In mass customization using resources efficiently with changing customer demands and manufacturing conditions is vital, since in today's competitive market conditions, this efficiency provides cost, time and labor savings to companies. In production environment, Process Planning (PP) and Scheduling are two functions that provide efficient usage of resources. Isolation and long time gap between these functions are main problems affecting the effiency of production. In this study, to solve the problems of productivity, it is aimed to build an integrated system which is able do PP and Scheduling in parallel and respond quickly to fluctuations in job floor. In existing integration models aiming to eliminate this isolation and time gap, it has been observed that if the search and solution space expands, the computational time increases rapidly. Therefore, in this research, a hybrid optimization approach, which can find the optimal solution rapidly is considered and a hybrid model combining both Genetic Algorithm (GA) and Artificial Neural Network (ANN) is proposed. To improve GA performance and increase the effiency of searching, clustering activities are carried out for building new cromosome structures. To increase population diversity, effective genetic operator schemes and efficient genetic represantations are used. In the integration module, 3 different GA structures created within the scope of our research are compared and the algorithm formed by clustering method shows better performance than the others. In this paper by using ANN method, a new system trained by data obtained from Scheduling is generated and this system is able to quickly respond the changes in shop floor and provide new schedules instantly. In rescheduling module, ANN's performance measures provide evidence to accuracy of Heuristic solution generated by Integration module.