  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.
