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I am currently an Associate Professor in the School of Management at Tianjin University of Commerce, China. I received my B.E. degree in Logistics Engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2011. I received the M.Sc. in Industrial Engineering and the Ph.D. degree in Management Science and Engineering (under the supervision of Prof. Zhen He) from Tianjin University, Tianjin, China, in 2016. I was a Visiting Scholar (under the supervision of Prof. Mengjie Zhang and Prof. Bing Xue) at the Evolutionary Computation Research Group (ECRG), Victoria University of Wellington (VUW), Wellington, New Zealand, in 2018.
May 8, 2023 - Our research paper that establishes a multi-objective PSO algorithm for Key Quality
Feature Identification in complex manfacuring processes is recently accepted and published
in Information Sciences. https://doi.org/10.1016/j.ins.2023.119062
Jun 3, 2023 - Our research paper proposing a probablity-based discrete PSO algorithm for product portfolio planning problem is recently accepted and published
in Soft Computing, a free view-only link is available at https://rdcu.be/ddHsU.
The doi link is https://doi.org/10.1016/j.ins.2023.119062
I focus on the research in Quality Engineering and Management using the methods in the Machine Learning and Computational Intelligence contexts (such as Feature Selection and Evolutionary Algorithms). My research interests include:
Key Quality Feature (or Process Variable) Identification
Quality Prediction and Control
Evolutionary Multi-objective Optimization (EMO)
Evolutionary Computation (EC)
Feature Selection (with EC techniques)
Robust Multi-response Optimization
* denotes the corresponding author
Journal Papers
Li, A.-D.*, Xue, B., & Zhang, M. (2023). Multi-objective particle swarm optimization for key quality feature selection
in complex manufacturing processes. Information Sciences, 641, 119062.
https://doi.org/10.1016/j.ins.2023.119062 [BibTeX]
[pdf] [Source Code]
Liu, X., & Li, A.-D.* (2023). An improved probability-based discrete particle swarm optimization
algorithm for solving the product portfolio planning problem. Soft Computing.
doi:10.1007/s00500-023-08530-0 [BibTeX]
[Source Code]
Li, A.-D., He, Z., & Zhang, Y.* (2022). Robust multi-response optimization considering location effect, dispersion effect,
and model uncertainty using hybridization of NSGA-II and direct multi-search. Computers & Industrial Engineering, 169, 108247.
doi:10.1016/j.cie.2022.108247 [BibTeX] [pdf]
[Source Code]
He, Z., Hu, H., Zhang, M., Zhang, Y., & Li, A.-D.* (2022). A decomposition-based multi-objective particle swarm optimization
algorithm with a local search strategy for key quality characteristic identification in production processes.
Computers & Industrial Engineering, 172, 108617.
doi:10.1016/j.cie.2022.108617 [BibTeX] [pdf]
Li, A.-D.*, Xue, B., & Zhang, M. (2021). Improved binary particle swarm optimization for feature selection
with new initialization and search space reduction strategies. Applied Soft Computing, 106, 107302.
doi:10.1016/j.asoc.2021.107302 [BibTeX] [pdf]
[Source Code]
Li, A.-D.*, & He, Z. (2020). Multiobjective feature selection for key quality characteristic identification
in production processes using a nondominated-sorting-based whale optimization algorithm. Computers & Industrial Engineering,
149, 106852. doi:10.1016/j.cie.2020.106852
[BibTeX] [pdf] [Source Code]
Li, A.-D.*, Xue, B., & Zhang, M. (2020). Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Information Sciences, 523, 245–265. doi:10.1016/j.ins.2020.03.032 [BibTeX] [pdf]
Li, A.-D.*, He, Z., Wang, Q., & Zhang, Y.* (2019). Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method. European Journal of Operational Research, 274(3), 978–989. doi:10.1016/j.ejor.2018.10.051 [BibTeX] [pdf]
Li, A.-D., He, Z.*, & Zhang, Y. (2016). Bi-objective variable selection for key quality characteristics selection based on a modified NSGA-II and the ideal point method. Computers in Industry, 82, 95–103. doi:10.1016/j.compind.2016.05.008 [BibTeX]
Zhang, Y., Shang, Y., Hu, X., & Li, A.-D.* (2022). An improved exponential EWMA chart for monitoring time between events. Quality and Reliability Engineering International. doi:10.1002/qre.3102 [BibTeX]
Zhang, Y., Shang, Y., & Li, A.-D.* (2021). Self-information-based weighted CUSUM charts for monitoring Poisson count data with varying sample sizes. Quality and Reliability Engineering International, 37(5), 1847–1862. doi:10.1002/qre.3102 [BibTeX]
Conference Papers
Li, A.-D.*, Xue, B., & Zhang, M. (2021). A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification.
IEEE Congress on Evolutionary Computation, CEC 2021, Kraków, Poland, June 28 - July 1, 2021, 786–793.
doi:10.1109/CEC45853.2021.9504949 [BibTeX]
[Source Code] [pdf]
Li, A.*, & He, Z. (2016). ReliefF Based Forward Selection Algorithm to Identify CTQs for Complex Products. Proceedings of the 22nd
International Conference on Industrial Engineering and Engineering Management 2015. Paris: Atlantis Press.
doi:10.2991/978-94-6239-180-2_3 [BibTeX]
Liu, X.*, Xia, Y., Chen, M., & Li, A.-D. (2019). Integrating Assembly Line Balancing in Product Family Planning Design under the Multinomial Logit Choice Model. 2019 International Conference on Industrial Engineering and Systems Management (IESM), 1–6. doi:10.1109/IESM45758.2019.8948102 [BibTeX]
Journal Papers in Chinese
李岸达,何桢 & 何曙光.(2016).基于NSGA-Ⅱ的非平衡制造数据关键质量特性识别. 系统工程理论与实践,36(06),1472-1479. [pdf]
李岸达,何桢 & 何曙光.(2015).基于GSA的复杂产品关键质量特性识别. 系统工程与电子技术,37(09),2073-2079. [pdf]
李岸达,何桢 & 王庆.(2019).基于多目标鲸鱼优化的关键质量特性识别方法. 系统工程,37(01),134-142. [pdf]
李岸达,何桢 & 何曙光.(2014).基于Filter与Wrapper的复杂产品关键质量特性识别. 工业工程与管理,19(03),53-59. doi:10.19495/j.cnki.1007-5429.2014.03.009 [pdf]
闫伟,何桢 & 李岸达.(2014).基于CEM-IG算法的复杂产品关键质量特性识别. 系统工程理论与实践,34(05),1230-1236. [pdf]
2022-2024: Key Quality Factor Identification and Online Quality Prediction for Complex Manufacturing Processes Based on Machine Learning Approaches. National Natural Science Foundation of China (NSFC). Grant: 300,000 CNY. (PI)
2019-2021: Key Quality Characteristic Identification for Multi-stage Manufacturing Processes of Complex Products. Humanities and Social Sciences Youth Fund of Ministry of Education of China. Grant: 80,000 CNY. (PI)
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