HGHA: task allocation and path planning for warehouse agents. Issue 2 (5th January 2021)

• Record Type: Journal Article
• Authors: Liu, Yandong; Han, Dong; Wang, Lujia; Xu, Cheng-Zhong
• Is Part Of: Assembly automation. Volume 41:Issue 2(2021)
• Page Start: 165
• Page End: 173
• Publication Date: 2021-01-05
• Subjects: Path planning -- Multi-agent -- Task allocation -- Hierarchical Genetic Highways Algorithm; Automation -- Periodicals; Automatic machinery -- Periodicals; Assembly-line methods -- Periodicals; Industrial engineering -- Periodicals; 670.42705
• DOI: 10.1108/AA-10-2020-0152
• Languages: English
• ISSNs: 0144-5154

Abstract

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.