通过列生成和本德分解实现造纸行业的端到端供应链规划
End-to-End Supply Chain Planning in the Paper Industry Via Column Generation and Benders Decomposition
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中文总结 AI 辅助
研究造纸行业端到端供应链规划难题，提出结合列生成与本德分解的BDCG - DP框架，利用问题结构特征求解。实验表明该框架能降低成本、减少运行时间，为行业提供首个工业规模集成精确模型，助规划者快速获可行调度。
中文摘要 AI 辅助
问题定义：本文研究大规模造纸生产中的集成端到端规划问题，需协调生产调度、裁切决策、车辆装载以及按订单生产和按库存生产需求的多期履行。实际中这些决策常顺序优化，导致材料浪费等问题，工业规模求解该完全集成问题具计算挑战性。方法/结果：问题关键结构特征是下游履行决策仅通过随时间的总供应可用性依赖上游生产和物流选择。据此开发精确数学公式并提出两阶段混合框架（BDCG - DP），结合精确动态规划的列生成进行供应侧决策和本德分解进行下游履行。对北美一家主要造纸制造商的专有实例进行计算实验表明，BDCG - DP在八周规划问题上比传统CG - DP降低总成本24.4%，四周规划问题的运行时间中位数从CG - DP的五小时以上降至BDCG - DP 的一小时以内。管理启示：本文提供首个在工业规模集成生产、裁切、装载规划和多期履行的精确模型，所提方法能在2.3至6小时内为最复杂规划问题返回整数可行计划，使规划者能在数小时内获得高质量可实施调度，这是此前实践中无法做到的。
英文摘要
Problem definition: The paper studies an integrated end-to-end planning problem in large-scale paper manufacturing, where production scheduling, trimming decisions, vehicle loading, and multi-period fulfillment of make-to-order and make-to-stock demand must be coordinated over time. In practice, these decisions are often optimized sequentially, leading to material waste, inefficient transportation, and degraded service levels. Solving the fully integrated problem at industrial scale remains computationally challenging due to its combinatorial structure. Methodology/results: A key structural feature of the problem is that downstream fulfillment decisions depend on upstream production and logistics choices only through aggregate supply availability over time. By exploiting this structure, the paper develops an exact mathematical formulation and proposes a two-phase hybrid framework (BDCG-DP) that integrates column generation (CG) using exact dynamic-programming (DP) for supply-side decisions with Benders decomposition (BD) for downstream fulfillment. Computational experiments on proprietary instances from a major North American paper manufacturer show that BDCG-DP lowers total costs by 24.4% compared to a traditional CG-DP on challenging eight-week planning problems. Median runtime for four-week planning problems decreases from over five hours using CG-DP to under one hour using BDCG-DP. Managerial implications: This paper provides the first exact model that integrates production, trimming, load planning, and multi-period fulfillment at an industrial scale. The proposed approach returns integer-feasible plans within 2.3 to 6 hours for the most complex planning problems, enabling planners to access high-quality implementable schedules within hours, a capability that was previously unavailable in practice.
