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.

中文翻译：

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HGHA：仓库代理的任务分配和路径规划

目的

随着电子商务的快速发展，物流需求与日俱增。以多代理系统为核心的现代仓储应运而生。本文旨在研究多代理仓库系统所需的任务分配和路径规划（TAPP）问题。

设计/方法/方法

TAPP 问题旨在通过将任务分配给代理并为代理规划无碰撞路径来最小化完工时间。本文介绍了分层遗传高速公路算法 (HGHA)，这是一种结合优化和多智能体寻路 (MAPF) 的分层算法。顶层是遗传算法 (GA)，以优化的方式将任务分配给代理。较低级别是所谓的高速公路局部修复（HLR）过程，当且仅当发生冲突时通过局部修复避免碰撞。

发现

实验表明，HGHA 在仓库场景中比 max multi-flow 执行得更快、更高效。本文还将 HGHA 应用于具有 MultiBots 的定制仓库模拟平台中具有一百个代理和一千个存储位置的 TAPP 实例。

原创性/价值

本文制定了多代理仓储配送问题，TAPP。基于分层架构的HGHA准确快速地解决了TAPP。通过大型多智能体仿真平台 MultiBots 验证 HGHA。