Steel coil cold rolling workshop dynamic scheduling method based on multiple processes and multiple production lines

Abstract

The invention relates to a steel coil cold rolling workshop dynamic scheduling method based on multiple processes and multiple production lines. The method comprises the following steps that data are preprocessed; grouping all the steel coils in batches according to attributes; selecting the batch group with the highest production emergency degree from all the batch groups according to a rule; distributing the selected batch group to the equipment with the highest equipment preference, distributing the selected batch group to the corresponding equipment if the capacity of the equipment is idle, and otherwise, selecting the suboptimal equipment; deleting the steel coils which are arranged in the plan in the original order contract pool, and adding the produced steel coils into the batch of the corresponding process in the contract pool; inquiring whether the number of residual steel coils in the order contract pool is zero or not, and if yes, returning to the step 3; and if so, the scheduling work is ended. According to the method, production scheduling is conducted on the multi-process multi-production-line steel coil cold rolling workshop, and the production scheduling efficiency of the cold rolling workshop is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of production data processing, relates to the field of cold rolling production of steel coils with multiple processes and multiple production lines, and specifically relates to a dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines. Background Art

[0002] At present, most of the cold rolling production scheduling is done by production planners who estimate the time required for pickling, rolling, galvanizing, color coating and other processes of steel coils with different thickness and width requirements based on experience, so as to arrange the process allocation of the entire workshop. However, with the improvement of rolling technology and the increase in the number and type of orders, it has become a problem to allocate the process allocation of the cold rolling workshop reasonably and efficiently by manual labor alone. Manual allocation of processes may lead to increased labor costs in the cold rolling workshop, insufficient utilization of production equipment resources, and even delays in delivery orders, which will undoubtedly hinder the development of the cold rolling business. Therefore, it is very necessary to use system software to combine order information, production processes, production equipment status and efficient scheduling algorithms to mobilize the production rhythm of each process in the cold rolling workshop and make reasonable arrangements. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines in view of the deficiencies in the prior art.

[0004] The present invention is achieved through the following technical solutions:

[0005] A dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines comprises the following steps:

[0006] Step 1: Preprocess the data: Sort the steel coils in the contract pool and record the order information for subsequent scheduling reference; obtain the steel coil information at the end of all equipment in the current state;

[0007] Step 2: Divide all steel coils into batches according to the three attributes of "batch number", "equipment preference" and "process name". The three attributes that are exactly the same are grouped into the same batch. The production urgency of each batch must meet the upper and lower limits of the production urgency of the steel coils in the batch;

[0008] Step 3: Select the batch group with the highest production urgency from all batch groups according to the rules;

[0009] Step 4: Assign the batch group selected in step 3 to the equipment with the highest "equipment preference" corresponding to the "process name". If the equipment has idle capacity, assign the batch group selected in step 3 to the corresponding equipment, otherwise select the suboptimal equipment;

[0010] Step 5: Delete the "batch number" of the steel coils scheduled in the original order contract pool, update the earliest production time of the same steel coil "batch number" in the next process, and add the output steel coils to the batch of the corresponding process in the contract pool; at the same time, update the steel coil information at the end of all equipment in the current state to connect the subsequent steel coils to be scheduled;

[0011] Step 6: Check whether the remaining number of steel coils in the order contract pool is zero. If it is greater than zero, return to step 3; if it is zero, end the scheduling work;

[0012] Step 7: Assess the completion of the completed scheduling work, and use the assessment results as a reference for the next scheduling.

[0013] Moreover, the specific selection rules in step 3 are executed in order as follows:

[0014] Select the steel coils in the bottleneck process that meet the order recorded in step 1;

[0015] Select the batch group that contains the steel coils of the process before the bottleneck process and has a batch group;

[0016] Selects batch groups that have a specified generation time and whose equipment end time matches that time.

