A generalized algorithm and framework for online 3-dimensional bin packing in an automated sorting center

TL;DR

This paper introduces a new framework and algorithms for real-time 3D bin packing in automated sorting centers, addressing the challenge with limited prior solutions and demonstrating practical effectiveness with industry data.

Contribution

It proposes a novel adaptive framework OPack and two MILP-based algorithms, MPack and MPackLite, for online 3D bin packing with look-ahead, advancing real-time industrial automation solutions.

Findings

MPackLite and heuristics operate within robot constraints

OPack improves heuristic performance with online info

Algorithms tested on synthetic and industry data

Abstract

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.