Flow Shop Scheduling Models with Data Represented by Rough Sets

摘要

In reality, the processing times are often imprecise and this imprecision is critical for the scheduling procedure. THis research deals with flow shop scheduling in rough environment. In this type of scheduling problem, we employ the rough set to represent the data parameters. The job processing times are assumed to be rough variables, and the problem is to minimize the makespan. Three novel types of rough scheduling models are presented. A rough simulation-based genetic algorithm is designed to solve these models and its effectiveness is well illustrated by numerical experiments.