K
Kin Keung Lai
Researcher at Shenzhen University
Publications - 587
Citations - 15177
Kin Keung Lai is an academic researcher from Shenzhen University. The author has contributed to research in topics: Supply chain & Artificial neural network. The author has an hindex of 60, co-authored 547 publications receiving 13120 citations. Previous affiliations of Kin Keung Lai include City University of Hong Kong & North China Electric Power University.
Papers
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On q-Quasi-Newton’s Method for Unconstrained Multiobjective Optimization Problems
TL;DR: In this paper, a parameter-free optimization technique is applied in Quasi-Newton's method for solving unconstrained multiobjective optimization problems, and the components of the Hessian matrix are constructed using q-derivative.
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Multiple criteria models for evaluation of competitive bids
TL;DR: In this article, Liu et al. presented a survey of the state-of-the-art Chinese academic institutions in terms of their work in the field of systems science, including the following: S. Y. Wang Institute of Systems Science Academy of Mathematics and Systems Sciences Chinese Academy of Sciences, Beijing 100080, China E-Mail: sywang@iss02.iss.ac.edu.
Journal Article
Relationship between stock indices and investors' sentiment index in Chinese financial market
Xunfa Lu,Xunfa Lu,Kin Keung Lai +2 more
TL;DR: Wang et al. as mentioned in this paper used the ARMA-GARCH-type models to examine the relationship between the investors' sentiment index and the stock returns, and found that the change rate of investors sentiment has no significant Granger causality relationship.
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Analysing remanufacturing decisions of supply chain members in uncertainty of consumer preferences
TL;DR: In this article, a closed-loop supply chain with manufacturers, sellers, and consumers where consumers may or may not be willing to pay remunerative price for remanufactured products vis-a-vis new products is considered.
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A stochastic approach to professional services firms’ revenue optimization
TL;DR: A network optimization model for PSFs revenue management under an uncertain environment is proposed in a stochastic programming formulation so as to capture the randomness of the unknown demand.