High-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm

Abstract

The invention relates to the technical field of job shop scheduling, and discloses a high-dimensional multi-flexible job shop scheduling method based on an intelligent optimization algorithm, and the method comprises the following steps: setting a main target of scheduling, and adding constraint conditions according to the wear degree of a tool of machine tool equipment, the change of orders, the fault of equipment and the difference of raw materials; storing the job information, the resource information, the production flow and the scheduling constraint according to a data structure distributed by the model design of the job shop; a sensor, a bar code and an RFID technology are used for collecting the equipment state, the process progress and the resource utilization condition in a workshop in real time; the optimal scheduling algorithm is selected according to the characteristics of the workpiece needing to be machined and the machining target. The device has the capability of maintaining production stability, when one or more machines break down, the working procedure can bypass the broken-down machine, machining is carried out on other machines, so that production is continued, and production stability is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of job shop scheduling, and in particular to a high-dimensional multi-flexible job shop scheduling method based on an intelligent optimization algorithm. Background Art

[0002] In flexible job shop scheduling, high-dimensional goals usually refer to multiple optimization goals that need to be considered simultaneously during the scheduling process. These goals are often interrelated and sometimes conflicting, so a reasonable scheduling solution needs to find a balance between these goals.

[0003] In the job shop scheduling problem, the processing system has a group of machine tools with different functions. The workpiece to be processed contains multiple processes, each process is processed on a machine tool, the processing routes of the workpieces are different, and there are sequence constraints between each workpiece process.

[0004] The existing patent discloses an optimization method and system for the scheduling problem of a reentrant assembly flexible job shop (publication number CN117055481A), the method includes: S1, obtaining the processing information of the workshop to be scheduled, the processing information includes the product information of the workpiece to be processed and the machine information of the processing workshop; S2, inputting the processing information of the workshop to be scheduled into a pre-built workshop scheduling optimization model; S3, solving the workshop scheduling optimization model according to the multi-objective evolutionary algorithm MOEA / D-OOLS based on decomposition combined with goal-oriented local search, and obtaining a workshop scheduling plan. In the technology disclosed in this patent, when there are uncertain factors in the workshop due to the wear degree of machine tool tools, order changes, equipment failures and differences in raw materials, the processing and deployment speed of the workpiece will be greatly reduced. Summary of the invention

[0005] The main technical problem solved by the present invention is to provide a high-dimensional multi-flexible job shop scheduling method based on an intelligent optimization algorithm, which solves the problems in the above-mentioned background technology.

[0006] To solve the above technical problems, according to one aspect of the present invention, more specifically, a high-dimensional multi-flexible job shop scheduling method based on an intelligent optimization algorithm comprises the following steps:

[0007] S1. Set the main goal of scheduling and add constraints based on the wear of machine tool tools, order changes, equipment failures, and differences in raw materials;

[0008] S2, designing a data structure allocated according to the job shop model to store job information, resource information, production process and scheduling constraints;

[0009] S3. Use sensors, barcodes and RFID technology to collect real-time information about equipment status, process progress and resource utilization in the workshop;

[0010] S4, selecting the optimal scheduling algorithm according to the characteristics of the workpiece to be processed and the processing objectives;

[0011] S5. Run the preliminary scheduling plan generated by the scheduling algorithm and verify whether the plan meets all constraints;

[0012] S6. Adjust the scheduling plan of the flexible job shop based on the generated preliminary scheduling plan and actual conditions.

[0013] Furthermore, in step 1, the scheduling objectives set include minimizing production cycle, reducing costs, improving equipment utilization and shortening delivery time.

[0014] Furthermore, the operation information, resource information and production process stored in step 2 can be used as historical reference data for preferential selection and analysis in step S4.

[0015] Furthermore, the scheduling method in step 4 includes a heuristic algorithm and a meta-heuristic algorithm;

[0016] The heuristic algorithm is used to divide and match machine tool equipment according to the wear degree of equipment tools and the uncertain targets caused by the difference in raw materials;

[0017] The meta-heuristic algorithm is used to divide machine tool equipment into uncertain targets caused by order changes and sudden equipment failures.

[0018] Furthermore, in step 6, the scheduling plan of the flexible job shop is monitored by the monitoring end to monitor the implementation effect, and feedback data is collected and fed back to step S5, and the effectiveness of the plan is evaluated based on the degree of consistency between the evaluation performance of the scheduling plan and actual production.

[0019] Furthermore, the similarity between the evaluation performance i produced by the optimally selected scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as:

[0020]

[0021] Where M i,j It represents the average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end.

[0022] Furthermore, the average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as:

[0023]

[0024] In the formula, k represents the amount of equipment that can be deployed in the flexible workshop; C i,j The value range is between 0 and 1. i,j The larger the value of , the greater the similarity between the evaluation performance i produced by the selected scheduling algorithm and the actual production status j monitored by the monitoring end.

[0025] Furthermore, when the similarity between the evaluation performance produced by the selected scheduling algorithm and the actual production status monitored by the monitoring end is lower than 0.6, it is necessary to re-compare the heuristic algorithm with the meta-heuristic algorithm.

[0026] Beneficial effects:

[0027] 1. The present invention can effectively improve the utilization rate of equipment. Once the machine tool is idle, the workpiece can be arranged for processing, thereby reducing the idle and waiting time of the equipment.

[0028] 2. The present invention has the ability to maintain stable production. When one or more machines fail, the process can bypass the failed machine and perform processing on other machines so that production can continue, thereby ensuring stable production.

