Production line unit task allocation method and system based on reinforcement learning

By using a reinforcement learning-based production line unit task allocation method, real-time monitoring of orders and equipment status, and combining genetic algorithms and particle swarm optimization, the problem of multi-objective balance in production line task allocation is solved, achieving efficient and flexible task allocation, improving production efficiency and equipment utilization, and reducing energy consumption and costs.

CN121504043APending Publication Date: 2026-02-10DALIAN UNIV OF TECH +1
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Patent Information

Application Number
CN202511677464.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-17
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing production line task allocation methods struggle to balance task completion time, equipment utilization, energy consumption, and changeover costs in dynamic environments, leading to resource waste and inefficiency, and an inability to quickly respond to multidimensional constraints and changes in order priorities.

Method used

A reinforcement learning-based task allocation method for production line units is adopted. Through data acquisition, multi-objective function calculation, particle swarm optimization, and adaptive adjustment mechanism, order demand and equipment status are monitored in real time to generate a highly adaptive task allocation scheme. Combined with genetic algorithm and particle swarm optimization algorithm, multi-objective dynamic balance is achieved.

Benefits of technology

It improves production efficiency and equipment utilization, reduces energy consumption and production costs, enhances system flexibility and scalability, and can quickly respond to order priority fluctuations and equipment failures in highly dynamic environments, ensuring the real-time nature and effectiveness of task allocation schemes.

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Abstract

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.
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