Flexible job shop order insertion dynamic scheduling optimization method

Abstract

A flexible job shop order insertion dynamic scheduling optimization method is a solution method aiming at the delay problems caused by the order insertion in the job shop batch dynamic scheduling, andcomprises the steps of on the basis of establishing a mathematical model of the task sequence optimization and the order batch distribution, researching a batch selection strategy, adopting an example simulation mode to obtain the reasonable sub-batch number, at the same time, according to the simulation and calculation of the typical examples, giving a recommending value of the batch number; secondly, based on the three-layer gene chromosomes of the processes, the machines and the order distribution number, taking the minimum maximum time of completion and the delay period as the optimization targets; and finally, adopting a mixed algorithm of a particle swarm optimization algorithm and a genetic algorithm to improve the speed of evolution of the sub-batch number towards an optimal direction, thereby effectively reducing the tardiness quantity. The method is good at reducing the delay period in the job shop dynamic scheduling, and for the conventional genetic algorithm, enables the convergence speed and the stability to be improved substantially, at the same time, fully combines the actual production statuses of the intelligent job shops, greatly promotes the dynamic scheduling solution, and has the great application value in the 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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