[0017] Moreover, in step 3, when there are multiple batch groups that meet the conditions in a rule, the batch group with the earliest average start time is preferentially selected. If there is no time window constraint, the batch group that meets the upper and lower limit requirements of the batch group and has the lowest average scheduling comfort rate is selected.

[0018] Moreover, after completing step 3, for the steel coils newly placed in the multi-process and multi-production lines, the sorting position of the steel coils is adjusted according to the relevant time parameters. The steel coils with suitable positions are directly allocated to the corresponding equipment, and the steel coils without suitable positions are directly placed in the contract pool.

[0019] Furthermore, the equipment with the highest preference in step 4 refers to the equipment with the highest average equipment preference of the steel coils in the batch group.

[0020] Moreover, the overall target KPI for the completion of the scheduling work described in step 8 is summed up by weighting each sub-item indicator: KPI = 0.4*KPIt + 0.2*KPIc + 0.2*KPIa + 0.2*KPIh. The higher the overall target KPI value, the better the scheduling result.

[0021] The sub-item indicators are:

[0022] Order contract punctuality rate KPIt: the ratio of the number of steel coils scheduled to the total number of steel coils ordered on the day. The higher the ratio, the higher the order punctuality rate;

[0023] Equipment scheduling comfort rate KPIc: the sum of the penalties caused by the width, thickness and temperature jumps of adjacent steel coils in the model. The higher the penalty, the lower the equipment scheduling comfort rate;

[0024] Equipment utilization KPIa: The ratio of the equipment's processing time for steel coils to the equipment's theoretical maximum capacity time is the equipment utilization rate;

[0025] Equipment preference hit rate KPIh: After the units under different processes are debugged and put into production, the product quality of steel coils with different specifications is different. By labeling the equipment, different equipment preferences are formed. Equipment preference hit means that the specifications of the steel coils to be produced are arranged exactly to the equipment with the corresponding label preference.

[0026] Moreover, the tail coil information in step 1 includes the time window data of the coil loading start time, transportation waiting time, and unloading end time, as well as the coil specification parameter values ​​such as thickness, width, and temperature.

[0027] An electronic device comprises a memory, wherein the processors are communicatively connected to each other, the memory stores computer instructions, and the processor executes any one of the above-mentioned methods for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines by executing the computer instructions.

[0028] A computer storage medium having computer instructions stored thereon, wherein the computer readable storage medium is used to enable a computer to execute any one of the above-mentioned methods for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines.

[0029] The advantages and beneficial effects of the present invention are:

[0030] The method of the present invention performs production scheduling for a steel coil cold rolling workshop with multiple processes and multiple production lines, effectively improves the production scheduling efficiency of the cold rolling workshop, greatly reduces the workload of planners, and reasonable production scheduling improves the quality of steel coil products and significantly improves the on-time delivery rate of order contracts. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of the dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with specific embodiments.

[0033] A dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines, such as Figure 1 As shown, the method comprises the following steps:

[0034] Step 1: Preprocess the data: Sort the steel coils in the contract pool in descending order by "rolling width", record the material arrangement order information for subsequent scheduling reference; obtain the steel coil information at the end of all equipment in the current state, mainly including time window data such as machine start time, transportation waiting time, machine end time, and thickness, width, temperature and other steel coil specification parameter values;

[0035] The order data in the database is divided into batches according to the similarity of the steel coil specifications and process requirements. The production urgency between batches is compared, focusing on the following three points: first, whether the steel coil is a bottleneck process steel coil; second, whether the steel coil is completed within the specified time period; third, whether the steel coil requires multiple processes.

[0036] If the steel coil is required to be produced at the front of the equipment, it is called the steel coil has a high production urgency. Then the equipment is allocated to the steel coil with the highest production urgency, and the equipment with the highest average equipment preference and idle capacity is selected for allocation. While production is in progress, the next batch with the highest production urgency is selected.

