  Packing cost accounts for a large part of the e-commerce logistics cost.
Mining the patterns of customer orders and designing suitable packing bins help
to reduce operating cost. In the classical bin packing problem, a given set of
cuboid-shaped items should be packed into bins with given and fixed-sizes
(length, width and height) to minimize the number of bins that are used.
However, a novel bin design problem is proposed in this paper. The decision
variables are the geometric sizes of bins, and the objective is to minimize the
total surface area. To solve the problem, a low computational-complexity,
high-performance heuristic algorithm based on dynamic programming and
depth-first tree search, named DPTS, is developed. Based on real historical
data that are collected from logistics scenario, numerical experiments show
that the DPTS out-performed 5.8% than the greedy local search (GLS) algorithm
in the total cost. What's more, DPTS algorithm requires only about 1/50 times
of the computational resources compared to the GLS algorithm. This demonstrates
that DPTS algorithm is very efficient in bin design problem and can help
logistics companies to make appropriate design.
