Recently, manufacturers with make-to-order (MTO) tend to use a flexible flows strategy, because they have to the job shop type production and small batch production to satisfy customer requirements. In this environment, the scheduling problems have complexities. The space of feasible schedules grows exponentially as there increase the number of different orders that must be processed, number of operations required by each order, batch size of each order, and the size and complexity of the installation of interest. In this paper, we propose an advanced production scheduling (APS) model with multiple objectives to generate the schedules considering resource constraints and precedence constraints in MTO process. Precedence of work-in-process and resources constraints, in APS problems, have recently emerged as one of the main constraints. The APS problems are formulated as a multi-objective mathematical model for generating operation schedules which satisfies resources constraints, alternative resources and the precedence constraints. The model is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. For effectively solving the APS model, the hybrid genetic algorithm (hGA) with local search-based mutation through swap mutation is proposed. Experimental results are presented for APS models with multi-objective of various sizes to describe the performance of the proposed hGA.