across different systems, lacking effective integration and real-time updates, leading to inaccurate scheduling decisions;

[0005] 3. Due to the rapid changes in market demand, production scheduling strategies need to be highly flexible and adaptable. However, traditional scheduling methods often have difficulty responding to these changes quickly;

[0006] 4. In the manual scheduling process, due to the lack of comprehensive data analysis and optimization tools, the utilization rate of equipment resources and human resources is often low, resulting in increased production costs;

[0007] 5. With the increasing popularity of customized production, rapid response to customer needs has become the key to corporate competition. However, traditional scheduling methods are difficult to meet the rapidly changing customer order needs;

[0008] Therefore, a discrete manufacturing plant production scheduling strategy determination system and method are needed to solve the above problems. Summary of the invention

[0009] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a discrete manufacturing plant production scheduling strategy determination system and method to solve the above problems.

[0010] The technical solution of the present invention is as follows:

[0011] A discrete manufacturing plant production scheduling strategy determination system includes a data acquisition unit, an analysis and processing unit, a scheduling strategy generation unit, a simulation strategy operation unit, an execution control unit, and an adjustment and optimization unit that are communicatively connected to a user interface unit;

[0012] Data collection unit, used to collect information on processing speed of processing equipment, production demand of parts, customer order priority and frequency of parts repair and replacement;

[0013] Analytical processing unit, used to analyze component processing speed differences, order demand, seasonal production needs, warehouse inventory and customer expected demand, and identify key components and high-priority orders;

[0014] The scheduling strategy generation unit is used to generate strategies for equipment allocation, production sequence, time planning, off-season and peak-season production adjustment, and order allocation based on the data provided by the data acquisition unit and the analysis unit;

[0015] Execution control unit, used to implement scheduling strategies, assign production tasks to processing equipment, monitor production execution, check processing progress daily, and report production progress;

[0016] The adjustment and optimization unit is used to adjust and optimize the scheduling strategy according to the actual production situation and feedback, so as to continuously update the new scheduling strategy;

[0017] The user interface unit is used to receive production scheduling instructions input by the user and display the scheduling strategy, production progress and equipment status in a graphical interface.

[0018] Furthermore, the data acquisition unit further comprises:

[0019] Real-time data acquisition interface, used to obtain production data from the production site and enterprise management system in real time, obtain the processing speed of each component processed by the equipment, the demand for each component in the current order, customer information and the replacement frequency of each component, set customer priority based on customer information, and obtain the replacement frequency of each component by repeating the number of different parts ordered by the customer each time;

[0020] The forecast data integration module combines market trends and customer historical order data to predict the order volume of different parts in each month or quarter.

[0021] Further, the analysis and processing unit includes:

[0022] Data processing module: cleans, organizes and formats the data from the data collection unit, summarizes similar data, and provides accurate data for subsequent difference analysis module, demand forecasting module and key identification module.

[0023] Difference analysis module: analyze the differences in processing speeds of different processing equipment and the differences in component production requirements;

[0024] Demand forecasting module: Combines market trends and customer historical order data to forecast production demand for each component in the future;

[0025] Priority identification module: Identify high-priority orders, formulate priority orders according to the customer priorities set in the real-time data acquisition interface, and provide a basis for the scheduling strategy generation unit.

[0026] Furthermore, the scheduling strategy generation unit further includes:

[0027] Production sequence planning module: Determine the order of production tasks and arrange production according to the order signing period when the order volume does not exceed the production capacity;

[0028] The order intelligent allocation module intelligently allocates the order sequence based on customer priority and order profit margin when the order volume exceeds the production capacity, rearranges the order production sequence and then feeds it back to the equipment allocation module for equipment allocation;

[0029] The off-season and peak-season intelligent analysis module predicts the off-season and peak-season cycles through historical data analysis and adjusts the production plan accordingly. During the off-season, you can prepare for the production of components for the peak season in advance and reserve the quantity of each component according to the production plan;

[0030] The equipment allocation module is equipped with a normal equipment allocation unit and an off-season intelligent equipment allocation unit, which respectively deal with the equipment allocation of normal processing parts and the equipment allocation of off-season processing parts. According to the processing speed of the parts, the processing demand and the urgency of the order, the specific number of processing equipment for each part is determined;

[0031] Determine the strategy unit: summarize the equipment allocation module, production sequence planning module, peak and off-season intelligent analysis module and order intelligent allocation module to formulate a scheduling strategy plan.

[0032] Further, the simulation strategy operation unit includes:

[0033] The time planning module uses operations research or machine learning algorithms to predict the order completion time in the scheduling strategy plan and develop a production schedule;

[0034] Strategy simulation module: before implementing the scheduling strategy, simulate the strategy and get the final result of the strategy;

[0035] Result evaluation module: compare the results obtained in the strategy simulation module with the planned completed orders, get the difference, evaluate the results of the simulation operation, and the evaluation results are divided into yes or no. When the difference is less than 5%, the result is yes, and when the difference is greater than 5%, the result is no. If the result is no, it is fed back to the scheduling strategy generation unit to generate a new scheduling strategy plan. If the result is yes, it is listed as a required scheduling strategy plan and fed back to the execution control unit to execute production.

[0036] Further, the execution control unit includes:

[0037] Task allocation module: allocates the production tasks on the scheduling strategy plan to specific processing equipment, generates a production schedule for each equipment, and produces parts according to the production schedule;

[0038] Production monitoring module: real-time monitoring of production execution and detection of production efficiency;

[0039] Progress check module: Check the processing progress daily and provide feedback to the adjustment and optimization unit based on the production schedule of the equipment.

[0040] Furthermore, the adjustment and optimization unit further comprises:

[0041] The production progress monitoring unit tracks the production progress in real time, compares the daily production schedule of each device and the daily production volume of each device, compares the difference values, and feeds back to the strategy automatic adjustment unit;

[0042] The strategy automatic adjustment unit automatically adjusts equipment allocation and production sequence based on monitoring results, and generates a new production schedule the next day and feeds it back to each production equipment.

[0043] Furthermore, it also includes a network communication interface for receiving remote customer order information.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. Through automated data collection and analysis, the present invention can quickly generate optimized scheduling strategies, reduce waiting time and downtime in the production process, and thus improve overall production efficiency.

[0046] 2. Through precise resource allocation and efficient equipment utilization, the system helps reduce waste and lower production costs.

[0047] 3. Improve customer satisfaction by responding to customer orders in a timely manner, ensuring delivery time, and improving product quality.

[0048] A method for determining a production scheduling strategy for a discrete manufacturing plant, comprising:

[0049] R1: The data acquisition unit and network communication interface collect order details from customers, equipment production status and market trends in real time, and use big data analysis to predict future order volumes;

[0050] R2: Combine the factory's current equipment production speed, inventory and order priority, and apply artificial intelligence algorithms to generate a preliminary scheduling strategy plan;

[0051] R3: By simulating different production scenarios, evaluate the impact of each plan on delivery time and resource utilization, and select the required scheduling strategy plan;

[0052] R4: Monitor progress during the production process and automatically adjust the production schedule based on actual conditions to ensure timely response to changes.

[0053] Furthermore, it also includes the rational allocation of production tasks by adjusting the optimization units during the off-season to prepare for the peak season order demand, and predicting the peak season order volume through the forecast data integration module.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The present invention simplifies the complexity of production scheduling by clarifying the operation flow of each step, making it more standardized.

[0056] 2. The scheduling method based on data analysis of the present invention can provide more accurate information support and help decision makers make higher quality scheduling decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a module diagram of the invented system. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] like Figure 1 As shown, a system and method for determining a production scheduling strategy for a discrete manufacturing plant includes a data acquisition unit, an analysis and processing unit, a scheduling strategy generation unit, a simulation strategy operation unit, an execution control unit, and an adjustment and optimization unit that are communicatively connected to a user interface unit;

[0060] Data collection unit, used to collect information on processing speed of processing equipment, production demand of parts, customer order priority and frequency of parts repair and replacement;

[0061] Analytical processing unit, used to analyze component processing speed differences, order demand, seasonal production needs, warehouse inventory and customer expected demand, and identify key components and high-priority orders;

[0062] The scheduling strategy generation unit is used to generate strategies for equipment allocation, production sequence, time planning, off-season and peak-season production adjustment, and order allocation based on the data provided by the data acquisition unit and the analysis unit;

[0063] Execution control unit, used to implement scheduling strategies, assign production tasks to processing equipment, monitor production execution, check processing progress daily, and report production progress;

[0064] The adjustment and optimization unit is used to adjust and optimize the scheduling strategy according to the actual production situation and feedback, so as to continuously update the new scheduling strategy;

[0065] A user interface unit, used to receive production scheduling instructions input by users, and display scheduling strategies, production progress and equipment status in a graphical interface;

[0066] In this embodiment, the data acquisition unit further includes:

[0067] Real-time data acquisition interface, used to obtain production data from the production site and enterprise management system in real time, obtain the processing speed of each component processed by the equipment, the demand for each component in the current order, customer information and the replacement frequency of each component, set customer priority based on customer information, and obtain the replacement frequency of each component by repeating the number of different parts ordered by the customer each time;

[0068] The forecast data integration module combines market trends and customer historical order data to predict the order volume of different parts in each month or quarter.

