A generalized algorithm and framework for online 3-dimensional bin packing in an automated sorting center
This addresses a practical problem for automated logistics in Industry 4.0, but it is incremental as it builds on existing methods with adaptations for online settings.
The paper tackles the online 3D bin packing problem with partial information in automated sorting centers by developing algorithms like MPackLite and a framework OPack, resulting in real-time performance within robot operation bounds as shown on synthetic and industry data.
Online 3-dimensional bin packing problem (O3D-BPP) is getting renewed prominence due to the industrial automation brought by Industry 4.0. However, due to limited attention in the past and its challenging nature, a good approximate algorithm is in scarcity as compared to 1D or 2D problems. This paper considers real-time O$3$D-BPP of cuboidal boxes with partial information (look-ahead) in an automated robotic sorting center. We present two rolling-horizon mixed-integer linear programming (MILP) cum-heuristic based algorithms: MPack (for bench-marking) and MPackLite (for real-time deployment). Additionally, we present a framework OPack that adapts and improves the performance of BP heuristics by utilizing information in an online setting with a look-ahead. We then perform a comparative analysis of BP heuristics (with and without OPack), MPack, and MPackLite on synthetic and industry provided data with increasing look-ahead. MPackLite and the baseline heuristics perform within bounds of robot operations and thus, can be used in real-time.