Modern manufacturing enterprises struggle to create efficient and reliable production schedules under multi-variety, small-batch, and rush-order conditions. High-mix discrete manufacturing systems require jointly optimizing mid-term production planning and machine-level scheduling under heterogeneous resources and stringent delivery commitments. We address this problem with a profit-driven integrated framework that couples a mixed-integer planning model with a machine-level scheduling heuristic. The planning layer allocates production, accessory co-production, and outsourcing under aggregate economic and capacity constraints, while the scheduling layer refines these allocations using a structure-aware procedure that enforces execution feasibility and stabilizes daily machine behavior. This hierarchical design preserves the tractability of aggregated optimization while capturing detailed operational restrictions. Evaluations are conducted on a real industrial scenario. A flexible machine-level execution scheme yields 73.3% on-time completion and significant outsourcing demand, revealing bottleneck congestion. In contrast, a stability-enforcing execution policy achieves 100% on-time completion, eliminates all outsourcing, and maintains balanced machine utilization with only 1.9 to 4.6% capacity loss from changeovers. These results show that aligning planning decisions with stability-oriented execution rules enables practical and interpretable profit-maximizing decisions in complex manufacturing environments.

翻译：现代制造企业在多品种、小批量及紧急订单条件下难以制定高效可靠的生产计划。高混合离散制造系统需要在异构资源与严格交付承诺下，联合优化中期生产规划与机器级调度。本文提出一种利润驱动的集成框架，将混合整数规划模型与机器级调度启发式方法相结合，以解决此问题。规划层在总体经济与产能约束下分配生产、配件联产及外包任务，而调度层则通过结构感知程序细化这些分配，确保执行可行性并稳定每日机器行为。这种分层设计在保持聚合优化可处理性的同时，捕捉了详细的操作限制。评估基于真实工业场景进行：灵活的机器级执行方案实现了73.3%的准时完成率，并产生显著的外包需求，揭示了瓶颈拥堵问题；相比之下，强化稳定性的执行策略实现了100%的准时完成率，消除了所有外包需求，并在换型仅导致1.9%至4.6%产能损失的情况下保持了均衡的机器利用率。结果表明，在复杂制造环境中，将规划决策与面向稳定性的执行规则对齐，能够实现实用且可解释的利润最大化决策。