Scheduling method of flexible processing production line

Abstract

The invention provides a production scheduling method for a flexible processing production line. The production scheduling method comprises the following steps: constructing a multi-target interference management scheduling model; the multi-target interference management scheduling model comprises a constraint condition and a target function which are used for solving the priority ranking of the to-be-processed parts; the production scheduling data are input into the multi-target interference management scheduling model, and the priority order of the to-be-machined parts is obtained through an optimization algorithm; the production scheduling data at least comprises a to-be-machined part set and time parameters of to-be-machined parts; and generating a production scheduling plan of the to-be-processed parts by using the priority ranking of the to-be-processed parts. According to the invention, by establishing a multi-target interference management scheduling model, a plurality of targets such as production efficiency, resource utilization rate and production cycle are comprehensively considered; dynamic production scheduling and scheduling are carried out by using a hybrid particle swarm optimization algorithm, so that the flexibility and the response speed of the flexible machining production line are remarkably improved, and various interferences and changes occurring in the production process can be effectively coped with.

Description

Technical Field

[0001] The present disclosure relates to the technical field of production management, and more specifically to a production scheduling method for a flexible processing production line. Background Art

[0002] In modern manufacturing, with the diversification and personalization of market demand, flexible processing production lines have been widely used as a production model that can quickly respond to market changes and improve production efficiency and resource utilization. The flexible processing production line has high flexibility and can quickly switch between different products to meet the production needs of multiple varieties and small batches. However, traditional production scheduling and scheduling methods often find it difficult to cope with the complexity and dynamics of flexible processing production lines, resulting in problems such as inefficient production efficiency, waste of resources and delayed delivery time.

[0003] Especially in the prefabricated machine room of energy stations in the construction industry, due to the characteristics of variable production tasks, high equipment utilization requirements, and tight construction schedules, there are higher requirements for the accuracy and real-time performance of production scheduling. Existing production scheduling methods often lack comprehensive management of interference factors under multi-objective and multi-constraint conditions, making it difficult to achieve efficient production scheduling in a complex production environment. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a production scheduling method for a flexible processing production line based on a multi-objective interference management scheduling model and a hybrid particle swarm optimization algorithm.

[0005] The present disclosure provides a production scheduling method for flexible processing production lines, including: building a multi-objective interference management scheduling model; a multi-objective interference management scheduling model includes constraints and objective functions for solving the priority sorting of components to be processed; input the production scheduling data into the multi-objective interference management scheduling model, and obtain the priority sorting of components to be processed using the optimization algorithm; the production scheduling data includes at least the set of components to be processed and the time parameters of components to be processed; and use the priority sorting of components to be processed to generate the production scheduling plan of components to be processed.

[0006] According to an embodiment of the present disclosure, a multi-objective interference management scheduling model is constructed, including: defining the decision variable of the multi-objective interference management scheduling model as the processing weight of the components to be processed; the processing weight is used to characterize the priority of the components to be processed; building constraints of the multi-objective interference management scheduling model according to the operation of the flexible processing production line; the operation includes machine disturbance, continuous processing and first-come-first processing of the components to be processed; and constructing the objective function of the multi-objective interference management scheduling model based on the completion time and processing lag time of the components to be processed.

[0007] According to an embodiment of the present disclosure, the constraints of the multi-objective interference management scheduling model are constructed, including: building a first constraint condition, the first constraint condition is that the components cannot be arranged to be processed within the time window of machine disturbance; building a second constraint condition, the second constraint condition is that the same machine can only start processing the next component after the previous component is processed; building a third constraint condition, the third constraint condition is that the components on the machine with the idle state are first processed.

[0008] According to an embodiment of the present disclosure, constructing an objective function of a multi-objective interference management scheduling model includes: constructing a first objective function according to the completion time of the parts to be processed; constructing a second objective function according to the processing lag time of the parts to be processed;

[0009] Among them, the expression of the first objective function is as follows:

[0010]

[0011] In the formula, ω j is the processing weight of the jth component; C ij is the completion time of the jth component in process i;

[0012] Among them, the expression of the second objective function is as follows:

[0013]

[0014] In the formula, ω j is the processing weight of the jth component; T j is the processing lag time of the jth component.

