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
More filters
Journal ArticleDOI
A transfer forecasting model for container throughput guided by discrete PSO
TL;DR: A transfer forecasting model guided by discrete particle swarm optimization algorithm (TF-DPSO) is proposed that constructs the forecasting model by a pattern matching method called analog complexing, and the results show the effectiveness of the proposed model.
Journal ArticleDOI
Bertrand vs. Cournot competition in distribution channels with upstream collusion
TL;DR: In this paper, the authors apply repeated game theory to tacit collusion in dynamic distribution channels based on the grim trigger strategy, and examine competitors' choice of the strategic instruments in distribution channels comprised of two manufacturers distributing through two independent retailers respectively.
Journal ArticleDOI
A distance-based decision-making method to improve multiple criteria ABC inventory classification
TL;DR: The proposed method determines the common weights associated with all rankings of the criteria importance, and then provides a comprehensive scoring scheme by aggregating all rankings.
Journal ArticleDOI
Fuel efficiency and emission in China's road transport sector: Induced effect and rebound effect
TL;DR: In this paper, the authors analyzed how endogenous road capacity, in terms of an increase in road accessibility and traffic demand, and exogenous efficiency policies and technological progress, affect fuel consumption and thereby exhaust emission.
Journal ArticleDOI
A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction
TL;DR: An alternative Slantlet denoising based hybrid methodology that attempts to incorporate the linear and nonlinear data features in exchange rates and outperforms the benchmark models.