Process scheduling system and process scheduling method
By optimizing process combinations through a process scheduling system, the problem of process arrangement in large-scale system assembly products has been solved, achieving efficient process connection and rational allocation of production equipment, thereby improving production efficiency and product quality.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- DELTA ELECTRONICS INC(CN)
- Filing Date
- 2025-01-03
- Publication Date
- 2026-07-09
AI Technical Summary
The assembly process for large system products is complicated, making it difficult to find the optimal operation unit and sequence. Human factors can easily lead to sequencing errors, resulting in low production efficiency.
The process scheduling system receives order data and optimization target values through a processor. Combined with simulation and optimization modules, it generates process combinations and optimizes the process scheduling table based on process constraints and equipment quantity, ensuring smooth connection between processes and reasonable allocation of production equipment.
It improved production efficiency and product quality, reduced efficiency losses caused by human factors, and ensured the optimal arrangement of processes.
Smart Images
Figure CN2025070564_09072026_PF_FP_ABST
Abstract
Description
Process scheduling system and process scheduling method Technical Field
[0001] This invention relates to a process scheduling system and a process scheduling method. Specifically, this invention relates to a process scheduling system and a process scheduling method that can consider the relationships between processes and efficiently allocate processes according to optimization objectives. Background Technology
[0002] Large-scale system assembly products are products that integrate numerous and bulky components, such as mechanical equipment, industrial automation systems, and energy storage systems. Due to the cumbersome assembly process, arranging the operation units and sequence of the process is time-consuming and labor-intensive, and it is difficult to find the optimal solution. Furthermore, human factors may cause sequencing errors.
[0003] Therefore, the above-mentioned technologies still have many shortcomings, and it is necessary for practitioners in this field to develop other suitable process scheduling systems and methods. Summary of the Invention
[0004] One aspect of this case relates to a process scheduling system. The process scheduling system includes a memory and a processor. The processor is communicatively connected to multiple production units in a production line system and the memory. The process scheduling system performs the following operations: the processor receives order data and multiple optimization target values from an input device, and obtains the maximum number of units corresponding to the multiple production units from the production line system, wherein the order data includes multiple processes corresponding to multiple processing tasks; the processor generates multiple process combinations based on at least one process constraint and the maximum number of units for each of the multiple processes; the processor generates multiple target process combinations based on the multiple optimization target values and simulation results corresponding to each of the multiple process combinations; the processor selects a first target simulation result based on the simulation results of each of the multiple target process combinations, wherein the first target simulation result corresponds to a first target process combination; and the processor generates a process scheduling table based on the first target process combination, and outputs the process scheduling table to the production line system.
[0005] In some embodiments, the processor includes a simulation module and an optimization module, and the processor further performs the following operations: (a) the simulation module generates a plurality of first process combinations and a first simulation result corresponding to each of the plurality of first process combinations based on at least one process constraint of each of the plurality of processes and the number of first devices; (b) the optimization module receives the plurality of first process combinations and the first simulation result corresponding to each of the plurality of first process combinations from the simulation module; (c) the optimization module adjusts the number of first devices to a second number of devices based on a plurality of optimization target values and the simulation results corresponding to each of the plurality of process combinations; (d) the simulation module generates a plurality of second process combinations and a second simulation result corresponding to each of the plurality of second process combinations based on at least one process constraint of each of the plurality of processes and the number of second devices; and (e) in response to the second simulation result corresponding to each of the plurality of second process combinations conforming to a plurality of optimization target values, the plurality of second process combinations are taken as a plurality of target process combinations.
[0006] In some embodiments, at least one of the multiple processes is limited to at least one or a combination of a preceding process, a group process, and a collaborative process.
[0007] In some embodiments, when the first step includes a preceding step and the preceding step is used to indicate the second step, in a combination of multiple steps, the execution order of the second step is earlier than the execution order of the first step.
[0008] In some embodiments, when the third step includes a coordinating step and the coordinating step is used to indicate the third step and the fourth step, in a combination of multiple steps, the third step and the fourth step are performed at the same time.
[0009] In some embodiments, when the fifth process includes a group process and the group process is used to indicate that the first group includes the fifth process and the sixth process, in each of the multiple process combinations, the first production device cannot perform the operation of other groups before the first production device completes the fifth process and the sixth process in the first group.
[0010] In some embodiments, generating multiple target process combinations includes the following operations: (a) selecting at least one first optimization target value from multiple optimization target values; (b) performing a genetic algorithm on the multiple process combinations based on the at least one first optimization target value and the simulation results corresponding to the multiple process combinations to generate multiple optimized process combinations; (c) repeating operations (a) and (b) until the number of iterations reaches a target evolution threshold; and (d) setting the multiple optimized process combinations as multiple target process combinations.
[0011] In some embodiments, the processor further performs the following operations: receiving new device counts from the production line system to update the maximum device count; and generating multiple process combinations based on at least one process limit and the maximum device count of each of the multiple processes.
[0012] In some embodiments, the production line system obtains the device skills of each of the plurality of production devices and the maximum number of devices corresponding to each of the plurality of device skills, and the plurality of processes also include device skill limitations, wherein the processor further performs the following operation: the processor generates a plurality of process combinations based on at least one process limitation and device skill limitation of each of the plurality of processes, the device skills of each of the plurality of production devices and the maximum number of devices corresponding to each of the plurality of device skills.