[0037] Step 2: Divide all steel coils into batches according to the three attributes of "batch number", "equipment preference" and "process name". The three attributes that are exactly the same are grouped into the same batch. The production urgency of each batch must meet the upper and lower limits of the production urgency of the steel coils in the batch;

[0038] Step 3: Select the batch group with the highest production urgency from all batch groups according to the rules; the selection rules are executed in the following order:

[0039] Select the steel coils in the bottleneck process that meet the order recorded in step 1;

[0040] Select the batch group that contains the steel coils of the process before the bottleneck process and has a batch group;

[0041] Select the batch group with a specified generation time and the equipment end time that matches this time;

[0042] When there are multiple batch groups that meet the conditions in a rule, the batch group with the earliest average start time is selected first. If there is no time window constraint, the batch group that meets the upper and lower limit requirements of the batch group and has the lowest average scheduling comfort rate is selected;

[0043] Step 4: Allocate the batch group selected in step 3 to the equipment with the highest preference. If the equipment has idle capacity, allocate the batch group selected in step 3 to the corresponding equipment. Otherwise, select the suboptimal equipment.

[0044] Step 5: Calculate the relevant time parameters for the newly added batch materials on the equipment, adjust the positions of the steel coils that do not meet the time window, directly allocate the steel coils with suitable positions to the corresponding equipment, and directly put the steel coils without suitable positions into the contract pool. At the same time, update the information of the last steel coil of all equipment in the current state to connect with the subsequent steel coils to be scheduled.

[0045] Step 6: Delete the steel coils scheduled into the plan in the original order contract pool, update the earliest production time of the same steel coil number in the next process, and add the output steel coils to the batch of the corresponding process in the contract pool;

[0046] Step 7: Check whether the remaining number of steel coils in the order contract pool is zero. If it is greater than zero, return to step 3; if it is zero, end the scheduling work; repeat the above operations. In the above process, when the steel coils are processed and unloaded from the equipment, the program automatically updates the existing order contract pool and allocates the produced steel coils to the existing batch classification until all steel coils are included in the plan or the production capacity is fully utilized, then the scheduling work is ended.

[0047] Step 8: Assess the completion of the completed scheduling work.

[0048] The total target KPI of this method consists of 4 parts, each part corresponds to a sub-KPI value, and the total target KPI is summed up by weighting coefficients of each sub-indicator: KPI = 0.4*KPIt+0.2*KPIc+0.2*KPIa+0.2*KPIh. The higher the total target KPI value, the better the scheduling result.

[0049] Order contract punctuality rate KPIt: the ratio of the number of steel coils scheduled to the total number of steel coils ordered on the day. The higher the ratio, the higher the order punctuality rate.

[0050] Equipment scheduling comfort rate KPIc: the sum of the penalties caused by jumps in width, thickness and temperature of adjacent steel coils in the model. The higher the penalty, the lower the equipment scheduling comfort rate.

[0051] Equipment utilization rate KPIa: The ratio of the equipment's processing time for steel coils to the equipment's theoretical maximum production capacity time is taken as the equipment utilization rate.

[0052] Equipment preference hit rate KPIh: After the units under different processes are debugged and put into production, the product quality of steel coils with different specifications is different. By labeling the equipment, different equipment preferences are formed. Equipment preference hit means that the specifications of the steel coils to be produced are arranged exactly to the equipment with the corresponding label preference.

[0053] An electronic device includes a memory, processors that are communicatively connected to each other, the memory storing computer instructions, and the processor executing the above-mentioned dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines by executing the computer instructions.

[0054] A computer storage medium having computer instructions stored thereon, wherein the computer readable storage medium is used to enable a computer to execute the above-mentioned dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines.

