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HGHA: task allocation and path planning for warehouse agents

Yandong Liu (Center for Cloud Computing, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China and Shenzhen College of Advanced Technology, University of the Chinese Academy of Sciences, Beijing, China)
Dong Han (Center for Cloud Computing, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China and Shenzhen College of Advanced Technology, University of the Chinese Academy of Sciences, Beijing, China)
Lujia Wang (Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China)
Cheng-Zhong Xu (State Key Lab of IOTSC, Department of Computer and Information Science, University of Macau, Taipa, Macao)

Assembly Automation

ISSN: 0144-5154

Article publication date: 5 January 2021

Issue publication date: 27 July 2021

308

Abstract

Purpose

With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.

Design/methodology/approach

The TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.

Findings

Experiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.

Originality/value

This paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

Keywords

Citation

Liu, Y., Han, D., Wang, L. and Xu, C.-Z. (2021), "HGHA: task allocation and path planning for warehouse agents", Assembly Automation, Vol. 41 No. 2, pp. 165-173. https://doi.org/10.1108/AA-10-2020-0152

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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