This paper proposes an integrated scheduling framework for procurement, production, and logistics aimed at optimizing overall supply chain performance. The framework aligns three subsystems-MRP (Material Requirements Planning: procurement), SS (Production Scheduling: production), and DP (Distribution Planning: logistics)-using multi-objective optimization techniques to minimize inter-layer discrepancies. Two methods are introduced concretely: a static approach based on push-pull constraints and a dynamic approach inspired by decentralized supply chain management (DSCM), employing Lagrangian decomposition for iterative adjustment. The framework allows flexible selection of solution methods based on specific business needs. Key techniques include methods of n-step hybrid flowshop scheduling denoted by n-GuptaEX based on J.D. Gupta et al. (2002), SETUPBO (Self-Tuning Portfolio-based Bayesian Optimization) for multi-objective weight tuning, Multi-objective Linear Programming (MOLP) and P3D-QAP (Pseudo Periodical Priority Dispatching-Quadratic Assignment Problem) for procurement, and Greedy-TS (Greedy-Tabu Search) for logistics. Case studies on three real-world datasets demonstrate the framework’s adaptability and effectiveness, with notable improvements in cycle time, due-date, energy efficiency, and loading rates. Compared to traditional approaches, the proposed methods achieve up to a 70% reduction in energy use and a 3.5-x increase in loading efficiency. This research offers a versatile and practical solution for integrated SC (Supply Chain) scheduling, with future work focusing on dynamic strategy refinement and scalability.