[0013] In some embodiments, each of the plurality of processes further includes a lower bound and an upper bound of the number of divisible elements, and the processor further performs the following operation: the processor generates a plurality of process combinations based on at least one process constraint of each of the plurality of processes, the device skill of each of the plurality of production devices, the maximum number of devices corresponding to each of the plurality of device skills, the lower bound and the upper bound of the number of divisible elements, wherein the maximum number of devices executing the target process corresponding to the target device skill is between the lower bound and the upper bound of the number of divisible elements included in the target process.
[0014] Another aspect of this case relates to a process scheduling method. The process scheduling method is used in a process scheduling system. The process scheduling system is communicatively connected to a production line system. The process scheduling method includes the following steps: receiving order data and multiple optimization target values from an input device, and obtaining the maximum number of devices corresponding to multiple production devices from the production line system, wherein the order data includes multiple processes corresponding to multiple processing tasks; generating multiple process combinations based on at least one process constraint and the maximum number of devices for each of the multiple processes; generating multiple target process combinations based on the multiple optimization target values and simulation results corresponding to each of the multiple process combinations; selecting a first target simulation result based on the simulation results of each of the multiple target process combinations, wherein the first target simulation result corresponds to a first target process combination; and generating a process scheduling table based on the first target process combination, and outputting the process scheduling table to the production line system.
[0015] This invention provides a process scheduling system and method that ensures smooth workflow between processes and that production units (or operating units) can be assigned to suitable processes. Furthermore, it optimizes the production process of the production line, thereby improving overall production efficiency and product quality, and reducing efficiency losses due to human factors. Attached Figure Description
[0016] The content of this case can be better understood by referring to the implementation methods in the following paragraphs and the accompanying drawings:
[0017] Figure 1 is a circuit block diagram of a process scheduling system and a production line system according to some embodiments of this case;
[0018] Figure 2 is a unit schematic diagram of the processor of a process scheduling system according to some embodiments of the present invention;
[0019] Figure 3 is a schematic diagram illustrating process limitations and process relationships according to some embodiments of this case;
[0020] Figure 4 is a schematic diagram of a process scheduling method according to some embodiments of this case;
[0021] Figures 5A and 5B are schematic representations of process schedules generated by a process scheduling system according to some embodiments of this case;
[0022] Figure 6 is a schematic representation of the process schedule generated by the process scheduling system according to some embodiments of this case;
[0023] Figure 7 is a schematic representation of the process schedule generated by the process scheduling system according to some embodiments of this case;
[0024] Figure 8 is a schematic representation of a process schedule generated by a process scheduling system according to some embodiments of the present invention; and Figure 9 is a schematic representation of a process schedule generated by a process scheduling system according to some embodiments of the present invention.
[0025] Figure Numbering Explanation: 100: Process Scheduling System 110: Memory 120: Processor 130: Input Device 140: Output Device 900: Production Line System 910: Production Unit 121: Simulation Module 122: Optimization Module M1: Management Agent Model M2: Agent Model U1: Management Operation Unit U2: Allocation Operation Unit U3: Finished Product Unit U4: Process Status Unit U5: Operation Unit U6: Encoding Unit U7: Decoding Unit U8: Evaluation Unit U9: Evolution Unit 20: Process Scheduling Method S1~S5: Steps 300: Process Constraints and Process Relationship Diagram CCS1~CCS2: Collaborative Processes CSG: Group Processes PCS: Preceding Processes p001~p048: Processes A, B, C, D: Devices 400, 410, 500, 600, 700, 800: Process Scheduling Tables PD1~PD8: Production Devices SK1~SK2: Device Skills Detailed Implementation
[0026] The spirit of this case will be clearly explained below with reference to the accompanying drawings and detailed description. Anyone skilled in the art who understands the embodiments of this case can make changes and modifications based on the technology taught in this case without departing from the spirit and scope of this case.
[0027] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of this case. The singular forms such as “a,” “this,” “this,” “the,” and “the” as used herein also include various other forms.
[0028] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.
[0029] Unless otherwise specified, the terms used herein generally have their ordinary meaning in the context of the art, the subject matter, and the specific content of this case. Certain terms used to describe this case will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing the case.
[0030] Figure 1 is a circuit block diagram illustrating a process scheduling system 100 and a production line system 900 according to some embodiments of this invention. The process scheduling system 100 includes a memory 110, a processor 120, an input device 130, and an output device 140. The memory 110 is coupled to the processor 120, the input device 130, and the output device 140. The production line system 900 includes a plurality of production devices 910.
[0031] In some embodiments, memory 110 includes flash memory, hard disk drive (HDD), solid state drive (SSD), dynamic random access memory (DRAM), or static random access memory (SRAM).
[0032] In some embodiments, the processor 120 may include, but is not limited to, a single processor and an integration of multiple microprocessors, such as a central processing unit (CPU), a digital signal processor (DSP), or a graphics processing unit (GPU).
[0033] In some embodiments, the input device 130 is used to receive user operations to receive process data, optimization targets, device data of various production devices 910 of the production line system 900, and order data. For example, the input device 130 may be implemented using one or more of the following: a keyboard, a mouse, a touch screen, buttons, or any device with similar functionality. This invention does not limit the hardware type of the input device 130.
[0034] In some embodiments, the output device 140 may be implemented as a liquid crystal display (LCD), a speaker, or a similar audio output device. In some embodiments, the input device 130 and the output device 140 may be integrated into a single device, such as a touch screen.
