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教学科研人员

邵仲世
发布时间:2019-10-14     浏览量:   分享到:


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邵仲世
职称/职务:讲师
电话:
个人主页:https://www.researchgate.net/profile/Zhongshi_Shao
电子信箱:shaozhongshi@snnu.edu.cn
研究方向:智能优化理论及复杂生产过程的建模、优化与调度
办公地点: 文津楼3506


个人简介

邵仲世,博士,民盟盟员,中国仿真学会智能仿真优化与调度专委会委员,2019年获南京航空航天大学软件工程专业博士学位,同年受聘于英国正版365官方网站。主要研究领域为智能优化方法及复杂生产过程建模、调度与优化。先后主持国家自然科学基金青年项目、教育部人文社会科学研究项目、中国博士后科学基金面上项目、陕西省自然科学基础研究计划项目等。2021年入选西安市科协青年人才托举计划,入围202220232024年斯坦福大学“全球前2%顶尖科学家年度影响力榜单。在IEEE TEVCIEEE TETCIIEEE TASEKBSSWEVOESWAEAAICORASOCEO等国际权威期刊发表论文40余篇,担任IEEE TEVCIEEE TCYBIEEE TASEIEEE SMCIEEE TPDSIEEE TII30余本国际期刊审稿人;曾获工信创新二等奖学奖和三等奖学奖、甘肃省自然科学三等奖、江苏省高等学校科学技术研究成果三等奖、南京航空航天大学“群星创新”奖、ACM南京分会优秀博士论文。


主要学术论文

[1] Zhongshi Shao, Weishi. Shao*, Jianrui. Chen, Dechang Pi. MQL-MM: A Meta-Q-Learning-Based Multi-Objective Metaheuristic for Energy-Efficient Distributed Fuzzy Hybrid Blocking Flow-Shop Scheduling Problem. IEEE Transactions on Evolutionary Computation, 2024, 1-15.

[2] Zhongshi Shao, Weishi, Shao*, Dechang Pi. LS-HH: A Learning-Based Selection Hyper-Heuristic for Distributed Heterogeneous Hybrid Blocking Flow-Shop Scheduling. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(1), 111-127.

[3] Zhongshi Shao, Weishi Shao*, Jianrui Chen, Dechang Pi. A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time, Engineering Applications of Artificial Intelligence, 2024, 131, 107818.

[4] Zhongshi Shao, Weishi Shao*, Dechang Pi. Effective constructive heuristic and iterated greedy algorithm for distributed mixed blocking permutation flow-shop scheduling problem, Knowledge-Based Systems, 2021, 221, 106959.

[5] Zhongshi Shao, Weishi Shao*, Dechang Pi. Effective Constructive Heuristic and Metaheuristic for the Distributed Assembly Blocking Flow-shop Scheduling Problem. Applied Intelligence, 2020, 50, 4647-4669.

[6] Zhongshi Shao, Dechang Pi, Weishi Shao*. Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Expert Systems with Applications, 2020, 145, 113147.

[7] Zhongshi Shao, Weishi Shao*, Dechang Pi, Effective heuristics and metaheuristics for the distributed fuzzy blocking flow-shop scheduling problem, Swarm and Evolutionary Computation, 2020, 59, 100747.

[8] Shuizhen Xing, Zhongshi Shao*, Weishi Shao, Jianrui Chen, Dechang Pi. Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer. Computers & Industrial Engineering, 2024, 198, 110686.

[9] Zeyu Zhang, Zhongshi Shao*, Weishi Shao, Jianrui Chen, Dechang Pi. MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects. Swarm and Evolutionary Computation, 2024, 85, 101479.

[10] Weishi Shao, Zhongshi Shao*, Dechang Pi. A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling. Engineering Optimization, 2024, 1-28.

[11] Weishi Shao, Zhongshi Shao* and Dechang Pi. Lot Sizing and Scheduling Problem in Distributed Heterogeneous Hybrid Flow Shop and Learning-Driven Iterated Local Search Algorithm. IEEE Transactions on Automation Science and Engineering, 2024, 21(4), 1-16.

[12] Weishi Shao, Zhongshi Shao*, Dechang Pi. Modelling and optimization of distributed heterogeneous hybrid flow shop lot-streaming scheduling problem, Expert Systems with Applications, 2023, 214, 119151.

[13] Weishi Shao, Zhongshi Shao*, Dechang Pi. An Ant Colony Optimization Behavior-Based MOEA/D for Distributed Heterogeneous Hybrid Flow Shop Scheduling Problem Under Nonidentical Time-of-Use Electricity Tariffs. IEEE Transactions on Automation Science and Engineering, 2022, 19(4), 3379-3394.

[14] Weishi Shao, Zhongshi Shao*, Dechang Pi. A network memetic algorithm for energy and labor-aware distributed heterogeneous hybrid flow shop scheduling problem. Swarm and Evolutionary Computation, 2022, 75, 101190.

[15] Weishi Shao, Zhongshi Shao*, Dechang Pi. Multi-local search-based general variable neighborhood search for distributed flow shop scheduling in heterogeneous multi-factories. Applied Soft Computing, 2022, 125, 109138.

[16] Weishi Shao, Zhongshi Shao*, Dechang Pi. Effective constructive heuristics for distributed no-wait flexible flow shop scheduling problem. Computers & Operations Research, 2021, 136, 105482.


主持科研项目

[1] 国家自然科学基金青年项目(620032032021.1-2023.12,主持

[2] 教育部人文社会科学研究西部和边疆地区项目(24XJCZH015, 2025.1-2026.12,主持

[3] 中国博士后科学基金面上项目(2023M732166, 2023.6-2025.5,主持

[4] 陕西省自然科学基础研究计划青年项目(2020JQ-425, 2020.1-2021.12,主持

[5] 西安市科协青年人才托举计划项目(095920211321, 2021.5-2023.5,主持


科研获奖

[1] 甘肃省自然科学三等奖(排名第四)

[2] 江苏省高等学校科学技术研究成果三等奖(排名第二)

[3] ACM南京分会优博论文奖


教学获奖

英国正版365官方网站第15届青年教师教学基本功大赛理科组优秀奖 


参编教材

雷丽晖, 李鹏, 邵仲世, 雷鸣, 王奇超. 大学计算机基础教程(Windows 10+Office 2019),科学出版社, ISBN: 978-7-03-073328-3.