[0015] According to an embodiment of the present disclosure, constructing a second objective function according to the processing lag time of the component to be processed includes: calculating the processing lag time according to the completion time of the component to be processed;

[0016] The calculation formula of processing lag time is as follows:

[0017]

[0018] In the formula, C j The actual completion time after all processes are completed for the jth component; The predicted completion time of the j-th component after completing all processes; the predicted completion time is obtained by optimizing the first objective function and then obtaining priority ranking.

[0019] According to an embodiment of the present disclosure, the production scheduling data is input into a multi-objective interference management scheduling model, and an optimization algorithm is used to obtain a priority ranking of parts to be processed, including: obtaining a first processing weight according to a first objective function and an optimization algorithm; using the first processing weight to calculate the processing lag time for obtaining a second processing weight according to a second objective function and an optimization algorithm; wherein the priority of the parts is positively correlated with the second processing weight.

[0020] According to an embodiment of the present disclosure, a production schedule for the parts to be processed is generated by utilizing the priority ranking of the parts to be processed, including: determining whether there is currently a shortage of materials and / or faulty equipment; if so, lowering the priority of the affected parts and generating a production schedule for the parts to be processed based on the priority ranking; if not, generating a production schedule for the parts to be processed based on the priority ranking.

[0021] The second aspect of the present disclosure provides a production scheduling device for a flexible processing production line, which can be used to implement the above method, including: a model module, which constructs a multi-objective interference management scheduling model; the multi-objective interference management scheduling model includes constraints and objective functions for solving the priority ranking of parts to be processed; an optimization module, which is used to input scheduling data into the multi-objective interference management scheduling model, and use an optimization algorithm to obtain the priority ranking of parts to be processed; the scheduling data at least includes a set of parts to be processed and time parameters of the parts to be processed; the scheduling module is used to generate a production scheduling plan for parts to be processed using the priority ranking of parts to be processed.

[0022] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the production scheduling method of the above-mentioned flexible processing production line.

[0023] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the production scheduling method for the above-mentioned flexible processing production line.

[0024] According to the production scheduling method for a flexible processing production line provided by the present invention, by collecting and reading production scheduling related information including production orders, equipment status, worker allocation and material supply, a multi-objective interference management scheduling model is established, which comprehensively considers multiple objectives such as production efficiency, resource utilization and production cycle; a hybrid particle swarm optimization algorithm is used, which combines the particle swarm optimization algorithm with other optimization technologies to perform dynamic production scheduling and dispatching. Since the production scheduling and dispatching scheme is continuously improved through iterative optimization, it at least partially solves the technical problem that traditional production scheduling and dispatching methods are often difficult to cope with the complexity and dynamics of flexible processing production lines, and realizes the technical effect of obtaining real-time data from the production management system, performing calculations and analysis based on the collected information, and generating and optimizing the dispatching scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flowchart schematically shows a production scheduling method for a flexible processing production line according to an embodiment of the present disclosure;

[0026] Figure 2 The structural block diagram of the production scheduling device of the flexible processing production line according to the embodiment of the present disclosure is schematically shown;

[0027] Figure 3 A block diagram of an electronic device suitable for implementing a production scheduling method for a flexible processing production line according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0030] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0031] It should be noted that the production scheduling method for a flexible processing production line disclosed in the present invention can be used for flexible processing production lines, and can also be used for production scheduling of production lines in the field of intelligent manufacturing, and the application field of the present invention is not limited.

[0032] The prefabricated machine room of energy station in the construction industry has the following special production requirements: Diverse production tasks: Prefabricated machine rooms involve the manufacture and assembly of multiple components, and the production tasks are changeable and complex; High equipment utilization: Equipment is expensive and needs to be used efficiently to avoid idleness and waste; Strict deadline requirements: Construction projects usually have strict deadline requirements, and the production of prefabricated machine rooms needs to be efficient and on time without delays; High quality standards: The components of prefabricated machine rooms must meet high quality standards, and the production process needs to be precisely controlled.

