个 人 信 息

姓名
张新功
性别
男
民族
汉

出生日期
政治面貌
中共党员
职称/职务
教授

毕业学校
上海理工大学
学历
博士
博导/硕导
硕导

学科专业
运筹学、管理科学与工程
研究方向
组合最优化、排序论

联系方式
zhangxingong@cqnu.edu.cn

个人简历

2002年9月—2005年6月 河南大学 数学与统计学院本科；
2005年9月—2008年6月 郑州大学数学系，运筹学与控制论 硕士；
2008年9月—2017年6月 上海理工大学 管理科学与工程，博士；
2011年7月至今重庆师范大学数学学院.

主要研究项目与科研奖励

主持的科研项目
1.重庆市教委重点项目:公平定价下的双代理新型排序问题研究，KJZD-K，20201；
2.重庆市教委研究生教改重点项目: 基于创新理念的系统理论研究生专业课程体系优化研究，yjg182019，201805-202104；
3.重庆市科委项目: 多代理竞争排序的新型排序研究，cstc2018jcyjAX0631, 2018；
4.重庆市教委项目: 与时间相关的若干机器排序问题研究, KJ**, 201607-201806；
5.国家自然科学基金青年基金: 基于退化效应或可控加工时间的竞争排序研究, **, 201501-201712；
6.重庆市自然科学基金: 学习或退化效应下的重新排序和竞争排序研究, cstc2014jcyjA00003, 2014-2016；
7.中国博士后基金资助项目: 基于退化效应或可控加工时间的竞争排序研究(2014T70854)
8.中国博士后基金面上项目: 学习或退化效应下重新排序和竞争排序研究(2013M540698)
9.国家自然科学基金天元专项基金: 重新排序问题及其研究，**，201301-201312；
10.重庆市教委项目: 现代排序模型研究，KJ120624，201201-201412；
11.上海市创新基金项目: 学习与退化效应下排序模型研究；(JWCXSL100)201001-201201;
12.重庆市高校首批精品在线开放课程(渝教高函〔2017〕53号)，重庆市教委；
13.重庆市级教改项目203298，基于在线开放课程资源的混合式课堂教学模式实践与探索---以高等代数为例.
参与项目
1.国家科学基金重大项目：最优化问题的人工智能方法**。子课题—组合优化的人工智能求解方法及应用**，子课题第二参与人，202001-202412；
2.国家自然科学基金面上项目：**机器具有指派费用的若干排序问题研究，2020.01-2023.12；
3.国家社科基金项目面上项目：18BKS151，新时代中国特色社会主义德育思想研究，2018.06-2021.06；
4.国家自然科学基金面上项目：**，同类机上的若干排序问题研究，2016/01-2019/12；
5.国家自然科学基金青年基金：**，忆阻神经网络建模分析与应用，2014/01-2016/12；

代表性成果

1.Parallel-machine scheduling with linear deteriorating jobs and preventive maintenance activities under a potential machine disruption, 2020, Computers & Industrial Engineering, 2020, 145, 106482.
2.Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function, Engineering Optimization, 2020，10.1080/**X.2020.**
3.Exact and heuristic algorithms for scheduling on two identical machines with early work minimization. Computers & Industrial Engineering, 2020, 144, 106449.
4.Single machine and flowshop scheduling problems with sum-of-processing time based learning phenomenon. Journal of Industrial and Management Optimization, 2020, 16(1): 231-244.
5.Heuristic and iterative greedy algorithms for the total weighted completion time order scheduling with release times. Swarm and Evolutionary Computation, 2019, 44: 913-926.
6.Tardiness minimization for a customer order scheduling problem with sum-of-processing-time-based learning effect. Journal of the Operational Research Society, 2019, 70:3, 487-501.
7.Competitive two-agents scheduling problems to minimize the weighted combination of makespan on two-machine open shop. Engineering Optimization, 2018, 50, 684-697.
8.Machine scheduling problems under deteriorating effects and deteriorating rate-modifying activities. Journal of the Operational Research Society. 2018, 69(3): 439-448.
9.Single-machine scheduling problems with a learning effect matrix. Iranian Journal of Science and Technology, Transactions A: Science, 2018, 42(3): 1327–1335.
10. Resource-constrained scheduling problems with general truncated sum-of-processing-time-dependent effect under single machine and unrelated parallel machines. Computers & Industrial Engineering, 2017, 110: 344–352.
11.Single-machine common/slack due window assignment problems with linear decreasing processing times. Engineering Optimization, 2017, 49(8): 1388–1400.
12.Two-agent scheduling problems on a single-machine to minimize the total weighted late work, Journal of Combinatorial Optimization, 2017, 33 (3): 945–955.
13.Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine. Engineering Optimization, 2017, 49, (1): 84-97.
14.Patients scheduling problems with deferred deteriorated functions. Journal of Combinatorial Optimization, 2015, 30(4): 1027-1041.
15.Single-machine scheduling CON/SLK due window assignment problems with sum-of-processed times based learning effect. Applied Mathematics and Computation, 2015, 250: 628-635.
16.Common due-window assignment and scheduling of job-dependent deteriorating jobs and multiple deteriorating maintenance activities. Asia-Pacific Journal of Operational Research. 2014, DOI: 10.1142/S02**043.
17.A note on machine scheduling with sum-of-logarithm-processing-time based and position-based learning effects .Information Sciences, 2012, 187: 298-304.
18.Single-machine scheduling problems with time-dependent and position-dependent learning effects. Annals of Operations Research, 2011, 186:345-356.
19.Single-machine scheduling problems with release time of jobs depending on resource allocated. International Journal of Advanced Manufacture Technology, 2011, 57:1175-1181.
20.Machine scheduling problems with a general learning effect. Mathematical and Computer Modeling, 2010, 51:84-90.
21.Single-machine group scheduling problems with deteriorated and learning effect. Applied Mathematics and Computation, 2010, 56:1259-1266.
22.Single-machine scheduling problems with a sum-of-processing time- based learning function. International Journal of Combinatorics, 2009, 624108.