文献类型：期刊文献

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

作者：Liu, Runhao[1]; Chen, Ziming[2]; Li, You[3]; Xie, Zequn[1]; Zhang, Peng[4]

机构：[1] Polytechnic Institute, Zhejiang University, Hangzhou, 310015, China; [2] School of Social and Public Administration, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Data Science and Engineering, Guangdong Polytechnic Normal University, Guangdong, Guangzhou, 510665, China; [4] School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20260013238)

语种：英文

外文关键词：Integer programming - Outsourcing - Production control - Profitability - Scheduling algorithms

摘要：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 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 1.9–4.6% 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. Copyright ? 2025, The Authors. All rights reserved.