[0035] In some embodiments, the production line system 900 may be a production line system for products corresponding to the manufacturing, logistics, food and pharmaceutical industries.
[0036] In some embodiments, multiple production facilities 910 include machines for relevant processes (e.g., assembly processes, disassembly processes, and transportation processes) in the aforementioned various fields (e.g., manufacturing, logistics, food, and pharmaceutical industries).
[0037] Figure 2 is a schematic diagram of various units of the processor 120 in the process scheduling system 100 of Figure 1, according to some embodiments of this invention. The processor 120 includes a simulation calculation module 121 and an optimization module 122. The simulation calculation module 121 includes a management agent model M1 and an agent model M2. The management agent model M1 includes a management operation unit U1 and an allocation operation unit U2. The agent model M2 includes a finished product unit U3, a process status unit U4, and an operation unit U5. The optimization module 122 includes an encoding unit U6, a decoding unit U7, an evaluation unit U8, and an evolution unit U9.
[0038] The simulation module 121 mainly applies Agent-Based Modeling and Simulation (ABMS) technology, which simulates the interaction and influence between agents through the micro-behavior of individual agents in the system, thereby exploring the macro-behavior in the system.
[0039] Next, referring to Figures 1 and 2, the simulation module 121 is used to establish a simulation environment for the production line and perform simulations. In the simulation environment, the simulation module 121 first establishes a management operation unit U1 with management functions and an allocation operation unit U2 based on the device skill categories of each of the multiple production devices 910. The simulation module 121 establishes a finished product unit U3 based on the order data input by the input device 130. The simulation module 121 establishes an operation unit U5 based on the maximum number of devices of the multiple production devices 910 in the production line system 900. The simulation module 121 establishes a process status unit U4 based on multiple process data from the order data (e.g., process name, required skills, process time, process limitations, and quantity limitations).
[0040] In some embodiments, the processor 120 determines the number of corresponding agent models (e.g., finished product unit U3, process status unit U4, and operation unit U5) based on the number of multiple parameters (e.g., order data, total number of multiple production devices 910, and detailed data of the process) of multiple process combinations.
[0041] The management operation unit U1, the allocation operation unit U2, the finished product unit U3, the process status unit U4, and the operation unit U5 simulate each of the multiple process combinations generated by the optimization module 122, and generate simulation results for each of the multiple process combinations.
[0042] The optimization module 122 mainly applies the genetic algorithm (GA), which aims to quickly find an approximate optimal solution.
[0043] Next, please refer to Figures 1 and 2. The encoding unit U6 of the optimization module 122 is used to determine the process execution sequence based on multiple process data of the order data of the production line system 900.
[0044] The decoding unit U7 of the optimization module 122 is used to convert the data format transmitted between the analog computing module 121 and the optimization module 122. In some embodiments, the decoding unit U7 is used to convert the data processed by the encoding unit U6 into the data format specified by the analog computing module 121.
[0045] The evaluation unit U8 of the optimization module 122 is used to evaluate various simulation results from the simulation module 121 to select the best process combination. The evolution unit U9 of the optimization module 122 is used to generate various new process combinations (or next-generation process combinations) based on the selected best process combination. The evolution unit U9 generates various next-generation process combinations using genetic algorithms, including selection, crossover, and mutation methods.
[0046] To facilitate understanding of the operation between the simulation module 121 and the optimization module 122, please refer to Figures 1 and 2. The simulation module 121 of the processor 120 in Figure 2 obtains multiple process data (e.g., Table 1 below) of order data for at least one product and device data (e.g., maximum number of devices, device skill category) of multiple production devices 910 from the production line system 900 and input device 130 in Figure 1. The maximum number of devices can be the total number of devices on the actual production line.
[0047] Table 1.
[0048] Please refer to Figure 2. The simulation module 121 is used to generate multiple process combinations and simulation results for each of the multiple process combinations based on at least one process constraint and any number of devices within the maximum number of devices.
[0049] To make the definition of process constraints determined by the simulation module 121 of processor 120 easier to understand, please refer to Figure 3 and Table 1. Figure 3 is a schematic diagram 300 illustrating process constraints and process relationships according to some embodiments of this case.
[0050] Please refer to Figure 3 and Table 1. The process constraint and process relationship diagram 300 includes processes p001, p002, p003, p004, p005, p006, p007, p008, and p009. Process constraints include cooperating processes (e.g., cooperating processes CCS1 and CCS2), preceding processes PCS, and group processes CSG. The definition of process constraints will be explained in subsequent paragraphs with reference to the accompanying drawings. It should be noted that the number of processes is not limited to the embodiments shown in the accompanying drawings.
[0051] When the first process in a plurality of processes (e.g., process p003 or process p004) includes a preceding process PCS and the preceding process PCS is used to indicate the second process (e.g., process p001), in the plurality of process combinations, the execution order of the second process (e.g., process p001) is earlier than the execution order of the first process (e.g., process p003 or process p004).
[0052] For example, process p001 is the prerequisite process (PCS) for processes p003, p004, and p006. Process p001 must be completed first in the execution flow before processes p003, p004, and p006 can be executed. Similarly, process p002 is the prerequisite process (PCS) for process p005. Process p005 is the prerequisite process (PCS) for processes p007 and p008.
[0053] In some embodiments, the processor 120 determines whether two processes in a combination are predecessor processes (PCS). If so, the processor 120 generates a prompt message, which is then displayed to the user via the output device 140. In other words, the process scheduling system 100 excludes the possibility that two processes are predecessor processes (PCS) to avoid errors in the process scheduling system 100.