[0033] Traditional production scheduling methods are often based on static, single-objective optimization models, lacking comprehensive consideration of the complexity and dynamics of flexible production environments. Specific deficiencies include: Poor adaptability: Traditional methods are difficult to cope with real-time changes in production tasks, equipment status, material supply and other factors. Inefficiency: In a multi-variety, small-batch production environment, traditional methods are difficult to quickly switch products and optimize scheduling, resulting in low production efficiency. Waste of resources: The lack of dynamic management and optimization of resources leads to low equipment utilization and waste of resources. Slow response: In the face of emergencies and production disruptions, traditional methods are difficult to adjust in a timely manner, resulting in delayed delivery.

[0034] Therefore, the present invention provides a production scheduling method for a flexible processing production line, which covers multiple interdisciplinary subjects such as production automation, computer integrated manufacturing systems, operations research optimization algorithms, and industrial engineering. It aims to improve the adaptability, flexibility, and efficiency of the production line through advanced algorithms and system design to cope with the multi-variety, small-batch, and rapidly changing production needs in the modern manufacturing industry.

[0035] Figure 1 The flowchart of the production scheduling method of the flexible processing production line according to the embodiment of the present disclosure is schematically shown. Figure 1 As shown, an embodiment of the present disclosure provides a production scheduling method for a flexible processing production line, including: constructing a multi-objective interference management scheduling model; the multi-objective interference management scheduling model includes constraints and objective functions for solving the priority ranking of parts to be processed; inputting production scheduling data into the multi-objective interference management scheduling model, and using an optimization algorithm to obtain the priority ranking of parts to be processed; the production scheduling data at least includes a set of parts to be processed and time parameters of the parts to be processed; and using the priority ranking of parts to be processed to generate a production scheduling plan for the parts to be processed.

[0036] Through the embodiments of the present disclosure, the present disclosure realizes efficient scheduling of flexible production lines in dynamic environments by establishing a multi-objective interference management scheduling model. It can generate optimized production scheduling plans by real-time collection and analysis of production data, significantly improve the flexibility and response speed of the production line, and meet the special production needs of prefabricated machine rooms in energy stations in the construction industry.

[0037] It should be noted that due to the flexible production line's demand for small-batch production of multiple varieties, the machines required for processing various components in a certain process may overlap. Therefore, how to arrange the components to minimize the processing time is crucial to the production efficiency of the flexible production line.

[0038] On the basis of the above embodiments, before constructing the multi-objective interference management scheduling model, it also includes: collecting production scheduling data, specifically including: collecting production order data, such as order information (including order number, customer information, product specifications, quantity, delivery date), priority (some orders may have a higher priority and need to be prioritized for scheduling and dispatching), order status (tracking the progress of orders, such as received, in production, and completed); collecting equipment data, such as equipment utilization (real-time monitoring of equipment operation status and utilization, recording equipment start-up and downtime), maintenance information (recording equipment maintenance and maintenance plans, estimating equipment failure rate), production capacity (including equipment production capacity, processing speed, and available Collect data on workers, such as worker skills (recording workers’ skill levels and processes they are good at in order to allocate tasks reasonably), worker scheduling (tracking workers’ scheduling information, including working hours and rest time), and labor efficiency (monitoring workers’ work efficiency and production performance, and adjusting worker allocation plans); collect material data, such as inventory information (updating inventory data of raw materials and semi-finished products in real time to ensure the continuity of the material supply chain), procurement and logistics (recording material procurement plans, transportation status and arrival times, and estimating the reliability of material supply), and material demand forecasting (based on production plans and historical data, estimating future material demand and making procurement arrangements in advance).

[0039] In this embodiment, for production order data: obtain order number, customer information, product specifications, quantity, delivery date and other data through the enterprise resource planning (ERP) system or customer order management system; mark and manage order priorities to ensure that high-priority orders can be scheduled in time. For equipment status data: monitor the operating status, utilization, fault information and maintenance plan of the equipment in real time through Industrial Internet of Things (IIoT) devices, sensors and manufacturing execution systems (MES); record the start and downtime of the equipment, and store historical data for analysis and prediction. For worker allocation data: obtain worker skill level, scheduling information and work efficiency data through the human resources management system (HRMS); track the attendance of workers and update the status of workers in real time. For material data: update the inventory data of raw materials and semi-finished products in real time through the warehouse management system (WMS) to ensure the continuity of the material supply chain; obtain the procurement plan, transportation status and arrival time of materials, and estimate the reliability of material supply.

