Comprehensive multi-objective model to remote sensing data processing task scheduling problem

Author:

Xing Lining123ORCID,Li Wen4ORCID,He Minfan1,Tan Xu5

Affiliation:

1. School of Mathematics and Big Data; Foshan University; Foshan 528000 P.R. China

2. College of Engineering; Shanghai Polytechnic University; Shanghai 201209 P.R. China

3. College of Information Systems and Management; National University of Defense Technology; Changsha 410073 P.R. China

4. Business School of Hunan University; Changsha 410082 P. R. China

5. School of Software Engineering; Shenzhen Institute of Information Technology; Shenzhen 518172 P.R. China

Funder

National Natural Science Foundation of China

Program for New Century Excellent Talents in University, Foundation for the Author of National Excellent Doctoral Dissertation of PR China

Youth Training Program for Innovation and Entrepreneurship Platform of Science and Technology at Hunan Province, the Outstanding Youth Fund Project of Hunan Provincial Natural Science Foundation

Top-notch Innovative Talents Training Plan of National University of Defense Technology, the Outstanding Youth Fund Project of National University of Defense Technology

Fundamental Research Funds for the Central Universities

Shenzhen Basic Research Project for Development of Science and Technology

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference34 articles.

1. Algorithm and application to scheduling problem of agile earth observing satellites;Yao;Disaster Advances,2012

2. Integrative forest fire monitoring system framework design aim to early warning tasks;Li;Disaster Advances,2012

3. Mission planning for space-based disaster monitoring;Lian;Disaster Advances,2012

4. Integration solving framework for global navigation satellite system planning and scheduling problem;Lian;Disaster Advances,2013

5. A robust regularization path algorithm for ν-support vector classification;Gu;IEEE Trans Neural Netw Learn Syst,2016

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