AI 中文总结
针对加拿大林业多周期原木运输车辆路径规划与调度难题，分析业务规则构建网络，提出含所有运营约束的MILP公式及求解方法，结合先进求解器与元启发式策略，实验显示能在实际时间内获近最优结果，有财务收益且减排。
AI 中文摘要
本文研究多周期原木运输车辆路径规划与调度问题（$\mathcal{LTRSP}$），这是林业中的关键运营活动，运输成本占总运营成本超三分之一。加拿大林业面临诸多物流难题。我们解决了林业运营文献中长期存在的开放性问题，即缺乏整合所有运营约束的精确且可扩展的林业车辆路径规划问题公式。为此，分析林业业务规则构建反映实际运营的路径网络，提出综合改进的混合整数线性规划（MILP）公式并阐述关键建模选择理由，还给出基于分解的求解方法。通过结合先进求解器与元启发式分解策略，利用加拿大森林公司历史数据进行计算实验，结果显示在实际计算时间内接近最优，有显著财务收益并减少温室气体排放。
英文摘要
This paper addresses the multi-period log-truck routing and scheduling problem ($\mathcal{LTRSP}$), a key operational activity in the forestry industry, where transportation accounts for more than one-third of total operational costs. The Canadian forestry sector faces significant logistical difficulties driven by vast geographic distances, seasonal variability, volatile markets, and environmental considerations. Our research tackles a long-standing open question in the forestry operations literature, namely the absence of an exact and scalable formulation of the forestry vehicle routing problem integrating the full range of operational constraints \cite{ronnqvist2015operations}: \textit{How can we model and solve an exact formulation of the forestry VRP problem?} In response, we analyze business rules specific to the forestry sector to construct a routing network reflecting real operational practices, and then propose a comprehensive improved mixed-integer linear programming (MILP) formulation incorporating all known forestry operational constraints, with a detailed justification of key modeling choices such as time discretization, along with a decomposition-based solution methodology tailored for large-scale, multi-period industrial instances. To address the inherent combinatorial complexity, we combine state-of-the-art solvers with a metaheuristic decomposition strategy based on \textit{Relax\&Fix} and \textit{Fix\&Optimize}. Computational experiments on historical data from a Canadian forest company demonstrate near-optimal results within practical computation times, with significant financial gains and reduced greenhouse gas emissions.