[0040] It should be noted that after collecting data, it is necessary to further clean, deduplicate and format the collected data to ensure the accuracy and consistency of the data. The processed data is stored in the database to facilitate the subsequent scheduling model and optimization algorithm.

[0041] Through the embodiments of the present disclosure, production data is collected and analyzed in real time, and the scheduling model is continuously updated using the real-time data provided by the information collection module to ensure the accuracy and real-time performance of the model, thereby dynamically adjusting the production scheduling plan to adapt to changes in the production environment.

[0042] On the basis of the above embodiments, a multi-objective interference management and scheduling model is constructed, including: defining the decision variable of the multi-objective interference management and scheduling model as the processing weight of the parts to be processed; the processing weight is used to characterize the priority of the parts to be processed; according to the operation status of the flexible processing production line, the constraint conditions of the multi-objective interference management and scheduling model are constructed; the operation status includes machine disturbance, continuous processing and first-come-first-served processing of parts; according to the completion time and processing lag time of the parts to be processed, the objective function of the multi-objective interference management and scheduling model is constructed.

[0043] In this embodiment, the present disclosure uses a multi-objective optimization method and a Pareto optimal solution set method to balance the conflicts among multiple objectives and find the optimal solution set.

[0044] The following are the parameter definitions of the multi-objective interference management scheduling model: Parts set j: the set of parts to be processed, n>1, n is the number of parts contained in the parts set; processing weight ω j : processing weight of component j; processing procedure i: total number of procedures, L≥2, L is the number of processing procedures; parallel same speed machines Li : Parallel machines with the same speed for process i, at least one process i satisfies L i >1; Processing time P ij : Processing time of component j in process i; start time S ijk : The start time of component j on the kth machine in process i; the completion time C ijk : The completion time of component j on the kth machine of process i; Completion time π: The final completion time of component j on the entire production line; Scheduling process schedule π': represents a feasible scheduling process schedule. In addition, it also includes the parameter definitions of interference management: machine disturbance (occurring at time t1, causing machine M to be occupied from t1 to t2) and processing lag time.

[0045] It should be noted that the time parameters, such as parameter definitions, may optionally include processing time, completion time, and start time. The multi-objective interference management scheduling model also includes correcting model parameters and optimizing strategies through a production process feedback mechanism to improve the model's predictive ability and adaptability.

[0046] Through the embodiments of the present disclosure, a multi-objective interference management scheduling model is used to quickly generate an optimal scheduling plan under multi-objective and multi-constraint conditions by integrating multiple factors (such as production efficiency, resource utilization, and production cycle), thereby improving production efficiency, increasing the utilization of equipment and workers, and reducing idle time. Specifically, historical production data is used for statistical analysis to identify key bottlenecks and influencing factors in the production process; model parameters such as equipment capacity, worker efficiency, material supply cycle, etc. are set based on the analysis results; optimization goals are determined: optimization goals such as maximizing production efficiency, optimizing resource utilization, minimizing costs, and punctual delivery and quality control are defined; and constraints on equipment and workers are defined based on the production environment and process flow.

[0047] On the basis of the above embodiment, the constraints of the multi-objective interference management scheduling model are constructed, including: constructing a first constraint, the first constraint is that parts cannot be arranged for processing within the time window of machine disturbance; constructing a second constraint, the second constraint is that the same machine can only start processing the next part after the previous part is processed; constructing a third constraint, the third constraint is that parts on idle machines are processed on a first-come, first-served basis.

[0048] In this embodiment, the constraint condition is specifically expressed as:

[0049] (1) Machine disturbance time window: Newly added parts cannot be processed within the machine disturbance time window.

[0050] (2) Continuous processing constraint: The same machine can only start processing the next part after the previous part is processed, which is expressed as

[0051] S i(j+1)k ·x i(j+1)k ≥S ijk ·x ijk +P ijk ·x ijk ,

[0052] Among them, S ijk represents the start time of component j on the kth machine of process i; P ijk represents the processing time of component j on the kth machine in process i; x ijk Indicates whether component j is processed by machine k in process i.

