Optimization Methods in Information and Management Sciences

Copyright © 2015 Jung-Fa Tsai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This special issue continues to raise the aim of The Scientific World Journal in the operations research subject area through its cutting-edge presentation of advanced research, carefully developed applications, and innovative technology components. This special issue also provides a forum for researchers and practitioners to review and disseminate quality research work on optimization methods and their applications in the context of information and management sciences and to identify critical issues for further developments. This special issue involves original papers, selected by the editors and related to the various researches themes on optimization methods in information and management sciences. These papers are related to various subjects such as computer science and electric engineering, computational intelligence, big data, security, management strategy, data envelopment analysis, facility location problem, production planning, and workflow nets. The research area represented by the papers accepted for the special issue should be of interest for a wide scientific community because of its multidisciplinary character. The accepted papers of this special issue are briefly described as below. J. W.-S. Hu et al. provide several notable findings from the empirical studies. First, the expected temperature shock significantly and positively affects both the near-month and far-month natural gas and heating oil futures returns. This suggests that the investors should invest a portion of their funds on the weather derivative commodities for their hedging and arbitraging purposes. Next, significant temperature shock effect on both the conditional mean and volatility of natural gas and heating oil prices suggests that when the market expects the degree days in the future to be higher than average , energy demand increases, increasing the near-month and far-month natural gas and heating oil futures returns. The nonlinear temperature effects exist for natural gas futures returns, suggesting the market expects the degree day volatility to increase with natural gas demand. Also, the results indicate that expected inventory surprises significantly and negatively affect the far-month natural gas futures returns only. Moreover, volatility of natural gas futures returns is higher on Thursdays and that of near-month heating oil futures returns is higher on Wednesdays than other days, coinciding with the release of the weekly natural gas and heating oil storage report by the Energy …