A case-injected genetic algorithm for single machine scheduling problems with release time

This paper presents a case-injected genetic algorithm (CIGA) to solve the single machine scheduling problems with release time and the objective is to minimize the total weighted completion time. The initial case base is generated by using general genetic algorithm (GA) for different problem sets. To solve a new problem, CIGA first retrieves the similar cases from the case base according to the two-phase similarity coefficient adaptation procedure developed in this research, and then incorporates these similar cases into the GA to solve the problems at hand. Finally, CIGA stores the solved problems in the case base for future retrievals. Extensive experimental results, including case retrieval of case base as initial population and injection of case base every 600 generation, show that CIGA outperformed the other GAs with better quality of solutions.