A dynamic scheduling optimization method for flexible job shop insertion orders

Abstract

An optimization method for dynamic scheduling of insertion orders in a flexible job shop, that is, a solution to the problem of delay caused by insertion of orders in dynamic batch scheduling of workshops. Based on the mathematical model of task sequence optimization and single-batch allocation, this method studies the strategy of batch selection and obtains a reasonable number of sub-batches by means of example simulation. At the same time, according to the simulation calculation of typical examples, The recommended value of the batch quantity is given, followed by the three-layer gene chromosome based on the process, machine, and order quantity, with the minimum and maximum completion time and delay as the optimization goal; finally, a hybrid algorithm of particle swarm optimization and genetic algorithm is used to Improve the evolution speed of the number of sub-batches to the optimal direction, and effectively reduce the delay. This method performs well in reducing the delay period in the dynamic scheduling of the workshop, and for the traditional genetic algorithm, it has a significant improvement in the convergence speed and stability. At the same time, it fully combines the actual production status of the intelligent workshop. It has great application value in engineering.

Description

technical field [0001] The invention relates to the technical field of multi-objective optimization of flexible workshop scheduling, in particular to a genetic algorithm-based batch dynamic scheduling optimization method for flexible workshops. Background technique [0002] The dynamic scheduling problem of the flexible job shop has always been considered as one of the most difficult scheduling problems in the manufacturing system. Anticipated emergencies, such as rush order insertion, machine tool failure, etc., lead to many limitations in the actual application process of production, so many domestic and foreign scholars have been focusing on the research of dynamic scheduling, such as literature [ A,KaraslanF S.Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach[J].International Journal of Production Research,2017,55(11):3308-3325. Optimization of lead times and scheduling sequences. With the rapid development of enterprise intelli...

AI Technical Summary

Problems solved by technology

Although scholars have done a lot of research on the problem of batch scheduling in flexible workshops, there is no reasonable solution to the problem of increased delay in actual order insertion scheduling. A batch selection strategy is adopted to design a three-tier system based on process, machine, and order quantity. Gene chromosomes, combined with the particle swarm algorithm to update each generation of population individuals of the genetic algorithm, realize the quantity allocation of batch orders and the sequence optimization of scheduling tasks, while minimizing the delay period and improving production efficiency

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