[0054] When a third process in a plurality of processes (e.g., processes p001 and p002 or processes p007 and p008 in Figure 3) includes a cooperating process (e.g., cooperating processes CCS1 and CCS2) and the cooperating process is used to indicate the third and fourth processes, in the plurality of process combinations, the third and fourth processes are executed at the same time.
[0055] For example, operations p001 and p002 belong to cooperating operations CCS1, which means that operations p001 and p002 need to be executed simultaneously. Similarly, operations p007 and p008 also belong to cooperating operations CCS2, which means that operations p007 and p008 need to be executed simultaneously.
[0056] Please refer to Figures 1 and 3. When the fifth process in a plurality of processes includes a group process CSG and the group process CSG is used to indicate that the first group includes the fifth process (e.g., process p003 in Figure 3) and the sixth process (e.g., process p004 in Figure 3), in each of the plurality of process combinations, the first production device (e.g., one of the plurality of production devices 910) cannot perform the operation of other groups or other processes before it completes the fifth process (e.g., process p003 in Figure 3) and the sixth process (e.g., process p004 in Figure 3) in the first group.
[0057] For example, processes p003 and p004 are group processes CSG, meaning that processes p003 and p004 belong to the same group of processes, and processes p003 and p004 must be completed alternately or continuously before other processes outside the group can be executed. It should be noted that the execution order of processes p003 and p004 is not limited to the embodiments shown in the accompanying drawings.
[0058] After understanding the process constraints, please refer to Figures 2 and 3 and Table 1. The simulation module 121 arranges and simulates processes p001, p002, p003, p004, p005, p006, p007, p008 and p009 in Table 1 based on any number of devices within the maximum number of devices (e.g., 4 units) and the number of devices required for each process in Table 1, so as to generate multiple process combinations and simulation results for each of the multiple process combinations.
[0059] For example, referring to Figures 2 and 3 and Table 1, assume a maximum number of devices of four, namely device A, device B, device C, and device D (not shown in the figures), and each device supports device skills SK1 and device skills SK2. In this example, the simulation module 121 simulates devices A, B, C, and D processing operations p001 to p009 in Table 1, respectively.
[0060] Next, the simulation module 121 generates preliminary simulation results, in which device A completes process p001 (i.e., takes 30 units of time), and devices B and C jointly complete process p002 (i.e., takes 90 units of time). Then, device A completes process p004 (i.e., takes 120 units of time), and devices B and C complete process p003 (i.e., takes 180 units of time). Next, devices A, B, and C complete process p005 (i.e., takes 90 units of time). Finally, device B sequentially completes processes p006 (i.e., takes 480 units of time), p008 (i.e., takes 360 units of time), and p009 (i.e., takes 180 units of time), and device A or C completes process p007 (i.e., takes 300 units of time). After the simulation calculation module 121 calculates and sorts, it generates a simulation result (i.e., preliminary simulation result) for the corresponding processing steps p001 to p009. The total process time is equal to the total operating time of device B, which is calculated to be 90+180+90+480+360+180=1380 unit time.
[0061] Please refer to Figures 1 and 2. In Figure 2, the optimization module 122 of the processor 120 obtains the optimization target value (e.g., minimum number of devices used, minimum total process time, or production line balancing) from the input device 130. The optimization module 122 receives multiple process combinations generated by the simulation calculation module 121 based on the number of devices (e.g., 4 or 3 units) and the simulation results of each of the multiple process combinations.
[0062] Next, the optimization module 122 adjusts the number of devices in the previous simulation (e.g., 4 units) to a new arbitrary number of devices (e.g., 3 units) based on multiple optimization target values (e.g., minimum number of devices used) and multiple simulation results, so as to transmit the new arbitrary number of devices to the simulation calculation module 121 to perform simulation again, so as to generate new simulation results.
[0063] Furthermore, the optimization module 122 responds to multiple process combinations whose simulation results meet multiple optimization target values (e.g., minimum number of devices used), and treats the corresponding multiple process combinations as multiple target process combinations. For example, the optimization module 122 generates new simulation results based on the minimum number of devices used, as shown in Table 2 below.
[0064] Table 2.
[0065] Please refer to Table 2. Assume the maximum number of devices is four: Device A, Device B, Device C, and Device D, and each device supports Device Skill SK1 and Device Skill SK2. The optimization module 122, based on the minimum total process time optimization target, considers the processing time of each process's production device and generates new simulation results (as shown in Table 2 above). In this simulation result, the processing order of processes p005 and p006 is arranged before processes p003 and p004. That is, Device A, Device B, Device C, and Device D process processes p001 to p009 respectively. After Device A completes process p001 (i.e., spends 30 units of time), and Devices B and D jointly complete process p002 (i.e., spend 90 units of time), process p005 (i.e., spend 90 units of time) will be completed by Devices A, B, and C. Next, device A completes process p006 (taking 480 units of time), and devices B, C, and D complete processes p003 (taking 180 units of time) and p004 (taking 120 units of time). When devices B, C, and D complete processes p003 and p004 (taking the longest completion time of 180 units of time), device A still needs 300 units of time to complete process p006. At this point, devices B and C can jointly complete processes p007 and p008, and device D can complete process p009 (taking 540 units of time). After the optimization module 122 calculates and sorts the results by the simulation calculation module 121, it generates simulation results for the corresponding processing processes p001 to p009, with a total process time of 90 + 90 + 540 = 720 units of time. Compared to the preliminary simulation results in Table 1, the total process time in the new simulation results has been reduced from 1380 units of time to 720 units of time.
