Balanced Order Batching with Task-Oriented Graph Clustering

TL;DR

This paper introduces BTOGCN, an end-to-end learning framework that formulates the warehouse order batching problem as a balanced graph clustering task, achieving better results than traditional heuristics.

Contribution

Proposes BTOGCN, a novel task-oriented graph clustering network that directly optimizes order batching, outperforming existing deep clustering methods and heuristic algorithms.

Findings

Achieves an average reduction of 4.57 meters in picking distance on single-graph datasets.

Achieves an average reduction of 0.13 meters in picking distance on multi-graph datasets.

Demonstrates strong generalization ability in practical warehouse scenarios.

Abstract

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.