[0069] In this embodiment, the analysis and processing unit includes:

[0070] Data processing module: cleans, organizes and formats the data of the data collection unit, summarizes the same type of data, and provides accurate data for the subsequent difference analysis module, demand forecasting module and key identification module;

[0071] Difference analysis module: analyze the differences in processing speeds of different processing equipment and the differences in component production requirements;

[0072] Demand forecasting module: Combines market trends and customer historical order data to forecast production demand for each component in the future;

[0073] Priority identification module: Identify high-priority orders, formulate priority orders according to the customer priorities set in the real-time data acquisition interface, and provide a basis for the scheduling strategy generation unit.

[0074] In this embodiment, the scheduling strategy generation unit further includes:

[0075] Production sequence planning module: Determine the order of production tasks and arrange production according to the order signing period when the order volume does not exceed the production capacity;

[0076] The order intelligent allocation module intelligently allocates the order sequence based on customer priority and order profit margin when the order volume exceeds the production capacity, rearranges the order production sequence and then feeds it back to the equipment allocation module for equipment allocation;

[0077] The off-season and peak-season intelligent analysis module predicts the off-season and peak-season cycles through historical data analysis and adjusts the production plan accordingly. During the off-season, you can prepare for the production of components for the peak season in advance and reserve the quantity of each component according to the production plan;

[0078] The equipment allocation module is equipped with a normal equipment allocation unit and an off-season intelligent equipment allocation unit, which respectively deal with the equipment allocation of normal processing parts and the equipment allocation of off-season processing parts. According to the processing speed of the parts, the processing demand and the urgency of the order, the specific number of processing equipment for each part is determined;

[0079] Determine the strategy unit: summarize the equipment allocation module, production sequence planning module, peak and off-season intelligent analysis module and order intelligent allocation module to formulate a scheduling strategy plan.

[0080] In this embodiment, the simulation strategy running unit includes:

[0081] The time planning module uses operations research or machine learning algorithms to predict the order completion time in the scheduling strategy plan and develop a production schedule;

[0082] Strategy simulation module: before implementing the scheduling strategy, simulate the strategy and get the final result of the strategy;

[0083] Result evaluation module: compare the results obtained in the strategy simulation module with the planned completed orders, get the difference, evaluate the results of the simulation operation, and the evaluation results are divided into yes or no. When the difference is less than 5%, the result is yes, and when the difference is greater than 5%, the result is no. If the result is no, it is fed back to the scheduling strategy generation unit to generate a new scheduling strategy plan. If the result is yes, it is listed as a required scheduling strategy plan and fed back to the execution control unit to execute production.

[0084] In this embodiment, the execution control unit includes:

[0085] Task allocation module: allocates the production tasks on the scheduling strategy plan to specific processing equipment, generates a production schedule for each equipment, and produces parts according to the production schedule;

[0086] Production monitoring module: real-time monitoring of production execution and detection of production efficiency;

[0087] Progress check module: Check the processing progress daily and provide feedback to the adjustment and optimization unit based on the production schedule of the equipment.

[0088] In this embodiment, the adjustment and optimization unit further includes:

[0089] The production progress monitoring unit tracks the production progress in real time, compares the daily production schedule of each device and the daily production volume of each device, compares the difference values, and feeds back to the strategy automatic adjustment unit;

[0090] The strategy automatic adjustment unit automatically adjusts the equipment allocation and production sequence according to the monitoring results, and generates a new production schedule the next day and feeds it back to each production equipment;

[0091] Preferably, it also includes a network communication interface for receiving remote customer order information;

[0092] First, collect data such as the processing speed of processing equipment, production demand for parts, customer order priority, and frequency of parts repair and replacement from the production site and enterprise management system. Then, combine customer order data from previous years and market trends to predict the order volume for replenishment parts in the next month or quarter.

[0093] By cleaning the obtained data, more accurate data can be obtained;

[0094] By comparing the differences in processing speed between each component and the differences in order volume of each component, we analyze the production equipment that needs to be allocated to each component. Through the demand forecasting module, we predict the demand for each component in the future, ranging from one week to half a month. Through the recent order volume, historical order data and market demand, we can more accurately predict the order volume in the short term in the future, so as to provide more accurate data for subsequent generation strategies. During this period, we need to mark the company's old customers and customers with large order volumes as priority customers.

