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Zohreh Davarzani,Soheila Staji,Fahimeh Dabaghi-Zarandi 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12
This article deals with solving the flexible job shop scheduling problem in dynamic environment (DFJSSP). In this problem, environment may face with many real time events such as random arrival of jobs or breakdown machine efficiency. Jobs and their operations are processing the machines according to static scheduling in which environment might face with such events. Regarding being NP – hard of the problem , a hybrid of artificial immune and virus evolutionary algorithm are offered to solve it which use the technique of stable action – reaction scheduling . In these algorithms two objective functions are minimized: Efficiency and stability. Efficiency is the objectives value in static scheduling, whereas stability is presented in dynamic scheduling because its purpose is to improve static scheduling, reduce the deviation from the first scheduling, and increase system stability.
Sima Vosoghi Asl,Zohreh Davarzani,Soheila Staji 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11
This article examines navigation of a flying robot inside a building environment in three dimensional spaces in which the size and location of some obstacles are not determined and other obstacles and target can be moving. This article suggests a new method by combining Q-learning algorithm and Monte Carlo algorithm on optimal navigation by the flying robot. The rewards are intended to be maximized when the robot flies in the right route; moreover, the maximum performance power would be measured according to the future predictions and the well-doing of that action would be also measured. Here, this method has been implemented with Webots simulator, and simulated data are analyzed by MATLAB. The simulation results show that control of the policy obtained from Q-learning and Monte Carlo methods is more efficient compared to traditional methods in controlling flying robot navigation.