Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers

TL;DR

This paper introduces algorithms for optimizing agent target assignment and path planning in automated sortation centers, significantly reducing station idle times and improving throughput using a novel flow-based approach tested on realistic simulations.

Contribution

It presents a new min-cost max-flow formulation for collision-free path planning that minimizes station idle time, applicable to both one-shot and lifelong scenarios in industrial settings.

Findings

Algorithms efficiently handle up to 350 agents.

Significant reduction in station idle time.

Validated on realistic industrial simulation data.

Abstract

In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin. The throughput of such centers is largely determined by the total idle time of all stations since their queues can frequently become empty. To address this problem, we first formalize and study the one-shot version that assigns stations to a set of agents and finds collision-free paths for the agents to their assigned stations. We present efficient algorithms for this task based on a novel min-cost max-flow formulation that minimizes the total idle time of all stations in a fixed time window. We then demonstrate how our algorithms for solving the one-shot problem can be applied to solving the lifelong problem as well. Experimentally, we believe to be the first researchers to consider real-world automated sortation centers using an industrial simulator with realistic data and a kinodynamic model of real robots. On this simulator, we showcase the benefits of our algorithms by demonstrating their efficiency and effectiveness for up to 350 agents.