Intelligent production line scheduling method and system

By constructing a multi-production line topology network and equipment status model, quantifying the cost of logistics transfer, and optimizing cross-production line scheduling plans, the problem of uneven utilization of equipment resources across multiple production lines was solved, achieving efficient utilization of equipment resources and reduced energy consumption.

CN122414698APending Publication Date: 2026-07-17SHAANXI SCI TECH UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHAANXI SCI TECH UNIV
Filing Date
2026-05-09
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing industrial internet systems struggle to fully utilize the temporal and state-change characteristics of equipment in multi-production line scheduling, leading to uneven utilization of equipment resources, concentrated order delivery pressure, and the potential introduction of additional logistics transfer costs and energy consumption for cross-production line scheduling.

Method used

By constructing a production line topology network across multiple production lines, we can capture alternative equipment and build schedulable paths, quantify the cost of logistics transfer, build a state model using historical equipment data, perform fitting training, and optimize the scheduling plan to minimize the total cost, thereby achieving cross-production line material scheduling.

Benefits of technology

It improved the utilization rate of equipment resources, reduced energy consumption and logistics transfer costs, optimized production efficiency, and achieved full utilization of equipment resources and overall collaborative efficiency.

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Abstract

The application discloses an intelligent production line scheduling method and system, relates to the technical field of industrial scheduling, and comprises the following steps: in a multi-production line topology network, alternative devices capable of executing the same process flow are identified, and a schedulable path is constructed; a dynamic evolution curve is generated based on historical single-run data of the devices, attention weights are allocated to samples in combination with evolution trends, and a quantitative relationship among single-run duration, processing efficiency and energy consumption intensity under a current state is fitted; a logistics transfer cost is converted to a unified scale comparable to device energy consumption, and under the constraints of order timeliness, material continuity and device capacity, a cross-production line material scheduling scheme is output by iteratively solving a target of minimizing the sum of total device energy consumption and total scheduling cost.
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