个东谈主信息:
基本信息:李岸达,男,博士,处理学院副纯属,东谈主力资源处理系主任,新西兰惠灵顿维多利亚大学看望学者(2018.7-2018.12),天津市工业工程学会理事,IEEE会员,中国优选法统筹法与经济数学筹商会会员。
培植配景:
(1) 2013-9至2016-6, 天津大学, 处理科学与工程, 博士
(2) 2011-9至2013-6, 天津大学, 工业工程, 硕士
(3) 2007-9至2011-6, 北京邮电大学, 物流工程, 本科
赢得荣誉:
1. 2018年度天津市“131”立异型东谈主才培养工程第三头绪东谈主选
2. 入选天津市高校“后生后备东谈主才复古有计划”
3. 天津市教会团队成员
4. 2019年天津交易大学优秀教师
Email: adli@tjcu.edu.cn
个东谈主主页:https://andali89.github.io/homepage/
学科边界: 质地处理与质地工程,诡计智能,机器学习,东谈主力资源处理。
代表性论文:
[1] 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. (ABS4, FMS A类, SCI)
[2] 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. (FMS B类, CCF B类, SCI)
[3] 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,不会 523, 245–265. (FMS B类, CCF B类, SCI)
[4] 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. (ABS2, FMS B类, SCI)
[5] 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. (SCI)
[6] Li, A.-D.*,神秘顾客应用 & 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. (ABS2, FMS B类, SCI)
[7] 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. (ABS3, SCI)
[8] 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 (SCI)
[9] 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. https://doi.org/10.1016/j.cie.2022.108617 (ABS2, FMS B类, SCI)
[10] 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 (SCI)
[11] 李岸达, 何桢, & 何晨曦 (2016).基于NSGA-Ⅱ的非均衡制造数据要津质地特点识别. 系统工程表面与践诺, 36(6): 1472-1479. (FMS 华文T1级)
科研情景:
[1] 基于机器学习的复杂制造流程要津质地成分识别与在线质地展望筹商, 国度当然科学基金后生科学基金情景 (72101182), 2022-01至2024-12, 在研, 主抓
[2] 多阶段复混居品制造流程要津质地特点识别筹商, 培植部东谈主文社会科学筹商后生基金情景(19YJC630071), 2019-01至2022-12, 结题, 主抓
[3] 复杂制造流程中详尽数据监控挨次筹商,国度当然科学基金后生科学基金情景 (71401123), 2015-01至2017-12,结题,参与
[4] 基于信息熵的事件动态死亡图筹商, 培植部东谈主文社会科学筹商后生基金情景(19YJC630221), 2019-01至2021-12, 在研, 第一参与东谈主
市场调查的目标一般为顾客、零售商、销售商,零售商和销售商为经销商调查商品的店家,顾客一般为应用该商品的消費人群。在以顾客为调查目标时,要留意到有时候某一商品的消费者和使用人不一致,如对婴幼儿食品的调查,其调查目标应是小孩的妈妈。除此之外还应留意到一些商品的消費目标关键对于某一特殊消費人群或偏重于某一消費人群,这时候调查目标应留意挑选商品的关键消費人群,如针对护肤品,调查目标关键挑选女士;针对酒水商品,其调查目标关键为男士。
[5] 基于非均衡数据的复混居品要津质地特点识别筹商, 天津市高档学校东谈主文社会科学筹商情景, 2017-01至2019-12,结题,主抓
团队成员:张阳,刘晓杰,刘超超,李娟,刘盟,陈芳