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Steel production optimization scheduling method based on greedy strategy

A technology for optimizing scheduling and greedy strategy, applied in biological models, instruments, computing models, etc., can solve problems such as different workpiece process routes, multiple idle periods of production lines, and impact on enterprise production efficiency.

Pending Publication Date: 2021-04-20
成都博智云创科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 2. Different types of workpieces have different process routes
[0006] 3. Due to the constraints of the process, the production line usually has more idle time
This kind of uncertainty will cause fluctuations in real production capacity in the case of a large flow of workers in the steel industry, which will affect the production efficiency of enterprises.

Method used

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  • Steel production optimization scheduling method based on greedy strategy
  • Steel production optimization scheduling method based on greedy strategy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] A method for optimal scheduling of steel production based on a greedy strategy, comprising the following steps:

[0045] S1: Obtain the total production task from the production purchase and sales contract, and then decompose each production task into several production sub-tasks according to the production process;

[0046] S2: According to the relevant information of the workshop production line, generate a production line information table, and each production line completes a process;

[0047] S3: Create a structure array T and a process information array W, the structure array T is a set of decomposed production subtasks, the process information array W stores process information, and sequentially associate the production subtasks with the corresponding production subtasks according to the processing sequence Establish connection with the process information to find the predecessor of the production subtask;

[0048] S4: Using the greedy algorithm, the production ...

Embodiment 2

[0052] This embodiment is on the basis of embodiment 1:

[0053] The antecedent is the object to be processed of the production subtask, and the process information corresponding to the first subtask of the production task is no antecedent.

[0054] The information stored in the production line information table includes the procedures, task sequences and processing time records corresponding to each production line.

[0055] In the S3 step, the information stored in the structure array T includes the corresponding production line, processing time, front parts and delivery date; the information stored in the process information array W includes the processing status of the production subtask and the completed processing time.

Embodiment 3

[0057] This embodiment is on the basis of embodiment 1:

[0058] Described greedy algorithm comprises the following steps:

[0059] S41: sort all production subtasks of the total production task in order of delivery date, if the delivery date is the same, sort in descending order of processing time;

[0060] If there are 2 production tasks and each production task has 3 production subtasks, then:

[0061] The production subtasks 1, 2, and 3 of task 1 are filled in T[1], T[2], and T[3] respectively, and the corresponding process information is: W[1], W[2], W[3] ].

[0062] The production subtasks 1, 2, and 3 of task 2 are respectively filled in T[4], T[5], and T[6], and the corresponding process information is: W[4], W[5], and W[6] ].

[0063] First sort by delivery date, the subtasks of task 1 must be arranged together, and then sort each subtask in descending order of processing time to extract the current subtask.

[0064] S42: Collect the sorted production subtasks seq...

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PUM

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Abstract

The invention provides a steel production optimization scheduling method based on a greedy strategy, and the method comprises the following steps: S1, obtaining a total production task from a production purchase and sale contract, and decomposing each production task into a plurality of production subtasks according to a production process; S2, generating a production line information table according to the relevant information of workshop production lines, wherein each production line completes one procedure; S3, creating a structural body array T and a process information array W, wherein the structural body array T is a set of decomposed production sub-tasks, the process information array W stores process information, and the production sub-tasks are sequentially associated with the corresponding process information according to the machining sequence to be used for searching for precedents of the production sub-tasks; and S4, adopting a greedy algorithm, and preferentially selecting the production sub-tasks with the highest production line utilization efficiency to be scheduled to each production line.

Description

technical field [0001] The invention belongs to the technical field of scheduling optimization for steel processing production, relates to a job shop scheduling problem, and specifically relates to a greedy strategy-based optimal scheduling method for steel production. Background technique [0002] Steel is a material of various shapes, sizes and properties required by pressure processing such as steel ingots and billets. It is widely used and has a variety of varieties. With the continuous development of my country's iron and steel industry, iron and steel products are gradually personalized and diversified. However, in the current discrete production mode, there are many production processes, and the information in the processing process is complex and difficult to control, which can easily cause problems such as low economic benefits. How to arrange a large number of steel processing tasks under production constraints is an important means for enterprises to improve prod...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N3/00
Inventor 杨博陈晓亮
Owner 成都博智云创科技有限公司