Prof. Jingjing Wang（王静静）

School of Management Engineering, Qingdao University of Technology

Email: wangjingjing@qut.edu.cn

RESEARCH AREA AND TEAM

Prof. Wang mainly focuses on studying system reliability, stochastic processes, and maintenance strategy optimization. She graduated from Beijing Institute of Technology in 2020 with a doctorate degree in management science and engineering. From November 2017 to November 2018, she went to the University of Toronto to complete a joint training project for high-level doctoral students sponsored by the National Scholarship Council. Currently, she presided over one program of NSFC, and published 13 SCI retrieval papers in international top journals such as Renewable & Sustainable Energy Reviews, Renewable Energy, Computers & Industrial Engineering, and Reliability Engineering & System Safety, among which two was highly cited paper.

EMPLOYMENT HISTORY

07/2020–Present, Associate professor | Qingdao University of Technology, Qingdao, China

EDUCATION

09/2014–05/2020, Beijing Institute of Technology, Beijing, China, PhD in Management Science and Engineering

11/2017–11/2018, Visiting Student | University of Toronto, Canada

09/2010–06/2014, Shandong Jianzhu University, Jinan, China, Bachelor in Management Engineering Cost

CURRENT RESEARCH PROJECTS

[1] 72201148, National Natural Science Foundation of China, 2023-2025

PUBLICATIONS

[1] Wang J J , Zhao X*, Guo X X, Li B L. Analyzing the research subjects and hot topics of power system reliability through the Web of Science from 1991 to 2015[J]. Renewable & Sustainable Energy Reviews, 2018, 82: 700-713. (SCI, IF: 14.98, Q1)

[2] Wang J J, Zhao X*, Guo X X. Optimizing wind turbine’s maintenance policies under performance-based contract[J]. Renewable Energy, 2019, 135: 626-634. (SCI, IF: 8.001, Q1)

[3] Wang J J, Makis V*, Zhao X. Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system[J]. Computers & Industrial Engineering, 2019, 134: 1-10. (SCI, IF: 5.431, Q1)

[4] Wang J J, Qiu Q A*, Wang H H. Joint optimization of condition-based and age-based replacement policy and inventory policy for a two-unit series system. Reliability Engineering & System Safety, 2021, 205: 107251. (SCI, IF: 6.188, Q1)

[5] Wang J J*, Qiu Q A*, Wang H H, Lin C. Optimal condition-based preventive maintenance policy for balanced systems. Reliability Engineering & System Safety, 2021, 211: 107606. (SCI, IF: 6.188, Q1)

[6] Wang J J, Miao Y H*. Optimal preventive maintenance policy of the balanced system under the semi-Markov model. Reliability Engineering & System Safety, 2021, 213: 107690. (SCI, IF: 6.188, Q1)

[7] Qiu Q A, Liu B L, Lin C, Wang J J*. Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair. Proceedings of the iMeche, Part O: Journal of Risk and Reliability, 2021: 1-16. (SCI, IF: 2.61, Q2)

[8] Wang J J, Miao Y H*, Yi Y G, Huang D G. An imperfect age-based and condition-based opportunistic maintenance model for a two-unit series system. Computers & Industrial Engineering, 2021, 160: 107583. (SCI, IF: 5.431, Q1)

[9] Miao Y H, Wang J J*, Zhang B Y, Li H. Practical framework of Gini index in the application of machinery fault feature extraction. Mechanical Systems and Signal Processing, 2022, 165: 108333. (SCI, IF: 6.823, Q1)

[10] Wang J J, Wang Z X, Zheng R. Optimal inventory policy for a balanced system subject to hard failure. Reliability Engineering & System Safety, 2021, 216: 108015. (SCI, IF: 6.188, Q1)

[11] Wang J J, Yang L, Ma X B, Peng R. Joint optimization of multi-window maintenance and spare part provisioning policies for production systems. Reliability Engineering & System Safety, 2021, 216: 108006. (SCI, IF: 6.188, Q1)

[12] Wang J J, Zheng R, Lin T.R. Maintenance modeling for balanced systems subject to two competing failure modes. Reliability Engineering & System Safety, 2022, 225: 108637. (SCI, IF: 6.188, Q1).

[13] Zheng R, Wang J J, Zhang Y.Z. A hybrid repair-replacement policy in the proportional hazards model. European Journal of Operational Research, 2022, doi: 10.1016/j.ejor.2022.05.020.