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Method for adaptively and dynamically scheduling manufacturing systems

A technology for dynamic scheduling and manufacturing systems, applied in control/regulation systems, non-electric variable control, speed/acceleration control, etc., can solve the problem of ignoring the dynamic accumulation of learning experience

Active Publication Date: 2015-05-20
江苏金猫机器人科技有限公司
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AI Technical Summary

Problems solved by technology

However, in the learning process of its dynamic scheduling algorithm, the action search adopts a fixed parameter value greedy strategy, and its greedy parameter value has certain subjectivity and blindness, ignoring the dynamic accumulation of learning experience in the learning process

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  • Method for adaptively and dynamically scheduling manufacturing systems
  • Method for adaptively and dynamically scheduling manufacturing systems
  • Method for adaptively and dynamically scheduling manufacturing systems

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Embodiment Construction

[0044] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0045] The invention discloses a method for adaptive dynamic scheduling of a manufacturing system, which includes the following content:

[0046] 1. Design the dynamic scheduling objective function. The definition of symbols in the scheduling process is given as follows: the job shop set is expressed as J={J 1 ,J 2 ,…J N}; The set of processing equipment is M={M 1 , M 2 ,...M M}; Each job consists of multiple processes, O ij Indicates job J i The processing time of the j-th process of the same operation cannot be processed on the same equipment, and one equipment can only process one process in a certain period of time; the operations are independent of each other and have no priority, and the operation J i The actual completion time is C i , the arrival time is AT i , lead time D i The formula is as follows:

[0047] ...

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Abstract

The invention discloses a method for adaptively and dynamically scheduling manufacturing systems, and relates to the field of production scheduling for manufacturing systems. The method has the advantages that a multi-Agent-based dynamic production scheduling system is constructed for the uncertainty of production environments of the manufacturing systems; an improved Q-learning algorithm on the basis of cluster-dynamic search is provided in order to guarantee that appropriate bid winning operation can be selected by equipment according to current system states, and dynamic scheduling strategies can be adaptively selected in the uncertain production environments under the guidance of the improved Q-learning algorithm; system state dimensions are reduced by the dynamic scheduling strategies by the aid of sequence clusters, and learning is carried out according to state different degrees and dynamic greedy search strategies; the convergence and the complexity of the algorithm are analyzed, and the effectiveness and the adaptability of the method for adaptively and dynamically scheduling the manufacturing systems are verified by simulation experiments.

Description

technical field [0001] The invention relates to the field of production scheduling of a manufacturing system, in particular to an adaptive dynamic scheduling method of a manufacturing system. Background technique [0002] In recent years, optimal scheduling of efficient production or adaptive scheduling in uncertain and complex production environments is an important problem to be solved in manufacturing systems, and it is of great significance to realize adaptive dynamic scheduling in the face of dynamic production environments. The study of adaptive production scheduling in uncertain production environments is becoming an active research field. Aiming at the problem of complex and changeable dynamic scheduling constraints, some scholars have established a constraint linkage scheduling model and algorithm to achieve fast human-computer interaction dynamic scheduling, and proposed a random adaptive scheduling that dynamically selects the most suitable rules according to the ...

Claims

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Application Information

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IPC IPC(8): G05D13/04
Inventor 王玉芳宋莹陈逸菲叶小岭杨丽薛力红
Owner 江苏金猫机器人科技有限公司
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