祝贺2014级硕士湛荣鑫同学的论文 “A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity” 被国际权威期刊IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (影响因子：2.428) 录用。

湛荣鑫同学于2014年3月加入实验室进行本科毕业设计的工作，9月正式入学后在毕业设计基础上继续进行深入研究，并发表论文。希望各位本科同学向湛荣鑫同学学习，提前进入科研状态，早日获取优质的学术成果。

论文的摘要以及作者简介如下：

A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity

Abstract——The problem of intercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed in this paper, which in nature is the coordination of production and transportation. Since it is a practical decision-making problem with high complexity and large problem instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic selection, is developed in this paper. In order to increase the diversity and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of parts, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to choose appropriate rules for machines and vehicles. Finally, scheduling solutions are obtained using the selected rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the strategies proposed in HEH, with a meta-heuristic approach that is suitable for large scale scheduling problems, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the best-performing human-made rules, but are more effective due to the evolutionary processes in HEH. Moreover, the HEH approach has advantages over other approaches in both computational efficiency and solution quality, and is especially suitable for problems with large instance sizes.

Dongni Li received the B.S. and Ph.D. degrees in computer science from Northeastern University, Shenyang, China, in 2000 and 2005, respectively.

Since 2008, she has been an Associate Professor with the Beijing Key Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, where she was a lecturer from 2005 to 2007. From 2010 to 2011, she was a postdoctoral researcher with the School of Operations Research and Information Engineering, Cornell University, Ithaca, NY. Her research interests include intelligent decision-making approaches and their applications to the manufacturing industry.

Rongxin Zhan received the B.S. degree in computer science from Zhengzhou University, Henan, China, in 2014.

Since 2014, he has been a graduate student for the M.E. degree with the major of computer technology, Beijing Institute of Technology. His research interests include swarm intelligence and production scheduling.

Dan Zheng received the B.S. degree in computer science from Beijing Foreign Studies University, Beijing, China, in 2013.

Since 2013, she has been a graduate student for the M.S. degree with the School of Computer Science, Beijing Institute of Technology. Her research interests include optimization approaches and production scheduling.

Miao Li received the B.S. degree in computer science from Beijing Institute of Technology, Beijing, China, in 2013.

Since 2013, he has been a graduate student for the M.S. degree with the School of Computer Science, Beijing Institute of Technology. His research interests include heuristic-based optimization approaches.

Ikou Kaku is a professor of the Department of Environmental and Information Studies, Tokyo City University. His research interests include industrial engineering and management.

（审核：李冬妮）