[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] The above-described embodiment has described the technical solution of the present invention in detail. It should be noted that this embodiment is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications and improvements made within the scope of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines, characterized in that: The following steps are involved: Step 1: Preprocess the data: Sort the steel coils in the contract pool and record the order information for subsequent scheduling reference; obtain the steel coil information at the end of all equipment in the current state; Step 2: Divide all steel coils into batches according to the three attributes of "Batch Number", "Equipment Preference" and "Process Name". Coils with the same three attributes are grouped into the same batch. The production urgency of each batch must meet the upper and lower limits of the production urgency of the steel coils in the batch; Step 3: Select the batch group with the highest production urgency from all batch groups according to the rules; Step 4: Assign the batch group selected in step 3 to the equipment with the highest "Equipment Preference" of the corresponding "Process Name". If the equipment has idle capacity, assign the batch group selected in step 3 to the corresponding equipment. Otherwise, select the suboptimal equipment. Step 5: Delete the "batch number" of the steel coils scheduled in the original order contract pool, update the earliest production time of the same steel coil "batch number" in the next process, add the output steel coils to the batch of the corresponding process in the contract pool, and update the steel coil information at the end of all equipment in the current state to connect the subsequent steel coils to be scheduled; Step 6: Check whether the remaining number of steel coils in the order contract pool is zero. If it is greater than zero, return to step 3; if it is zero, end the scheduling work; Step 7: Assess the completion of the completed scheduling work, and use the assessment results as a reference for the next scheduling.

2. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: The specific selection rules in step 3 are executed in order as follows: Select the steel coils in the bottleneck process that meet the order recorded in step 1; Select the batch group that contains the steel coils of the process before the bottleneck process and has a batch group; Selects batch groups that have a specified generation time and whose equipment end time matches that time.

3. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: In step 3, when there are multiple batch groups that meet the conditions in a rule, the batch group with the earliest average start time is selected first. If there is no time window constraint, the batch group that meets the upper and lower limit requirements of the batch group and has the lowest average scheduling comfort rate is selected.

4. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: After completing step 3, for the steel coils newly placed in the multi-process and multi-production lines, the sorting position of the steel coils is adjusted according to the relevant time parameters. The steel coils with suitable positions are directly allocated to the corresponding equipment, and the steel coils without suitable positions are directly placed in the contract pool.

5. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: The equipment with the highest preference in step 4 refers to the equipment with the highest average equipment preference of the steel coils in the batch group.

6. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: The overall target KPI for the completion of the scheduling work described in step 7 is summed up by weighting each sub-item indicator: KPI = 0.4*KPIt+0.2*KPIc+0.2*KPIa+0.2*KPIh. The higher the overall target KPI value, the better the scheduling result. The sub-item indicators are: Order contract punctuality rate KPIt: the ratio of the number of steel coils scheduled to the total number of steel coils ordered on the day. The higher the ratio, the higher the order punctuality rate; Equipment scheduling comfort rate KPIc: the sum of the penalties caused by the width, thickness and temperature jumps of adjacent steel coils in the model. The higher the penalty, the lower the equipment scheduling comfort rate; Equipment utilization KPIa: The ratio of the equipment's processing time for steel coils to the equipment's theoretical maximum capacity time is the equipment utilization rate; Equipment preference hit rate KPIh: After the units under different processes are debugged and put into production, the product quality of steel coils with different specifications is different. By labeling the equipment, different equipment preferences are formed. Equipment preference hit means that the specifications of the steel coils to be produced are arranged exactly to the equipment with the corresponding label preference.

7. The method for dynamic scheduling of a steel coil cold rolling workshop based on multiple processes and multiple production lines according to claim 1, characterized in that: The tail coil information in step 1 includes the time window data of the coil loading start time, transportation waiting time, and unloading end time, as well as the coil specification parameter values ​​such as thickness, width, and temperature.

8. An electronic device, comprising a memory, wherein the processors are communicatively connected to each other, wherein the memory stores computer instructions, and wherein the processor executes the dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines as described in any one of claims 1 to 7 by executing the computer instructions.

9. A computer storage medium, wherein the computer readable storage medium stores computer instructions, wherein the computer instructions are used to enable a computer to execute the dynamic scheduling method for a steel coil cold rolling workshop based on multiple processes and multiple production lines as described in any one of claims 1 to 7.

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