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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Proceedings ArticleDOI
Mean-Variance-Skewness-Kurtosis-based Portfolio Optimization
TL;DR: PGP approach is significantly efficient way to solve multiple conflicting portfolio objectives in the mean-variance-skewness-kurtosis framework, but it is found that the different investors' preferences not only affect asset allocations of portfolio, but also affect the four moment statistics of return.
Journal ArticleDOI
A stochastic programming approach for multi-site aggregate production planning
TL;DR: A stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment and a set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of this model.
Journal ArticleDOI
Supply chain integration and service oriented transformation: Evidence from Chinese equipment manufacturers
Yuanqiong He,Kin Keung Lai +1 more
TL;DR: In this article, the authors build a conceptual model to describe the relationships among operational integration and strategic integration of supply chain, product-based and customer action-based service provided by industrial manufacturers, and firm performance.
Journal ArticleDOI
Analysis of road transportation energy consumption demand in China
TL;DR: Wang et al. as mentioned in this paper analyzed the historical trends in road transportation energy consumption and GDP in developed economies to find out the development characteristics of road energy consumption, and employed path analysis to analyze the impact mechanism of the factors related to road transport energy consumption.
Journal ArticleDOI
A multiscale neural network learning paradigm for financial crisis forecasting
TL;DR: Experimental results reveal that the proposed multiscale neural network learning paradigm can significantly improve the generalization performance relative to conventional neural networks.