  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.