[0095] When the subsequent order volume cannot be completed on time, the orders of priority customers are processed first to retain high-quality customers. Among them, non-priority customers are evaluated to analyze which customers can bring the greatest benefits to the company and reduce the company's losses, and the orders of non-priority customers are selected for completion;

[0096] When predicting that orders can be fully completed in the near future, different quantities of equipment are deployed to meet the production of each component based on the recent order volume of each component. The production plan needs to be formulated in combination with the processing speed of the component, the processing demand, and the urgency of the order to avoid unfinished production when customer orders are overdue. On the basis of ensuring that orders are completed, additional components are produced and stockpiled based on the later predicted order volume;

[0097] During the off-season, some production equipment does not need to be turned on, but by predicting the demand in the next quarter or the historical peak season, various parts can be stockpiled in advance to reduce the number of customer orders that cannot be completed during the peak season, reduce the company's losses, and maximize the guarantee that all customer orders can be met during the peak season;

[0098] After determining the above situations, a scheduling strategy plan is formulated and a production simulation is performed. The feasibility of the scheduling strategy plan is obtained through simulation. If the (completed order quantity - actual order quantity) / actual order quantity in the scheduling strategy plan obtained by simulation is less than 5%, it is listed as the required scheduling strategy plan. If the (completed order quantity - actual order quantity) / actual order quantity in the scheduling strategy plan obtained by simulation is greater than 5%, a new scheduling strategy plan is generated by recombining the data. If the scheduling strategy plan generated ten times does not meet the requirements, the scheduling strategy plan with the maximum production volume is used as the required scheduling strategy plan.

[0099] Production is carried out after the required scheduling strategy plan sheet is completed. The required scheduling strategy plan sheet records the number of parts that each device needs to produce every day, which is called the production schedule of the device. Data on the daily output of each device is collected and fed back to the adjustment and optimization unit to monitor the daily production efficiency and check the progress of daily production;

[0100] The production efficiency is calculated by comparing the differences between each device and all devices on a daily basis and the production schedule. If the production efficiency exceeds the plan, it will be maintained. If it is lower than the plan, the reasons will be analyzed and the production schedule of the equipment will be readjusted to complete the order.

[0101] A method for determining a production scheduling strategy for a discrete manufacturing plant, comprising:

[0102] R1: The data acquisition unit and network communication interface collect order details from customers, equipment production status and market trends in real time, and use big data analysis to predict future order volumes;

[0103] R2: Combine the factory's current equipment production speed, inventory and order priority, and apply artificial intelligence algorithms to generate a preliminary scheduling strategy plan;

[0104] R3: By simulating different production scenarios, evaluate the impact of each plan on delivery time and resource utilization, and select the required scheduling strategy plan;

[0105] R4: Monitor progress during the production process and automatically adjust the production schedule based on actual conditions to ensure timely response to changes.

[0106] Through the above methods, the production scheduling of discrete manufacturing factories can be made intelligent, efficient and precise.

[0107] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A discrete manufacturing plant production scheduling strategy determination system, characterized by: It includes a data acquisition unit, an analysis and processing unit, a scheduling strategy generation unit, a simulation strategy operation unit, an execution control unit and an adjustment and optimization unit which are communicatively connected with the user interface unit; Data collection unit, used to collect information on processing speed of processing equipment, production demand of parts, customer order priority and frequency of parts repair and replacement; Analytical processing unit, used to analyze component processing speed differences, order demand, seasonal production needs, warehouse inventory and customer expected demand, and identify key components and high-priority orders; The scheduling strategy generation unit is used to generate strategies for equipment allocation, production sequence, time planning, off-season and peak-season production adjustment, and order allocation based on the data provided by the data acquisition unit and the analysis unit; Execution control unit, used to implement scheduling strategies, assign production tasks to processing equipment, monitor production execution, check processing progress daily, and report production progress; The adjustment and optimization unit is used to adjust and optimize the scheduling strategy according to the actual production situation and feedback, so as to continuously update the new scheduling strategy; The user interface unit is used to receive production scheduling instructions input by the user and display the scheduling strategy, production progress and equipment status in a graphical interface.

2. A discrete manufacturing plant production scheduling strategy determination system according to claim 1, characterized in that: The data acquisition unit further comprises: Real-time data acquisition interface, used to obtain production data from the production site and enterprise management system in real time, obtain the processing speed of each component processed by the equipment, the demand for each component in the current order, customer information and the replacement frequency of each component, set customer priority based on customer information, and obtain the replacement frequency of each component by repeating the number of different parts ordered by the customer each time; The forecast data integration module combines market trends and customer historical order data to predict the order volume of different parts in each month or quarter.