[0066] Following this, the optimization module 122, after repeatedly performing the above calculations by the simulation module 121, calculates the final simulation result based on the optimization target value of minimum total process time, considering the processing time of each process's production equipment. The final simulation result is that process p001 is completed by device D (i.e., taking 30 units of time). Simultaneously, process p002 is completed by devices B and C (i.e., taking 90 units of time). After completing process p001, process p003 is completed by devices A and D (i.e., taking 180 units of time), and after process p002 is completed, process p004 is completed by device B (i.e., taking 120 units of time). Next, process p005 is completed by devices A, B, and C (i.e., taking 90 units of time), and process p006 is completed by device D (i.e., taking 480 units of time). After process p005 is completed, process p007 is handed over to device A (taking 300 units of time), process p008 is handed over to device B (taking 360 units of time), and process p009 is handed over to device C (taking 180 units of time). The optimization module 122 then uses the simulation module 121 to calculate and sort the results, generating simulation results for the corresponding processing processes p001 to p009. The total process time is equivalent to the total operating time of device D, which is calculated to be 30 + 180 + 480 = 690 units of time. Compared to the aforementioned new simulation results, the total process time of the final simulation result is further reduced from 720 units of time to 690 units of time.
[0067] In detail, the management agent model M1 of the simulation calculation module 121 is used to confirm at least one process limit and device quantity of each of the multiple processes of the multiple process data, and to allocate multiple processes to the agent model M2 for simulation, and to confirm and record the processing simulation status of each of the multiple processes executed by the agent model M2.
[0068] Further examine the detailed operation of the management agent model M1, agent model M2, and optimization module 122 of the simulation module 121. The finished product unit U3 of the management agent model M1 is used to determine whether the order data has been simulated. The process status unit U4 of the management agent model M1 is used to confirm and record the processing simulation status of each of the multiple processes and the corresponding device quantity code (e.g., process p001 is 1, process p002 is 2, process p003 is 2, process p004 is 1, process p005 is 3, process p006 is 1, process p007 is 1, process p008 is 1, and process p009 is 1, which can be written in the code format [1,2,2,1,3,1,1,1,1]).
[0069] The management operation unit U1 is used to confirm at least one process constraint for each of the multiple processes (e.g., process constraints and process relationships in Table 1, equipment skill constraints for processes, equipment skills of multiple production devices 910 corresponding to the maximum number of devices, upper and lower bounds of the number of splittable devices). The allocation operation unit U2 is used to allocate multiple processes to the operation unit U5 of the agent model M2, so that the operation unit U5 simulates multiple production devices 910 in the production line system 900 to execute multiple processes respectively based on the number of devices (e.g., 4 or 3).
[0070] When the finished product unit U3 determines that the order data is in a completed state, the simulation calculation module 121 generates simulation results and process combinations for the corresponding number of devices (e.g., 4 or 3 units). Then, the simulation calculation module 121 transmits the aforementioned data to the evaluation unit U8 of the optimization module 122.
[0071] The coding unit U6 of the optimization module 122 is used to encode the number of devices (e.g., 4 or 3 units) in the process, wherein the actual number of devices used by the production line system 900 is between the lower bound and the upper bound of the divisible quantity of each process in the order data (e.g., Table 1).
[0072] The decoding unit U7 of the optimization module 122 is used to perform a decoding process on the encoded number of devices in order to provide the decoded number of devices to the analog calculation module 121.
[0073] The evaluation unit U8 of the optimization module 122 is used to evaluate the simulation results transmitted by the simulation module 121 based on the target values corresponding to multiple optimization target values (e.g., the minimum number of devices is 3) to generate evaluation results.
[0074] The evolution unit U9 of the optimization module 122 is used to adjust the number of devices used in the simulation based on the evaluation results and target values. For example, the number of devices used in each process is the new number of devices, so that the new number of devices can be transmitted to the simulation calculation module 121 through the encoding unit U6 and the decoding unit U7.
[0075] To facilitate understanding of the detailed operation of the process scheduling system 100, please also refer to Figure 4. Figure 4 is a schematic diagram of a process scheduling method 20 according to some embodiments of this invention. The process scheduling method 20 includes steps S1 to S5. In some embodiments, the process scheduling method 20 may be executed by the process scheduling system 100.
[0076] In step S1, the processor 120 of the process scheduling system 100 receives order data and multiple optimization target values from the input device 130, and obtains the number of each of the multiple production devices 910 from the production line system 900. In some embodiments, the order data includes multiple processes corresponding to multiple processing tasks.
[0077] In step S2, the processor 120 of the process scheduling system 100 generates multiple process combinations based on at least one process constraint of each of the multiple processes and the number of devices. In some embodiments, at least one process constraint of each of the multiple processes includes at least one or a combination of preceding processes, group processes, and collaborative processes.
[0078] Please refer to Figure 4 and the actual order data. The actual order data is shown in Table 3 below.
[0079] Table 3.
[0080] As shown in Table 3, the process restrictions for each process can be at least one of the following: a preceding process, a group process, and a collaborative process, or a combination thereof. It should be noted that the number of processes and process restrictions in Table 3 are not limited to the examples shown in the table.
