Large scale generation resource scheduling optimization problem is usually very hard to solve due to its combinatorial nature. Various algorithms such as Lagrangian relaxation based algorithm, Benders algorithm, and genetic algorithms have been proposed in the past to tackle this problem. One challenge facing researchers is how to determine which algorithm to use and how to test and compare the performance of various algorithms. To explore and propose new algorithms, benchmarking of the performance of existing algorithms is essential, since advantages and disadvantages of each algorithm can be better understood through extensive case studies. To carry out such studies, a systematic way based on a comprehensive case library is necessary. This paper describes an approach for building a case library that can be used for testing various resource scheduling algorithms. The implementation details including the development environment and special considerations are presented.