Real-time job shop scheduling control method in complicated production environment

Abstract

The invention discloses a real-time job shop scheduling control method in a complicated production environment. The method comprises the following steps: S101, a job shop production environment multi-agent model is initialized; S102, scheduling triggering events are registered; S103, after any event in the step S102 is triggered, a plurality of workpiece agents are scheduled sequentially according to the real-time job shop state; and S104, as for each workpiece agent, after the scheduling is completed, if a cache task execution condition is met, the cache task is executed, or otherwise, the cache task execution condition is waited to be met. Thus, real-time and dynamic multi-workpiece scheduling in the complicated production environment can be effectively realized; and when production disturbance factors such as equipment fault and emergency orders are handled, the system operation efficiency can be effectively enhanced.

Description

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AI Technical Summary

Benefits of technology

This patented technology proposes a way to manage complicated manufacturing environments efficiently without overwhelming any staff members or resources needed during peak periods. It uses advanced technologies like Event Trigger (EST) triggered timings that schedule tasks based on their current status rather than just predicting future times. By doing this, it can reduce incorrect calculation due to unexpected events happening around them while still ensuring efficient operations are achieved. Additionally, Interactive Collaborative Multiagent Scheduler(ICMAS), which helps multiple agents collaborate more accurately towards scheduled goals, enhances overall performance even under challenging conditions. Overall, these technical improvements help streamline managing complex production systems better and make maintenance easier.

Problems solved by technology

This patented technical solution describes how companies face challenging tasks during their shipping times at large scales. These include adjustment of productivity levels based upon customer demand patterns that may change frequently over time; reconfiguration of products after they've been installed into an assembly line without causing delays caused by unexpected events like machine failures or repairs; and optimizing resource allocation within specific areas where customers can meet up quickly. However, these solutions require accurate data about each individual item being produced but still lack flexibility when dealing with complicated environments involving multiple items simultaneously.

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