Open AccessSolving Flexible Job Shop Scheduling Problem in Cloud Manufacturing Environment Based on Improved Genetic Algorithm
Author(s) -
Yunlong Li,
Guangchun Luo
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/612/4/042065
Subject(s) - cloud computing , computer science , job shop scheduling , flow shop scheduling , scheduling (production processes) , cloud manufacturing , job shop , idle , residual , distributed computing , fair share scheduling , industrial engineering , genetic algorithm , dynamic priority scheduling , genetic algorithm scheduling , mathematical optimization , algorithm , engineering , embedded system , mathematics , operating system , machine learning , schedule , routing (electronic design automation)
In this paper, aiming at the new characteristics of flexible job shop scheduling in cloud manufacturing environment, this paper studies the residual capacity utilization of cloud manufacturing enterprises, considers the idle time of equipment, studies the workshop scheduling method with idle time, and constructs the scheduling framework of cloud manufacturing operation workshop. The production tasks accepted on the platform are carried out together with the tasks assigned by the workshop itself, with the goal of minimum penalty total cost, and the improved genetic algorithm is used to solve the optimal scheduling sequence of the workpiece. The validity of the proposed method is verified by using the benchmark example.