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Improved multi-target Jaya algorithm based discrete manufacture workshop production arranging method

A discrete manufacturing workshop, multi-target technology, applied in the field of intelligent manufacturing, can solve the problems of low production efficiency, low accuracy, poor energy saving and emission reduction effect, etc.

Active Publication Date: 2019-06-28
无锡思睿特智能科技有限公司
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Problems solved by technology

[0005] Aiming at the shortcomings of the existing discrete manufacturing workshop production planning and scheduling methods with low accuracy, unreasonable production scheduling scheme, poor energy saving and emission reduction effect, and low production efficiency, the present invention provides a discrete manufacturing based on the improved multi-objective Jaya algorithm. The production scheduling method in the workshop has high accuracy, effectively improves the rationality of production planning and production efficiency, and has a good effect on energy saving and emission reduction

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  • Improved multi-target Jaya algorithm based discrete manufacture workshop production arranging method

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Embodiment Construction

[0065] Below in conjunction with accompanying drawing, the present invention is further described:

[0066] like Figures 1~8 As shown, the present invention provides a method for scheduling production in a discrete manufacturing workshop based on the improved multi-objective Jaya algorithm, which includes the following steps, as figure 1 shown,

[0067] S1: Monitor the real-time status of the discrete manufacturing workshop through the Internet of Things system and collect data; for example, an IoT system based on RFID technology can be used to monitor the real-time status and process data.

[0068] S2: Preprocess the collected data and capture information about abnormal conditions to obtain effective workshop data.

[0069] S3: Use the abnormal event database to match the effective workshop data, analyze and judge whether the abnormal situation affects the processing time of the workpiece, and if the abnormal situation affects the processing time of the workpiece, input th...

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Abstract

The invention provides an improved multi-target Jaya algorithm based discrete manufacture workshop production arranging method. The method comprises the following steps that real-time state of the discrete manufacture workshops is monitored via the IoT (Internet of Things) system, data is processed, influence of abnormal condition on workpiece processing time is analyzed and determined, if the abnormal condition has influence on the workpiece processing time, information of the abnormal condition is input to the production arranging system, the production arranging system aims at maximizing the completion time and minimizing the workshop carbon emission, a statistical mathematical optimization model taking the machine tool as unit and the improved multi-target Jaya algorithm are used to optimize calculation, and an optimal production arranging scheme is obtained. The method has the advantages that the accuracy is high, production planning is more reasonable and efficiency, and the energy saving and emission reducing effect is good.

Description

technical field [0001] The invention relates to the technical field of intelligent manufacturing, in particular to a discrete manufacturing workshop scheduling method based on an improved multi-objective Jaya algorithm. Background technique [0002] The machinery manufacturing industry provides technical equipment for the entire national economy, but it also consumes a lot of resources and energy and generates carbon emissions in the production process, which has an impact on the environment. For example, carbon emissions will not only affect climate change, but also cause ocean acidification and soil imbalance. Therefore, a reasonable production plan plays an important role in controlling energy consumption in production and processing, reducing carbon emissions, and realizing a green, low-carbon and sustainable production model. [0003] In discrete manufacturing workshops, the main factors affecting energy consumption and carbon emissions include machine tool energy consu...

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Application Information

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IPC IPC(8): G05B13/04
Inventor 吉卫喜蔡酉勇吉伟伟孙琳
Owner 无锡思睿特智能科技有限公司
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