Scheduling rule intelligent excavating method based on rule parameter global coordination optimization

Abstract

The invention relates to a scheduling rule intelligent excavating method based on rule parameter global coordination optimization, belonging to the automatic control, information technology and advanced manufacturing field, in particular relates to a complex production process oriented scheduling rule intelligent excavating method for scheduling environment in real time. The invention is characterized in that the method includes the following steps: a complex production process oriented scheduling rule intelligent excavating frame for scheduling environment in real time is built, a scheduling problem instance classification model is build, and scheduling rule parameter global coordination optimization problem is constructed and solved. The invention is based on the scheduling rule intelligent excavating frame provided by the invention and adopts double-layer fuzzy C-means clustering method to classify scheduling problem instances. Rule parameter global coordination optimization problem is constructed directing at scheduling problem instance in each class and linear partition based particle swarm optimization is adopted to solve and optimize the problem, wherein Bayes estimation method is adopted to carry out comprehensive evaluation on scheduling rule performance. The obtained scheduling rule has better scheduling effect on different problem instances in similar scheduling environment.

Description

technical field [0001] The invention belongs to the fields of automatic control, information technology and advanced manufacturing. Specifically, it relates to an intelligent mining method for dispatching rules oriented to a complex production process real-time dispatching environment. Background technique [0002] Production process scheduling has always been a research hotspot in academia and industry. In the past few decades, people have proposed many methods for solving production process scheduling problems, such as methods based on operations research, methods based on heuristics, methods based on soft computing, etc. The method based on scheduling rules has the characteristics of being able to incorporate human (experienced scheduler) knowledge, simple expression, and small amount of calculation, and has obvious advantages in real-time scheduling environments. However, for complex production process scheduling problems, it is difficult to obtain scheduling rules wit...

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Problems solved by technology

However, for complex production process scheduling problems, it is difficult to obtain scheduling rules with good scheduling performance and strong adaptability purely based on manual experience or scheduling expert knowledge. The proposed scheduling rule mining methods for production process scheduling problems are mainly based on Local data (local scheduling environment parameters or local scheduling decision parameters) of scheduling problem instances at different scheduling decision-making moments, and the performance evaluation of scheduling rules is only based on the relationship between attribute values ​​before and after scheduling rules, and the mining process does not consider the relationship between different machine groups. The coupling between the corresponding scheduling rules does not consider the requirements of the optimization goal of the scheduling problem, so the scheduling rules obtained based on this have the defects of unsatisfactory global performance and difficulty in adapting to different scheduling environments

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