Intelligent optimization and scheduling method for clothing production scheduling

By constructing a multi-source state data model and dynamic weight evaluation mechanism for the garment production site, the multi-objective optimization problem of the garment production scheduling system in a complex dynamic environment was solved, realizing adaptive optimization and stability improvement of the scheduling strategy, and improving production efficiency and resource utilization.

CN122155151APending Publication Date: 2026-06-05GUANGDONG JINDING ZHIZAO GARMENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGDONG JINDING ZHIZAO GARMENT TECH CO LTD
Filing Date
2026-01-20
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing garment production scheduling systems struggle to adapt to the complex and dynamic changes in the production workshop when faced with multi-objective optimization demands. This leads to sluggish weight adjustment and unreasonable allocation of objective weights, affecting production efficiency and costs. In particular, they struggle to achieve overall optimal decision-making when there are conflicts in equipment resources, urgent orders, or fluctuations in production capacity.

Method used

By acquiring multi-source real-time status data from the garment production site, a dynamic evaluation model for target priorities is constructed. Combining a fuzzy rule base and a sliding window smoothing algorithm, a target weight sequence with enhanced stability is generated and injected into a multi-objective genetic algorithm to achieve adaptive optimization and feedback tuning of the production scheduling strategy, forming a closed-loop self-optimization mechanism.

Benefits of technology

It enables real-time adaptive adjustment of multi-objective weights during the production scheduling process, improving the responsiveness and robustness of scheduling decisions, reducing reliance on external experience, ensuring the stability and consistency of scheduling strategies, adapting to complex dynamic environments, and improving production efficiency and resource utilization.

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Abstract

The application provides an intelligent optimization and scheduling method for garment production scheduling, which collects multiple source state parameters such as equipment utilization rate, order delay risk, material matching rate and team load balancing through the Internet of Things network in real time, forms a production feature vector after normalization and space-time alignment, inputs a target priority dynamic evaluation model based on fuzzy rules and reinforcement learning, dynamically assigns weights according to optimization targets such as yield under production conditions, resource utilization rate and line change cost, performs smoothing processing through a sliding window algorithm, guides a multi-objective genetic algorithm to generate and screen a production scheduling scheme, and iterates a self-optimizing model through a feedback mechanism. The application can realize real-time adjustment of target priority driven by production data, effectively improve the adaptability of production scheduling strategy, the efficiency of resource allocation and the robustness of process.
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