Implications Of Global Financial Crisis Research Articles

Implications of Global Financial Crisis on Textile Industry of Pakistan

The global financial crisis of 2008, had very little or no impact on our textile sector performance, the main cause of downward trend of Pakistan textile industry is the reason of energy shortages, law and order situation, lack of active marketing strategies, and the behavior or small, medium and large manufacturers of textile products are more profit oriented, ignoring their responsibility towards social and economic development of Pakistan. The author purpose was to explore the phenomenon of financial crisis and the performance of Pakistan industry how they are linked and what factors were linked with both the crisis. The phenomenon studied includes the era of financial crisis and simultaneously the crisis of textile industry of Pakistan and to identify the weak links. This study also aims to analyze the potential of productivity and investment of textile sector and its contribution in the earnings for the Government of Pakistan. The research participants were from the area of field research on textile sector of Pakistan, working on it since last 4 to 5 years. Data analysis was done by using Colaizzi’s (1978) phenomenological methods; was used for data analysis, validation and verification. Out of various statements 12 themes emerged which represents the whole story how the textile sector crisis took place and the factors behind them.

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.