[0053] (3) First come, first served: Parts on idle machines are processed on a first come, first served basis, which is a constraint of FCFS scheduling rules.

[0054] Through the embodiments of the present disclosure, the constraints of equipment and workers are defined according to the production environment and process flow, so that the model conforms to the actual production operation situation.

[0055] On the basis of the above embodiment, constructing the objective function of the multi-objective interference management scheduling model includes: constructing a first objective function according to the completion time of the parts to be processed; constructing a second objective function according to the processing lag time of the parts to be processed;

[0056] The expression of the first objective function is as follows:

[0057]

[0058] In the formula, ω j is the processing weight of the jth component; C ij is the completion time of the jth component in process i;

[0059] The expression of the second objective function is as follows:

[0060]

[0061] In the formula, ω j is the processing weight of the jth component; T j is the processing lag time of the jth component.

[0062] Through the embodiments of the present disclosure, the initial scheduling optimization goal is to minimize the weighted sum of the completion time of all parts, and the rescheduling optimization goal is to minimize the weighted sum of the processing lag time of all parts. The two objective functions comprehensively consider the initial arrangement and the adjustment after interference. The initial scheduling optimization is an optimization for the situation at the beginning of the production plan. The initial scheduling optimization will arrange the production sequence and resource allocation according to the existing production information under the set parameters and conditions to maximize the overall production efficiency. The rescheduling optimization is mainly to adjust the processing sequence and resource allocation of parts to deal with interference factors (such as equipment failure, worker absence, etc.) that occur in the production process. On the basis of the above embodiments, according to the processing lag time of the parts to be processed, a second objective function is constructed, including: calculating the processing lag time according to the completion time of the parts to be processed;

[0063] The calculation formula of the processing lag time is as follows:

[0064]

[0065] In the formula, C j The actual completion time after all processes are completed for the jth component; The predicted completion time of the j-th component after completing all processes; the predicted completion time is obtained by optimizing the first objective function and then obtaining a priority ranking.

[0066] It should be noted that processing lag time is a common concept in the field of mechanical processing. It involves many aspects, including the lag time of machine tool probe measurement, the stagnation time in production lead time, and the actual processing time exceeding the expected time due to various reasons during the processing.

[0067] Through the embodiments of the present disclosure, by taking the processing delay time caused by interference factors into consideration in the model, monitoring the interference factors in the production process in real time, and adjusting the scheduling model in real time according to the interference factors, it is ensured that the production plan can flexibly respond to changes, compensate for the processing delay time, and optimize the production process, which can effectively improve production efficiency and processing quality.

[0068] On the basis of the above embodiment, the production scheduling data is input into the multi-objective interference management scheduling model, and the priority ranking of the parts to be processed is obtained by using the optimization algorithm, including: obtaining a first processing weight according to the first objective function and the optimization algorithm; using the first processing weight to calculate the processing lag time for obtaining a second processing weight according to the second objective function and the optimization algorithm; wherein the priority of the parts is positively correlated with the second processing weight.

[0069] In this embodiment, a hybrid particle swarm optimization algorithm is introduced: (1) Particle initialization: Initialize the position and speed of the particle. The position of the particle represents a potential solution, and the speed represents the update direction of the solution. The initial values ​​of the position and speed are usually randomly generated; (2) Speed ​​update, position update: The speed of each particle is updated according to its own experience and group experience, and the position of the particle is updated according to the updated speed; (3) Fitness evaluation: The fitness of each particle is evaluated according to the objective function, and the historical best position and the global best position are updated; (4) Local optimization and global search: HPSO introduces other optimization algorithms such as simulated annealing (SA) and genetic algorithm (GA) on the basis of PSO to enhance the global search and local optimization capabilities. For example, first, local optimization, after each particle update, SA is used for local optimization to further optimize the particle position and improve the accuracy of the solution. Second, global search, GA operations are periodically introduced to perform global search to prevent the algorithm from falling into the local optimum and improve the diversity of solutions and global exploration capabilities; Termination condition: When the maximum number of iterations is reached or the fitness converges, the algorithm terminates and outputs the best scheduling plan.

