Cargando…

Multi-agent task allocation for harvest management

Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that...

Descripción completa

Detalles Bibliográficos
Autores principales: Harman, Helen, Sklar, Elizabeth I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643785/
https://www.ncbi.nlm.nih.gov/pubmed/36388254
http://dx.doi.org/10.3389/frobt.2022.864745
_version_ 1784826594663071744
author Harman, Helen
Sklar, Elizabeth I.
author_facet Harman, Helen
Sklar, Elizabeth I.
author_sort Harman, Helen
collection PubMed
description Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that are ready to be harvested and team leaders who manage the workers in the fields. Creating these assignments is a dynamic and complex problem, as the skill of the workforce and the yield (quantity of ripe fruit picked) are variable and not entirely predictable. The work presented here posits that multi-agent task allocation methods can assist farm managers and team leaders to manage the harvesting workforce effectively and efficiently. There are three key challenges faced when adapting multi-agent approaches to this problem: (i) staff time (and thus cost) should be minimised; (ii) tasks must be distributed fairly to keep staff motivated; and (iii) the approach must be able to handle incremental (incomplete) data as the season progresses. An adapted variation of Round Robin (RR) is proposed for the problem of assigning workers to fields, and market-based task allocation mechanisms are applied to the challenge of assigning tasks to workers within the fields. To evaluate the approach introduced here, experiments are performed based on data that was supplied by a large commercial soft fruit farm for the past two harvesting seasons. The results demonstrate that our approach produces appropriate worker-to-field allocations. Moreover, simulated experiments demonstrate that there is a “sweet spot” with respect to the ratio between two types of in-field workers.
format Online
Article
Text
id pubmed-9643785
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96437852022-11-15 Multi-agent task allocation for harvest management Harman, Helen Sklar, Elizabeth I. Front Robot AI Robotics and AI Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that are ready to be harvested and team leaders who manage the workers in the fields. Creating these assignments is a dynamic and complex problem, as the skill of the workforce and the yield (quantity of ripe fruit picked) are variable and not entirely predictable. The work presented here posits that multi-agent task allocation methods can assist farm managers and team leaders to manage the harvesting workforce effectively and efficiently. There are three key challenges faced when adapting multi-agent approaches to this problem: (i) staff time (and thus cost) should be minimised; (ii) tasks must be distributed fairly to keep staff motivated; and (iii) the approach must be able to handle incremental (incomplete) data as the season progresses. An adapted variation of Round Robin (RR) is proposed for the problem of assigning workers to fields, and market-based task allocation mechanisms are applied to the challenge of assigning tasks to workers within the fields. To evaluate the approach introduced here, experiments are performed based on data that was supplied by a large commercial soft fruit farm for the past two harvesting seasons. The results demonstrate that our approach produces appropriate worker-to-field allocations. Moreover, simulated experiments demonstrate that there is a “sweet spot” with respect to the ratio between two types of in-field workers. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9643785/ /pubmed/36388254 http://dx.doi.org/10.3389/frobt.2022.864745 Text en Copyright © 2022 Harman and Sklar. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Harman, Helen
Sklar, Elizabeth I.
Multi-agent task allocation for harvest management
title Multi-agent task allocation for harvest management
title_full Multi-agent task allocation for harvest management
title_fullStr Multi-agent task allocation for harvest management
title_full_unstemmed Multi-agent task allocation for harvest management
title_short Multi-agent task allocation for harvest management
title_sort multi-agent task allocation for harvest management
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643785/
https://www.ncbi.nlm.nih.gov/pubmed/36388254
http://dx.doi.org/10.3389/frobt.2022.864745
work_keys_str_mv AT harmanhelen multiagenttaskallocationforharvestmanagement
AT sklarelizabethi multiagenttaskallocationforharvestmanagement