Abstract
The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multi-objective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