[0070] It should be noted that in multi-objective optimization, the concept of Pareto optimal solution set is used to balance the conflicts between multiple objectives. When evaluating fitness, the performance of each solution on multiple objectives is calculated. The Pareto optimal solution set is constructed, and the current optimal solution set is maintained. When updating particles and operating GA, the Pareto optimal solution set is referenced to update the particle swarm.

[0071] In this embodiment, in dynamic production scheduling, HPSO can effectively solve multi-objective and multi-constrained optimization problems. The following are the specific application steps of HPSO in dynamic production scheduling: (1) Initialize the particle swarm and generate the initial production schedule. The position of the particle represents a production schedule, and the speed represents the direction of the plan adjustment; (2) Execute the PSO update step, adjust the production schedule, use SA for local search, further optimize the production schedule, and periodically introduce GA operations to enhance the global search capability; (3) Fitness evaluation evaluates the fitness of each production schedule, considering multiple objectives such as production efficiency, resource utilization, and delivery time. Update the historical optimal position and global optimal position of the particle; (4) Interference management monitors the interference factors in the production process in real time, such as equipment failure, material shortage, etc., and dynamically adjusts the production schedule according to the interference situation to ensure production continuity and efficiency; (5) Termination condition When the maximum number of iterations is reached or the fitness converges, the optimal production schedule is output.

[0072] Through the embodiments of the present disclosure, the hybrid particle swarm optimization algorithm, as a method integrating multiple optimization technologies, has strong global search capabilities and local optimization capabilities. It combines the model containing constraints and objectives with the particle swarm optimization algorithm, that is, initializes the particle swarm to represent different parameter combinations, evaluates the satisfaction of each particle with the constraints and the objective function value, updates the individual and global optimal solutions, iteratively adjusts the particle speed and position to explore a better solution until the termination condition is met, and finally obtains the model parameters that satisfy the constraints and optimize the objectives, thereby being able to provide efficient and flexible dynamic production scheduling and dispatching solutions in a complex and changeable production environment, and using its powerful global search and local optimization capabilities to make up for the shortcomings of traditional methods.

[0073] On the basis of the above-mentioned embodiment, the priority ranking of the parts to be processed is used to generate a production schedule for the parts to be processed, including: determining whether there is currently a shortage of materials and / or faulty equipment; if so, lowering the priority of the affected parts, and generating a production schedule for the parts to be processed according to the priority ranking; if not, generating a production schedule for the parts to be processed according to the priority ranking.

[0074] In this embodiment, equipment failure: consider the possible equipment failure and maintenance time, and dynamically adjust the production plan. Material shortage: monitor material inventory and supply in real time, predict material shortages, and adjust the production plan. Worker absence: design backup plans for possible worker absences to ensure the continuity of the production line. Order changes: respond to changes and cancellations of customer orders, and flexibly adjust production tasks and priorities.

[0075] For example, in response to material shortages: Real-time inventory monitoring: Implement a real-time inventory monitoring system for raw materials and semi-finished products, and automatically alert materials that are below safety stock levels; Prioritize existing inventory: Based on production priorities, prioritize existing inventory to important orders or high-priority production tasks; Adjust production plans: Postpone low-priority orders: Postpone low-priority orders until the materials arrive; Adjust production processes: Adjust production processes based on available materials, and process orders that use less material or alternative materials first.

[0076] For example, in the case of equipment failure: Equipment monitoring system: real-time monitoring of equipment status, automatic recording of failure time and type, and identification of potential failures in advance; Adjustment plan: Assess the impact of the failure: Assess the impact of the failure on the current production plan, and determine the affected orders and production lines; Rescheduling: If spare equipment is available, transfer the affected tasks to other available equipment first, and adjust the order of production processes based on the availability of the equipment, temporarily postpone the processing of parts that need to be processed by the faulty equipment; Parallel processing of multiple processes: Adjust the processes that can be processed in parallel, and use the remaining resources to minimize production losses.

