Hybrid flow shop scheduling method based on time sequence difference

Abstract

The invention discloses a deep reinforcement learning algorithm based on time sequence difference, which is used for solving a hybrid flow shop scheduling problem of a related parallel machine, combines a convolutional neural network with TD learning in reinforcement learning, and performs behavior selection according to input state features; and therefore, the scheduling decision process of the actual order response type production and manufacturing system is better met. A scheduling problem is converted into a multi-stage decision problem, a convolutional neural network model is used to fit a state value function, manufacturing system processing state characteristic data is input into a model, a time sequence difference method is used to train the model, a heuristic algorithm or an allocation rule is used as a scheduling decision candidate behavior, and a reinforcement learning reward and punishment mechanism is combined, thus selecting an optimal combined behavior strategy for each scheduling decision. Compared with the prior art, the algorithm provided by the invention has the advantages of strong real-time performance, high flexibility and the like.

Description

technical field [0001] The invention belongs to the scheduling control technology of mixed flow shop, in particular to a scheduling method of mixed flow shop based on time sequence difference. Background technique [0002] Hybrid flow-shop scheduling problem (Hybrid flow-shop scheduling problem, HFSP), also known as flexible flow-shop scheduling problem, was first proposed by Salvador in 1973. This problem can be regarded as a combination of classic flow-shop scheduling problem and parallel machine scheduling problem. , which is characterized in that there is a parallel machine stage in the processing of the workpiece, and the machine allocation is carried out while the processing sequence of the workpiece is determined. In the HFSP problem, the number of processors in at least one stage is greater than 1, which greatly increases the difficulty of solving HFSP. It has been proved that the two-stage HFSP with 2 and 1 processors is an NP-hard problem. [0003] At present, exa...

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Problems solved by technology

[0005] (1) The traditional scheduling algorithm cannot effectively use historical data for learning, and its real-time performance is poor, so it is difficult to cope with the large-scale, complex and changeable actual production scheduling environment

[0006] (2) At present, although the research on traditional HFSP is very mature, there are few studies on the use of reinforcement learning to solve the mixed flow shop problem, and there are problems such as difficult to characterize the processing environment and limited functions of the function generalizer

[0007] (3) The deep reinforcement learning algorithm can solve the problem of limited functions of the function generalizer. The weight sharing strategy of the convolutional neural network reduces the parameters that need to be trained. The same weight allows the filter to detect the signal without being affected by the position of the signal. The characteristics of the trained model make the generalization ability of the trained model stronger, but there are few researches on the deep reinforcement learning algorithm to solve the workshop scheduling problem at home and abroad.

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