3. A discrete manufacturing plant production scheduling strategy determination system according to claim 2, characterized in that: The analysis and processing unit comprises: Data processing module: cleans, organizes and formats the data of the data collection unit, summarizes the same type of data, and provides accurate data for the subsequent difference analysis module, demand forecasting module and key identification module; Difference analysis module: analyze the differences in processing speeds of different processing equipment and the differences in component production requirements; Demand forecasting module: Combines market trends and customer historical order data to forecast production demand for each component in the future; Priority identification module: Identify high-priority orders, formulate priority orders according to the customer priorities set in the real-time data acquisition interface, and provide a basis for the scheduling strategy generation unit.

4. A discrete manufacturing plant production scheduling strategy determination system according to claim 3, characterized in that: The scheduling strategy generation unit further includes: Production sequence planning module: Determine the order of production tasks and arrange production according to the order signing period when the order volume does not exceed the production capacity; The order intelligent allocation module intelligently allocates the order sequence based on customer priority and order profit margin when the order volume exceeds the production capacity, rearranges the order production sequence and then feeds it back to the equipment allocation module for equipment allocation; The off-season and peak-season intelligent analysis module predicts the off-season and peak-season cycles through historical data analysis and adjusts the production plan accordingly. During the off-season, you can prepare for the production of components for the peak season in advance and reserve the quantity of each component according to the production plan; The equipment allocation module is equipped with a normal equipment allocation unit and an off-season intelligent equipment allocation unit, which respectively deal with the equipment allocation of normal processing parts and the equipment allocation of off-season processing parts. According to the processing speed of the parts, the processing demand and the urgency of the order, the specific number of processing equipment for each part is determined; Determine the strategy unit: summarize the equipment allocation module, production sequence planning module, peak and off-season intelligent analysis module and order intelligent allocation module to formulate a scheduling strategy plan.

5. A discrete manufacturing plant production scheduling strategy determination system according to claim 4, characterized in that: The simulation strategy operation unit comprises: The time planning module uses operations research or machine learning algorithms to predict the order completion time in the scheduling strategy plan and develop a production schedule; Strategy simulation module: before implementing the scheduling strategy, simulate the strategy and get the final result of the strategy; Result evaluation module: compare the results obtained in the strategy simulation module with the planned completed orders, get the difference, evaluate the results of the simulation operation, and the evaluation results are divided into yes or no. When the difference is less than 5%, the result is yes, and when the difference is greater than 5%, the result is no. If the result is no, it is fed back to the scheduling strategy generation unit to generate a new scheduling strategy plan. If the result is yes, it is listed as a required scheduling strategy plan and fed back to the execution control unit to execute production.

6. A discrete manufacturing plant production scheduling strategy determination system according to claim 5, characterized in that: The execution control unit comprises: Task allocation module: allocates the production tasks on the scheduling strategy plan to specific processing equipment, generates a production schedule for each equipment, and produces parts according to the production schedule; Production monitoring module: real-time monitoring of production execution and detection of production efficiency; Progress check module: Check the processing progress daily and provide feedback to the adjustment and optimization unit based on the production schedule of the equipment.

7. A discrete manufacturing plant production scheduling strategy determination system according to claim 6, characterized in that: The adjustment and optimization unit further comprises: The production progress monitoring unit tracks the production progress in real time, compares the daily production schedule of each device and the daily production volume of each device, compares the difference values, and feeds back to the strategy automatic adjustment unit; The strategy automatic adjustment unit automatically adjusts equipment allocation and production sequence based on monitoring results, and generates a new production schedule the next day and feeds it back to each production equipment.

8. The discrete manufacturing plant production scheduling strategy determination system according to claim 1, characterized in that: It also includes a network communication interface for receiving remote customer order information.

9. A method for determining a production scheduling strategy for a discrete manufacturing plant according to any one of claims 1 to 8, characterized in that: include R1: The data acquisition unit and network communication interface collect order details from customers, equipment production status and market trends in real time, and use big data analysis to predict future order volumes; R2: Combine the factory's current equipment production speed, inventory and order priority, and apply artificial intelligence algorithms to generate a preliminary scheduling strategy plan; R3: By simulating different production scenarios, evaluate the impact of each plan on delivery time and resource utilization, and select the required scheduling strategy plan; R4: Monitor progress during the production process and automatically adjust the production schedule based on actual conditions to ensure timely response to changes.

10. The method for determining a production scheduling strategy for a discrete manufacturing plant according to claim 9, characterized in that: It also includes the rational allocation of production tasks by adjusting and optimizing units during the off-season to prepare for peak season order demand, and predicting peak season order volume through the forecasting data integration module.

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