[0077] Through the embodiments of the present disclosure, during the production process, changes in the production environment and interference factors are monitored in real time, and the scheduling plan is dynamically adjusted to ensure the continuity and efficiency of production.

[0078] Based on the above-mentioned production scheduling method of a flexible processing production line, the present disclosure also provides a production scheduling device for a flexible processing production line, which can be used to implement the above-mentioned method. Figure 2 The device is described in detail.

[0079] like Figure 2 As shown, the production scheduling device 200 of the flexible processing production line of this embodiment includes a model module 201, an optimization module 202 and a production scheduling module 203.

[0080] The model module constructs a multi-objective interference management scheduling model; the multi-objective interference management scheduling model includes constraints and objective functions for solving the priority sorting of the parts to be processed. In one embodiment, the model module can be used to perform the operation S1 described above, which will not be repeated here.

[0081] The optimization module is used to input the production scheduling data into the multi-objective interference management scheduling model, and use the optimization algorithm to obtain the priority ranking of the parts to be processed; the production scheduling data at least includes the set of parts to be processed and the time parameters of the parts to be processed. In one embodiment, the optimization module can be used to perform the operation S2 described above, which will not be repeated here.

[0082] The production scheduling module is used to generate a production scheduling plan for the parts to be processed by using the priority ranking of the parts to be processed. In one embodiment, the production scheduling module can be used to perform the operation S3 described above, which will not be described in detail here.

[0083] Through the embodiments of the present disclosure, an efficient and flexible solution is provided, which is suitable for flexible manufacturing environments that require high flexibility and rapid response, solves the problem of rapid switching between different products, and effectively manages the production needs of multiple varieties and small batches, and meets the production management needs of prefabricated machine rooms of energy stations in the construction industry for high efficiency, low cost, and on-time delivery.

[0084] On the basis of the above-mentioned embodiment, the device also includes an information collection module for collecting and reading production scheduling related information and obtaining real-time data from the production management system. Specifically, it includes equipment installation and configuration (sensor deployment: installing sensors on production equipment to monitor the operating status, utilization and fault information of the equipment in real time; network connection: ensuring that sensors and data acquisition equipment are connected to the local area network or the cloud to support real-time data transmission; interface development: developing interfaces with enterprise resource planning (ERP), manufacturing execution system (MES), human resource management system (HRMS) and warehouse management system (WMS) to ensure that data automatically flows into the information collection module to provide a standardized API interface to facilitate other modules to call data), data transfer station (setting up a transfer server or data middle station to receive and aggregate data from each system: real-time data collection order information acquisition, obtaining production orders through the ERP system Detailed data, including order number, product specifications, quantity and delivery date, etc.; equipment status monitoring, real-time monitoring of equipment operation status, start-up and downtime, and preventive maintenance plan through sensors and MES system; worker allocation management, obtaining worker skills, scheduling and labor efficiency data through HRMS system, and updating worker status in real time; material supply management, updating inventory data of raw materials and semi-finished products through WMS system, tracking material procurement plan and logistics status) and data processing and storage (data cleaning and formatting: cleaning and formatting the collected data to ensure data consistency and accuracy; storage mechanism: storing the processed data in relational database or NoSQL database, and backing up the data regularly).

[0085] Figure 3 A block diagram of an electronic device suitable for implementing a production scheduling method for a flexible processing production line according to an embodiment of the present disclosure is schematically shown.

[0086] like Figure 3 As shown, the electronic device 300 according to an embodiment of the present disclosure includes a processor 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage part 308 into a random access memory (RAM) 303. The processor 301 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 301 may also include an onboard memory for caching purposes. The processor 301 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0087] In RAM 303, various programs and data required for the operation of electronic device 300 are stored. Processor 301, ROM 302 and RAM 303 are connected to each other through bus 304. Processor 301 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 302 and / or RAM 303. It should be noted that the program can also be stored in one or more memories other than ROM 302 and RAM 303. Processor 301 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0088] According to an embodiment of the present disclosure, the electronic device 300 may further include an input / output (I / O) interface 305, which is also connected to the bus 304. The electronic device 300 may further include one or more of the following components connected to the I / O interface 305: an input portion 306 including a keyboard, a mouse, etc.; an output portion 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 308 including a hard disk, etc.; and a communication portion 309 including a network interface card such as a LAN card, a modem, etc. The communication portion 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, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage portion 308 as needed.

