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The bin packing problem exists widely in real logistic scenarios (e.g.,packing pipeline, express delivery), with its goal to improve the packingefficiency and reduce the transportation cost. In this NP-hard combinatorialoptimization problem, the position and quantity of each item in the box arestrictly restricted by complex constraints and special customer requirements.Existing approaches are hard to obtain the optimal solution since rigorousconstraints cannot be handled within a reasonable computation load. In thispaper, for handling this difficulty, the packing knowledge is extracted fromhistorical data collected from the packing pipeline of Huawei. First, by fullyexploiting the relationship between historical packing records and inputorders(orders to be packed) , the problem is reformulated as a set coverproblem. Then, two novel strategies, the constraint handling and processacceleration strategies are applied to the classic column generation approachto solve this set cover problem. The cost of solving pricing problem forgenerating new columns is high due to the complex constraints and customerrequirements. The proposed constraints handling strategy exploits thehistorical packing records with the most negative value of the reduced cost.Those constraints have been implicitly satisfied in these historical packingrecords so that there is no need to conduct further evaluation on constraints,thus the computational load is saved. To further eliminate the iterationprocess of column generation algorithm and accelerate the optimization process,a Learning to Price approach called Modified Pointer Network is proposed, bywhich we can determine which historical packing records should be selecteddirectly. Through experiments on realworld datasets, we show our proposedmethod can improve the packing success rate and decrease the computation timesimultaneously.

A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry