Workshop production scheduling method and system based on genetic algorithm

A genetic algorithm and workshop technology, applied in the field of workshop production scheduling management, can solve the problems of relying on manual production scheduling, lack of consideration of the overall balance of the production line, and single target.

Active Publication Date: 2019-10-25
NANJING UNIV
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AI Technical Summary

Problems solved by technology

[0004] At present, there are some research schemes that try to solve the problem of dynamic production scheduling, but these research results are often not suitable for the actual production, and have the following shortcomings: 1) The linearity of the production line is not considered, and the machines in actual production have a certain degree of correlation. The machines are connected to a production line. Once a product is placed on the production line, it must pass through all the machines on this line. However, most of the existing solutions only regard the machines as scattered point sets, instead of taking the production line as the basis. 2) The objective function basically only focuses on the minimiz

Method used

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  • Workshop production scheduling method and system based on genetic algorithm
  • Workshop production scheduling method and system based on genetic algorithm
  • Workshop production scheduling method and system based on genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0128] to combine figure 2 As shown, Embodiment 1 is the main steps of the genetic algorithm-based scheme generation module, and Embodiment 2 to Example 4 are used as supplementary descriptions of Embodiment 1, specifically including:

[0129] enter:

[0130] 1. The maximum iteration number N of genetic algorithm operation;

[0131] 2. The serial number and quantity of the products to be produced contained in each order to be produced, the process that needs to be completed to produce each product, and the priority of each order to be produced (output by the order processing module);

[0132] 3. The time required for each process in each production line;

[0133] 4. Time required for line change t ex .

[0134] Output: The final population coding scheme after the iteration of the genetic algorithm is completed.

[0135] The specific implementation steps of the scheme generation based on genetic algorithm include:

[0136] S21. In the case of satisfying the constraint co...

specific example

[0171] First arrange all the unit tasks that need to be completed: the unit tasks are arranged in ascending order of the serial numbers of the corresponding products; when the product serial numbers are the same, they are arranged in ascending order of the process sequence of the products. Note that the total number of unit tasks is J.

[0172] Then use a two-row matrix to represent the production scheduling plan, the dth column corresponds to the dth unit task, the value of the first row is the number of the production line that completed the unit task, and the value of the second row is the number of the unit task on the production line on the production sequence. The matrix is ​​in one-to-one correspondence with the production scheduling plan, that is, it can be seen from the matrix what the production scheduling plan is.

[0173] The following example illustrates the encoding scheme:

[0174] The required products and their production processes are shown in Table 3 below...

Embodiment 3

[0185] Here, for the decoding of the encoding scheme (that is, the encoding matrix E) involved in step S22 in the first embodiment, a matrix F including the production process sequence and its completion time on the production line is obtained. Now combined with specific examples to illustrate.

[0186] Combined with the required production products and their production process tables, the production process sequences of each production line are obtained. According to the obtained sequence of procedures, solve each minimum value. The result of the solution is represented by a matrix F that includes the sequence of processes produced on the production line and its completion time. Each element in the matrix records the completion time. If the production line does not work after k processes, then the values ​​of the elements behind the row It is the moment when the last process is completed.

[0187] Decoding the encoding matrix E to obtain the matrix F, the conversion proce...

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Abstract

The invention discloses a workshop production scheduling method based on a genetic algorithm, and the method comprises the steps: constructing and training a decision tree model based on labeled orderdata, and inputting to-be-produced order data into the trained decision tree model to obtain the priority of a to-be-produced order; based on comprehensive consideration of a genetic algorithm, constraint conditions, encoding and decoding, fitness calculation and the like, finding an optimal production scheduling plan and finally, presenting a specified number of encoding schemes to production scheduling personnel in a graphical interface form for selection. Furthermore, the invention also discloses a workshop production scheduling system based on the genetic algorithm corresponding to the workshop production scheduling method. In the process of using the genetic algorithm to participate in decision making, the factors such as production efficiency, overall balance of the production line,multi-objective optimization and possible emergencies are comprehensively considered, the production efficiency is improved, the balance of the production line is realized, and the maximization of enterprise benefits is facilitated.

Description

technical field [0001] The invention belongs to the technical field of workshop production scheduling plan management, and in particular relates to a production scheduling method and system based on a genetic algorithm. Background technique [0002] At present, the scheduling in actual production mostly uses the static method of ERP (Enterprise Resource Planning) system, and the production scheduling plan is compiled according to the requirements of the order and the supply of material capacity. However, the actual production situation is often inconsistent with expectations. The ERP system cannot collect production data in real time, nor can it effectively schedule and configure production resources, resulting in waste of time and resources and increased production costs. [0003] In the research of some enterprises, we found that many enterprises have not yet paid attention to dynamic production scheduling. Enterprises often do not have strict requirements for production s...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06N3/12
CPCG06Q10/04G06Q10/06313G06Q10/06312G06Q10/06316G06Q50/04G06N3/126Y02P90/30
Inventor 窦蓉蓉黄秉焜骆靓川曾祥薇郑天烨笪郁文庄建军葛中芹
Owner NANJING UNIV
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