A discrete job-shop scheduling algorithm based on improved genetic algorithm

Abstract

In a discrete job-shop, the scheduling objectives are often conflicting and constrained, and the actual production is disturbed by many uncertainties. This paper combines bi-directional scheduling with genetic algorithm (GA) to optimize the static scheduling results. Considering the dynamicity of the various emergencies in the discrete job-shop, a multi-level dynamic scheduling model was proposed based on rolling window. The model integrates the merits of periodic rescheduling and event-driven rescheduling to reduce the scheduling cost and mitigate the impact of disturbances, without sacrificing the stability and efficiency of production. Our method was verified through discrete event simulation.