Collaborative cloud production scheduling method and system based on deep reinforcement learning

Abstract

The invention relates to the technical field of production plan scheduling, in particular to a collaborative cloud production scheduling method and system based on deep reinforcement learning. The method comprises the following steps: a client inputs scheduling demand information to a scheduling decision module; edge equipment acquires production state data of each production line in an intelligent workshop in real time, and uploads the production state data to an edge cloud for primary processing of the data; the edge cloud uploads the primarily processed data to a core cloud for data fusion; the scheduling decision module receives the fusion data and the scheduling demand information, and generates a scheduling strategy corresponding to each production line by using a deep reinforcement learning algorithm; the scheduling decision module transmits scheduling strategy information back to the edge cloud, and the edge cloud realizes production scheduling control on machines of each production line; and in a production process, an abnormal event monitoring module monitors abnormal data in the production process of the intelligent workshop in real time and gives an alarm feedback. According to the invention, the problem that current workshop production scheduling efficiency is low can be solved.

Description

technical field [0001] The invention relates to the technical field of production planning and scheduling (scheduling), in particular to a collaborative cloud scheduling method and system based on deep reinforcement learning. Background technique [0002] The goal of the production scheduling problem is to determine the processing order of each job in an order on each machine, so as to determine the start time and end time of the processing task of each job corresponding to the machine processing, so as to optimize the indicators for measuring production scheduling performance. For example: on-time delivery rate, average process time, work-in-progress quantity, machine and staff idle time. In actual factories, engineers mostly adopt some basic scheduling rules (work assignment rules) based on their previous experience or according to production conditions, for example, the first-in-first-out method, that is, the first-arriving order products are processed first, and Determi...

AI Technical Summary

Problems solved by technology

[0004] In view of the above defects, the purpose of the present invention is to propose a collaborative cloud production scheduling method and system based on deep reinforcement learning to solve the problem of low efficiency of current production scheduling

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