Integrated Planning and Machine-Level Scheduling for High-Mix Discrete Manufacturing: A Profit-Driven Heuristic Framework

TL;DR

This paper tackles profit-driven integrated planning and scheduling for high-mix discrete manufacturing with mold-dependent processing on heterogeneous machines. It introduces a two-layer hierarchy: a rolling-horizon, profit-focused mid-term planning MILP and a structure-aware daily scheduling heuristic that refines plans into executable, stable shop-floor sequences. In a real smartphone-case study, the stability-oriented Scheme C (daily mold dedication) achieves 100% on-time delivery and eliminates outsourcing, while revealing the critical role of planning in coordinating multi-stage operations. Ablation studies show that removing the planning layer degrades synchronization, underscoring the practical value of aligning strategic planning with execution rules for profit-maximizing performance.

Abstract

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.

Figures (7)

• Figure 1: Overview of proposed method.
• Figure 2: Machine-group Gantt chart under Scheme A.
• Figure 3: Machine-level Gantt chart under Scheme B.
• Figure 4: Machine-group Gantt chart under Scheme C.
• Figure 5: Detailed machine-level Gantt charts under Scheme C.