[0029] 3. The present invention can improve product quality and shorten production cycle. Mainly, multiple processes of the same workpiece can be processed continuously on the same machine tool, reducing the time consumption caused by intermediate loading and unloading and transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0031] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Example 1

[0033] like Figure 1 As shown, according to one aspect of the present invention, a high-dimensional multi-flexible job shop scheduling method based on an intelligent optimization algorithm is provided, comprising the following steps:

[0034] Step 1: Set the main goal of scheduling and add constraints based on the degree of wear of machine tool tools, order changes, equipment failures, and differences in raw materials; the scheduling goals set include minimizing production cycle time, reducing costs, improving equipment utilization, and shortening delivery time.

[0035] Step 2: Design a data structure based on the model of the job shop to store job information, resource information, production process and scheduling constraints; the stored job information, resource information and production process can be used as historical reference data for preferential selection analysis in step S4.

[0036] Step 3: Use sensors, barcodes, and RFID technology to collect real-time information on equipment status, process progress, and resource utilization in the workshop.

[0037] Step 4: Select the optimal scheduling algorithm based on the characteristics of the workpiece to be processed and the processing objectives.

[0038] Step 5: Run the preliminary scheduling plan generated by the scheduling algorithm and verify whether the plan meets all constraints.

[0039] Step 6: Adjust the scheduling plan of the flexible job shop based on the generated preliminary scheduling plan and actual conditions.

[0040] The scheduling method in step 4 of this embodiment includes a heuristic algorithm and a meta-heuristic algorithm; the heuristic algorithm is used to divide and match machine tools according to the uncertainty caused by the degree of wear of the equipment tools and the difference in raw materials; the meta-heuristic algorithm is used to divide the machine tools according to the uncertainty caused by the change of orders and the sudden failure of the equipment. Through the above steps, it can eventually ensure that the operation workshop has the ability to maintain stable production. When one or more machines fail, the process can bypass the failed machine and perform processing on other machines so that production can continue, ensuring stable production.

[0041] Example 1

[0042] The scheduling plan of the flexible operation workshop is monitored through the monitoring end to monitor the implementation effect, and the feedback data is collected and fed back to step five. The effectiveness of the plan is evaluated based on the degree of consistency between the evaluation performance of the scheduling plan and the actual production.

[0043] The similarity between the evaluation performance i produced by the optimally selected scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as:

[0044]

[0045] Where M i,j The average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end. The average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as:

[0046]

[0047] In the formula, k represents the amount of equipment that can be deployed in the flexible workshop; C i,j The value range is between 0 and 1. i,j The larger the value of , the greater the similarity between the evaluation performance i produced by the selected scheduling algorithm and the actual production status j monitored by the monitoring end.

[0048] And when the similarity between the evaluation performance produced by the selected scheduling algorithm and the actual production status monitored by the monitoring end is lower than 0.6, it is necessary to re-compare the heuristic algorithm with the meta-heuristic algorithm. This can improve product quality and shorten the production cycle. For the same workpiece, multiple processes can be processed continuously on the same machine tool, reducing the time consumption caused by intermediate loading and unloading and transportation.

[0049] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm, characterized in that: The following steps are involved: S1. Set the main goal of scheduling and add constraints based on the wear of machine tool tools, order changes, equipment failures, and differences in raw materials; S2, designing a data structure allocated according to the job shop model to store job information, resource information, production process and scheduling constraints; S3. Use sensors, barcodes and RFID technology to collect real-time information about equipment status, process progress and resource utilization in the workshop; S4, selecting the optimal scheduling algorithm according to the characteristics of the workpiece to be processed and the processing objectives; S5. Run the preliminary scheduling plan generated by the scheduling algorithm and verify whether the plan meets all constraints; S6. Adjust the scheduling plan of the flexible job shop based on the generated preliminary scheduling plan and actual conditions.

2. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 1 is characterized by: In step 1, the scheduling objectives set include minimizing production cycle, reducing costs, improving equipment utilization, and shortening delivery time.

3. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: The operation information, resource information and production process stored in step 2 can be used as historical reference data for preferential selection analysis in step S4.

4. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 1 is characterized by: The scheduling method in step 4 includes a heuristic algorithm and a meta-heuristic algorithm; The heuristic algorithm is used to divide and match machine tool equipment according to the wear degree of equipment tools and the uncertain targets caused by the difference in raw materials; The meta-heuristic algorithm is used to divide machine tool equipment into uncertain targets caused by order changes and sudden equipment failures.

5. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: In step 6, the scheduling plan of the flexible job shop is monitored by the monitoring end to monitor the implementation effect, and feedback data is collected and fed back to step S5, and the effectiveness of the plan is evaluated based on the degree of consistency between the evaluation performance of the scheduling plan and the actual production.

6. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 5 is characterized by: The similarity between the evaluation performance i produced by the optimally selected scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as: Where M i,j It represents the average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end.

7. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 6 is characterized by: The average information entropy of the evaluation performance i produced by the scheduling algorithm and the actual production status j monitored by the monitoring end can be expressed as: In the formula, k represents the amount of equipment that can be deployed in the flexible workshop; C i,j The value range is between 0 and 1. i,j The larger the value of , the greater the similarity between the evaluation performance i produced by the selected scheduling algorithm and the actual production status j monitored by the monitoring end.

8. The high-dimensional multi-flexible job shop scheduling method based on intelligent optimization algorithm according to claim 7 is characterized in that: When the similarity between the evaluation performance produced by the selected scheduling algorithm and the actual production status monitored by the monitoring end is less than 0.6, it is necessary to re-compare the heuristic algorithm with the meta-heuristic algorithm.

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