Efficient production scheduling plays a pivotal role in enhancing productivity and reducing energy consumption in mass manufacturing environments. This study presents a comparative evaluation of two mixed-integer linear programming (MILP) formulations - Discrete-Time Process Network Synthesis (PNS) and Precedence-Based Time-Constrained Process Network Synthesis (TCPNS) - for optimizing production scheduling in furniture manufacturing. Both approaches are grounded in the P-graph framework, which excels at representing complex, flexible process recipes commonly found in large-scale production systems. The TCPNS model, with its precedence-based structure, offers high-resolution scheduling capabilities and accurately manages complex changeover constraints. It enables the computation of exact start times and resource allocations, leading to highly optimized schedules. However, this precision comes with increased computational demand, which can become impractical for large-scale instances. Conversely, the PNS approach discretizes the planning horizon into time slots, significantly reducing model size and complexity. While this may result in less granular schedules, the formulation allows for faster solution times and easier integration of combinatorial simplifications, making it a practical alternative for real-time applications. The research also explores automated model generation techniques for both formulations, highlighting multi-resolution capabilities in the discrete-time approach that allow flexible trade-offs between accuracy and computational effort. A real-life case study from the furniture manufacturing sector is used to benchmark the two optimization strategies. The results demonstrate the practical implications of each method in terms of schedule precision, computational performance, and energy-aware utilization, i.e., if minute-to-minute scheduling is sufficient instead of milliseconds, then traditional PNS algorithms can offer the same sustainable solution with 10,000 times faster computation.