[0081] In step S3, the processor 120 of the process scheduling system 100 generates multiple target process combinations based on multiple optimization target values and simulation results corresponding to each of the multiple process combinations. In some embodiments, the optimization target values include at least one of the following: minimum total process time, minimum idle time, minimum process cost, maximum production line capacity, and line balance.
[0082] In step S4, the processor 120 of the process scheduling system 100 selects the first target simulation result based on the simulation results of multiple target process combinations.
[0083] For example, referring to Figures 2 and 4, the optimization module 122 of the processor 120 of the process scheduling system 100 generates multiple process combinations based on the process data recorded in Table 3 (e.g., process name, required skills, process time, process limitations, and quantity limitations). Then, the optimization module 122 transmits the multiple process combinations to the simulation calculation module 121.
[0084] Furthermore, the simulation module 121 generates multiple simulation results based on multiple process combinations through the management agent model M1 and agent model M2. Simultaneously, the optimization module 122 of the processor 120 of the process scheduling system 100 generates multiple target process combinations based on multiple optimization target values and multiple simulation results. Then, based on the simulation results of each of the multiple target process combinations, the optimization module 122 of the processor 120 of the process scheduling system 100 selects the first target simulation result (i.e., the optimal simulation result).
[0085] In some embodiments, the processor 120 of the process scheduling system 100 generates multiple target process combinations by the following operations: (a) selecting at least one first optimization target value from multiple optimization target values; (b) performing a genetic algorithm on the multiple process combinations based on the at least one first optimization target value and the simulation results corresponding to the multiple process combinations to generate multiple optimized process combinations; (c) repeating operations (a) and (b) until the number of iterations reaches a target evolution threshold; and (d) setting the multiple optimized process combinations as multiple target process combinations.
[0086] For example, referring to Figures 2 and 4, the processor 120 can select one or more optimization target values (e.g., minimum total process time, minimum idle time, minimum process cost, and production line balancing) to generate multiple optimized process combinations. For example, in the first iteration, multiple optimized process combinations are generated as follows: {First optimized process combination [1,2,2,1,2,2,1,1,…], Second optimized process combination [1,1,1,1,2,2,1,1,…], Third optimized process combination [2,1,2,1,2,1,2,1,…],……}. Then, the processor 120 can repeatedly generate multiple optimized process combinations until the number of iterations reaches the target evolution threshold (e.g., 50 times). Finally, the final generated multiple optimized process combinations are set as multiple target process combinations.
[0087] In step S5, the processor 120 of the process scheduling system 100 generates a process scheduling table based on the first target process combination corresponding to the first target simulation result, and outputs the process scheduling table to the production line system 900.
[0088] For example, please refer to Figures 1, 2, 4, 5A, and 5B. Figure 5A is a schematic diagram of a process scheduling table 400 generated by a process scheduling system 100 according to some embodiments of this invention. Figure 5B is a schematic diagram of a process scheduling table 410 generated by a conventional process scheduling system according to some embodiments of this invention. The first example is that the total number (or maximum number of devices) of multiple production devices 910 is four (e.g., production devices PD1, PD2, PD3, and PD4 in the process scheduling table 400), and the multiple production devices 910 can support multiple device skills (e.g., device skills SK1 and SK2 in Table 2).
[0089] Next, the process scheduling system 100 executes steps S1 to S5 of process scheduling method 20 based on the conditions of this example and Table 2 to generate the process scheduling table 400 of Figure 5A. The total process time of process scheduling table 400 is 3660 units of time, and its optimization objective is production line balancing. The unit of time can be seconds, minutes, or hours. In the process scheduling table 410 generated by the existing process scheduling system, processes p033 and p044 may both be arranged to be executed by production unit PD2, resulting in a total process time of 3960 units of time for process scheduling table 410. After adopting the technology of this invention, the process scheduling table 400 of Figure 5A enables production units PD1, PD2, PD3, and PD4 to operate evenly, and reduces the total process time by approximately 300 units of time.
[0090] In some embodiments, the processor 120 of the process scheduling system 100 receives the number of devices (which can be understood as the number of new devices or the number of device changes) of multiple production devices 910 from the production line system 900. The processor 120 determines the number of devices based on at least one process limit of each of the multiple production devices 910 and the number of devices.
[0091] For example, please refer to Figures 1, 2, 4, and 6. Figure 6 is a schematic diagram of a process scheduling table 500 generated by a process scheduling system 100 according to some embodiments of this case. A second example is the first example where the total number of multiple production devices 910 is four (e.g., production device PD1, production device PD2, production device PD3, and production device PD4 in process scheduling table 400), and each production device 910 can support multiple device skills (e.g., device skills SK1 and SK2 in Table 3). Based on the conditions of this example and Table 1, the process scheduling system 100 executes steps S1 to S5 of the process scheduling method 20 to generate the process scheduling table 500 of Figure 6. The total process time of process scheduling table 500 is 3555 units of time. Compared to the process scheduling table 400 of the embodiment in Figure 5A, the optimization objective of process scheduling table 500 is minimum idle time.
[0092] Furthermore, please refer to Figures 1, 2, 4, and 7. Figure 7 is a schematic diagram of a process scheduling table 600 generated by the process scheduling system 100 according to some embodiments of this case. Continuing with the second example above, compared to Figure 6, when a production device PD5 is added to the production line system 900, the total number of multiple production devices 910 changes from 4 to 5 (i.e., the updated maximum number of devices), and production device PD5 can also support multiple device skills (e.g., device skills SK1 and SK2 in Table 3). Based on the conditions of this example and Table 2, the process scheduling system 100 executes steps S1 to S5 of the process scheduling method 20 again to generate the process scheduling table 600 of Figure 7. The total process time of the process scheduling table 600 is 2920 unit time.