[0089] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0090] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

[0091] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0092] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0094] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A production scheduling method for a flexible processing production line, characterized in that: include: Constructing a multi-objective interference management scheduling model; the multi-objective interference management scheduling model includes constraints and objective functions for solving the priority sorting of parts to be processed; Inputting the production scheduling data into the multi-objective interference management scheduling model, and using an optimization algorithm to obtain the priority ranking of the parts to be processed; the production scheduling data at least includes a set of parts to be processed and time parameters of the parts to be processed; The priority ranking of the parts to be processed is used to generate a production schedule for the parts to be processed.

2. The method according to claim 1, wherein: Construct a multi-objective interference management scheduling model, including: The decision variable of the multi-objective interference management scheduling model is defined as the processing weight of the parts to be processed; the processing weight is used to characterize the priority of the parts to be processed; According to the operation conditions of the flexible processing production line, the constraint conditions of the multi-objective interference management scheduling model are constructed; the operation conditions include machine disturbance, continuous processing and first-come-first-served processing of parts; According to the completion time and processing lag time of the parts to be processed, the objective function of the multi-objective interference management scheduling model is constructed.

3. The method according to claim 2, wherein: The constraints for constructing the multi-objective interference management scheduling model include: Constructing a first constraint condition, wherein the first constraint condition is that the component cannot be scheduled for processing within a time window of machine disturbance; Constructing a second constraint condition, wherein the second constraint condition is that the same machine can only start processing the next component after the previous component is processed; A third constraint condition is constructed, wherein the third constraint condition is that parts on idle machines are processed on a first-come, first-served basis.

4. The method according to claim 2, wherein: Constructing the objective function of the multi-objective interference management scheduling model includes: Constructing a first objective function according to the completion time of the parts to be processed; Constructing a second objective function according to the processing lag time of the component to be processed; The expression of the first objective function is as follows: In the formula, ω j is the processing weight of the jth component; C ij is the completion time of the jth component in process i; The expression of the second objective function is as follows: In the formula, ω j is the processing weight of the jth component; T j is the processing lag time of the jth component.

5. The method according to claim 4, wherein: According to the processing lag time of the component to be processed, a second objective function is constructed, including: Calculate the processing delay time according to the completion time of the parts to be processed; The calculation formula of the processing lag time is as follows: In the formula, C j The actual completion time after all processes are completed for the jth component; The predicted completion time of the j-th component after completing all processes; the predicted completion time is obtained by optimizing the first objective function and then obtaining a priority ranking.

6. The method according to claim 4, wherein: The production scheduling data is input into the multi-objective interference management scheduling model, and the priority ranking of the parts to be processed is obtained by using an optimization algorithm, including: Obtaining a first processing weight according to the first objective function and the optimization algorithm; Calculating a processing delay time using the first processing weight to obtain a second processing weight according to the second objective function and an optimization algorithm; The priority of the component is positively correlated with the second processing weight.

7. The method according to claim 1, wherein: Using the priority ranking of the parts to be processed, a production schedule for the parts to be processed is generated, including: Determine if there are currently any shortages of materials and / or faulty equipment; If so, the priority of the affected parts is lowered, and a production schedule for the parts to be processed is generated according to the priority ranking; if not, a production schedule for the parts to be processed is generated according to the priority ranking.

8. A production scheduling device for a flexible processing production line, characterized in that: The production scheduling device of the flexible processing production line can be used to implement the method as claimed in any one of claims 1 to 7, including: A model module, constructing a multi-objective interference management scheduling model; the multi-objective interference management scheduling model includes constraints and objective functions for solving the priority sorting of parts to be processed; An optimization module, used for inputting the production scheduling data into the multi-objective interference management scheduling model, and obtaining the priority ranking of the parts to be processed by using an optimization algorithm; the production scheduling data at least includes a set of parts to be processed and time parameters of the parts to be processed; The production scheduling module is used to generate a production scheduling plan for the parts to be processed by using the priority ranking of the parts to be processed.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 7.

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