制造业装箱问题的数据驱动列生成算法
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry
摘要：
装箱问题广泛存在于真实的物流场景中（例如，包装管道、快递），其目的是提高包装效率，降低运输成本。在这个NP难的组合优化问题中，每个物品在箱子中的位置和数量受到复杂约束和特殊客户需求的严格限制。现有的方法很难获得最优解，因为严格的约束不能在合理的计算负荷内处理。为了解决这一问题，本文从华为包装流水线的历史数据中提取包装知识。首先，通过充分利用历史装箱记录和输入订单（待装箱订单）之间的关系，将问题转化为一个集合覆盖问题。然后，两个新的策略，约束处理和进程加速策略被应用到经典的列生成方法来解决这个集合覆盖问题。由于复杂的约束条件和客户需求，解决生成新列的定价问题的成本很高。所提出的约束处理策略利用历史包装记录的最负的价值减少的成本。这些约束条件在历史装箱记录中已经得到了隐含的满足，因此不需要对约束条件进行进一步的评估，从而节省了计算量。为了进一步消除列生成算法的迭代过程，加快优化过程，提出了一种称为改进指针网络的学习定价方法，通过该方法可以直接确定应该选择哪些历史装箱记录。通过在真实数据集上的实验，我们证明了我们提出的方法可以提高包装的成功率，同时减少计算时间。
英文摘要：
The bin packing problem exists widely in real logistic scenarios (e.g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transportation cost. In this NP-hard combinatorial optimization problem, the position and quantity of each item in the box are strictly restricted by complex constraints and special customer requirements. Existing approaches are hard to obtain the optimal solution since rigorous constraints cannot be handled within a reasonable computation load. In this paper, for handling this difficulty, the packing knowledge is extracted from historical data collected from the packing pipeline of Huawei. First, by fully exploiting the relationship between historical packing records and input orders(orders to be packed) , the problem is reformulated as a set cover problem. Then, two novel strategies, the constraint handling and process acceleration strategies are applied to the classic column generation approach to solve this set cover problem. The cost of solving pricing problem for generating new columns is high due to the complex constraints and customer requirements. The proposed constraints handling strategy exploits the historical packing records with the most negative value of the reduced cost. Those constraints have been implicitly satisfied in these historical packing records so that there is no need to conduct further evaluation on constraints, thus the computational load is saved. To further eliminate the iteration process of column generation algorithm and accelerate the optimization process, a Learning to Price approach called Modified Pointer Network is proposed, by which we can determine which historical packing records should be selected directly. Through experiments on realworld datasets, we show our proposed method can improve the packing success rate and decrease the computation time simultaneously.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