[0093] Next, please refer to Figures 1, 2, 4, and 8. Figure 8 is a schematic diagram of a process scheduling table 700 generated by the process scheduling system 100 according to some embodiments of this case. Continuing with the second example above, compared to Figure 7, when the production line system 900 adds another production device PD6, the total number of multiple production devices 910 changes from 4 to 6 (i.e., the updated maximum number of devices), and production device PD6 can also support multiple device skills (e.g., device skills SK1 and SK2 in Table 3). Based on the conditions of this example and Table 3, the process scheduling system 100 again executes steps S1 to S5 of the process scheduling method 20 to generate the process scheduling table 700 of Figure 8. The total process time of the process scheduling table 700 is 2550 unit time.
[0094] Conversely, in the initial stage of production line operation, the total number of production devices 910 is 6. During the process, at least one of the production devices 910 fails. Based on the conditions of this example and Table 3, the process scheduling system 100 executes steps S1 to S5 of the process scheduling method 20 again to change the number of failures corresponding to the process scheduling table 700 to the process scheduling table 500 or the process scheduling table 600.
[0095] In some embodiments, referring to FIG. 1 and Table 3, the processor 120 of the process scheduling system 100 obtains from the production line system 900 the device skills of each of the plurality of production devices 910 and the number of devices corresponding to each of the plurality of device skills, and the plurality of processes also include device skill limitations. The processor 120 generates a plurality of process combinations based on at least one process limitation and device skill limitation of each of the plurality of processes, the device skills and device skill limitations of each of the plurality of production devices 910, and the number of devices corresponding to each of the plurality of device skills.
[0096] For example, please refer to Figures 1, 2, 3, and 9. Figure 9 is a schematic diagram of a process scheduling table 800 generated by a process scheduling system 100 according to some embodiments of this case. The total number of multiple production devices 910 is eight (e.g., production devices PD1, PD2, PD3, PD4, PD5, PD6, PD7, and PD8 in the process scheduling table 800). Among them, production devices PD1, PD2, PD3, and PD4 can only support device skill SK1 in Table 3. Among them, production devices PD5, PD6, PD7, and PD8 can only support device skill SK2 in Table 3. That is, there are four production devices that support device skill SK1 and four production devices that support device skill SK2.
[0097] Next, based on the conditions of this example and Table 3, the process scheduling system 100 executes steps S1 to S5 of the process scheduling method 20 to generate the process scheduling table 800 of Figure 9. The total process time of the process scheduling table 800 is 2535 units of time.
[0098] In some embodiments, referring to Figure 1 and Table 3, the multiple processes further include a lower bound and an upper bound of the divisible quantity. The processor 120 generates multiple process combinations based on at least one process constraint of each of the multiple processes, the device skill of each of the multiple production devices, the number of devices corresponding to each of the multiple device skills, the lower bound and the upper bound of the divisible quantity. The number of devices performing the target process corresponding to the target device skill is between the lower bound and the upper bound of the divisible quantity included in the target process.
[0099] When the processor 120 of the process scheduling system 100 executes the process arrangement process, the processor 120 considers the lower and upper limits of the divisible quantity set for each process. For example, the processor 120 will consider the multiple production devices 910 of the production line system 900, the device skill SK2 required for process p005, the lower limit of the divisible quantity (e.g., quantity 2), and the upper limit of the divisible quantity (e.g., quantity 3). In other words, the processor 120 can adjust the number of devices required for process p005 according to actual needs (e.g., process constraints and device skill constraints).
[0100] In some embodiments, the aforementioned process scheduling tables 400, 500, 600, 700, and 800 are all Gantt charts. A Gantt chart is a bar chart used to display the inherent relationships between the progress of projects, schedules, processes, and other time-related systems over time. In some embodiments, process scheduling tables 400, 500, 600, 700, and 800 can be designed as flowcharts or spreadsheets, etc., to present Gantt charts in other embodiments, depending on actual needs.
[0101] In some embodiments, referring to FIG1, the output device 140 is used to perform the following actions: displaying a process scheduling table (e.g., one of process scheduling table 400, process scheduling table 500, process scheduling table 600, process scheduling table 700 and process scheduling table 800) and the target process combination corresponding to the process scheduling table (e.g., the process combination may be [1,2,1,1,2,1,2,1,2,1…,2] or various optimized process combinations exemplified in the above embodiments), and to receive user operation instructions to display the process constraints (e.g., the preceding processes, group processes and collaborative processes in Table 3) and various process-related data (e.g., assembling screws, docking two parts, aligning and welding multiple parts, product transportation, etc.) of multiple processes corresponding to the target process combination.
[0102] In some embodiments, the production apparatus described above may also be replaced by a human unit to perform the process scheduling method 20.
[0103] Based on the foregoing embodiments, this invention provides a process scheduling system and method that ensures smooth connection between processes and that production devices (or operating units) can be assigned to suitable processes. Furthermore, it ensures process optimization in the production line workflow, thereby improving overall production efficiency and product quality, and reducing efficiency losses due to human factors.
[0104] Although this case has been disclosed above with detailed embodiments, it does not exclude other possible implementations. Therefore, the scope of protection of this case shall be determined by the scope of the appended claims, and not by the limitations of the foregoing embodiments.
[0105] For those skilled in the art, various modifications and refinements can be made to this case without departing from the spirit and scope of this case. Based on the foregoing embodiments, all modifications and refinements made to this case are also covered within the protection scope of this case.
Claims
1. A process scheduling system, characterized in that, Include: Memory; and The processor is communicatively connected to multiple production devices in the production line system and the memory. The process scheduling system described above is used to perform the following operations: The processor receives order data and multiple optimization target values from the input device, and obtains the maximum number of devices corresponding to the multiple production devices from the production line system, wherein the order data includes multiple processes corresponding to multiple processing tasks; The processor generates multiple process combinations based on at least one process limitation of each of the multiple processes and the maximum number of devices; The processor generates multiple target process combinations based on the simulation results corresponding to the multiple optimization target values and the multiple process combinations; The processor selects a first target simulation result based on the simulation results of each of the plurality of target process combinations, wherein the first target simulation result corresponds to a first target process combination; as well as The processor generates a process scheduling table based on the first target process combination, and outputs the process scheduling table to the production line system.
2. The process scheduling system according to claim 1, characterized in that, The processor includes an analog computing module and an optimization module, and the processor further performs the following operations: (a) The simulation module generates multiple combinations of first processes and a first simulation result corresponding to each of the multiple combinations of first processes based on the at least one process limitation of each of the multiple processes and the number of first devices; (b) The optimization module receives from the simulation calculation module the plurality of first process combinations and the first simulation result corresponding to each of the plurality of first process combinations; (c) The optimization module adjusts the number of the first device to the number of the second device based on the multiple optimization target values and the simulation results corresponding to each of the multiple process combinations; (d) The simulation module generates multiple combinations of second processes and corresponding second simulation results for each of the multiple combinations of second processes based on the limitation of at least one process of each of the multiple processes and the number of second devices; as well as (e) In response to the second simulation result corresponding to each of the plurality of second process combinations meeting the plurality of optimization target values, the plurality of second process combinations are taken as the plurality of target process combinations.
3. The process scheduling system according to claim 1, characterized in that, The at least one process of each of the plurality of processes is limited to at least one of or a combination of preceding processes, group processes, and collaborative processes.
4. The process scheduling system according to claim 3, characterized in that, When the first process includes the preceding process and the preceding process is used to indicate the second process, in each of the multiple process combinations, the execution order of the second process is earlier than the execution order of the first process.
5. The process scheduling system according to claim 3, characterized in that, When the third process includes the coordinating process and the coordinating process is used to indicate the third process and the fourth process, in each of the multiple process combinations, the third process and the fourth process are executed at the same time.
6. The process scheduling system according to claim 3, characterized in that, When the fifth process includes the group process and the group process is used to indicate that the first group includes the fifth process and the sixth process, in each of the multiple process combinations, the first production device cannot perform the operation of other groups before the first production device completes the fifth process and the sixth process in the first group.
7. The process scheduling system according to claim 1, characterized in that, Generating the combination of the multiple target processes involves the following operations: (a) Select at least one first optimization objective value from the plurality of optimization objective values; (b) Based on the at least one first optimization target value and the simulation results corresponding to the plurality of process combinations, perform a genetic algorithm on the plurality of process combinations to generate a plurality of optimized process combinations; (c) Repeat operations (a) and (b) until the number of iterations reaches the target evolution threshold; and (d) Set the plurality of optimized process combinations as the plurality of target process combinations.
8. The process scheduling system according to claim 1, characterized in that, The processor further performs the following operations: The system receives new device counts from the production line system to update the maximum device count; and The processor generates the combination of the plurality of processes based on the at least one process limitation of each of the plurality of processes and the maximum number of devices.
9. The process scheduling system according to claim 1, characterized in that, The processor obtains the device skills corresponding to each of the plurality of production devices and the maximum number of devices corresponding to each of the plurality of device skills from the production line system, and the plurality of processes also include device skill limits, wherein the processor further performs the following operations: The processor generates the multiple process combinations based on the at least one process limitation of each of the multiple processes and the device skill limitation, the device skill corresponding to each of the multiple production devices and the maximum number of devices corresponding to each of the multiple device skills.
10. The process scheduling system according to claim 9, characterized in that, Each of the plurality of processes further includes a lower bound and an upper bound of the number of divisible elements, and the processor further performs the following operations: The processor generates the multiple process combinations based on the at least one process constraint of each of the multiple processes, the device skill of each of the multiple production devices, the maximum number of devices corresponding to each of the multiple device skills, the lower bound of the divisible quantity, and the upper bound of the divisible quantity. The maximum number of devices performing the target process corresponding to the target device skill is between the lower bound of the divisible number included in the target process and the upper bound of the divisible number.
11. A process scheduling method, characterized in that, For use in a process scheduling system, the process scheduling system being communicatively connected to a production line system, comprising: Receive order data and multiple optimization target values, and obtain the maximum number of devices corresponding to multiple production devices from the production line system, wherein the order data includes multiple processes corresponding to multiple processing tasks; Based on the limitation of at least one process of each of the multiple processes and the maximum number of devices, multiple process combinations are generated; Based on the multiple optimization target values and the simulation results corresponding to each of the multiple process combinations, multiple target process combinations are generated; Based on the simulation results of each of the multiple target process combinations, a first target simulation result is selected, wherein the first target simulation result corresponds to a first target process combination; as well as Based on the first target process combination, a process scheduling table is generated and output to the production line system.