A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System

A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles a...

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Autores principales: Yan, Xiaohui, Chan, Felix T. S., Zhang, Zhicong, Lv, Cixing, Li, Shuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354788/
http://dx.doi.org/10.1007/978-3-030-53956-6_9
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author Yan, Xiaohui
Chan, Felix T. S.
Zhang, Zhicong
Lv, Cixing
Li, Shuai
author_facet Yan, Xiaohui
Chan, Felix T. S.
Zhang, Zhicong
Lv, Cixing
Li, Shuai
author_sort Yan, Xiaohui
collection PubMed
description A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles are more realistic. According to the features of the problem, the encoding and decoding strategies for solutions to MABC algorithm are redesigned. Probability selection-based updating method is also introduced to enhance the neighborhood search and preserve the good fragments. The experimental results show that MABC can obtain better results than PSO and GA algorithm, and is a competitive approach for AS/RS scheduling optimization.
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spelling pubmed-73547882020-07-13 A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System Yan, Xiaohui Chan, Felix T. S. Zhang, Zhicong Lv, Cixing Li, Shuai Advances in Swarm Intelligence Article A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles are more realistic. According to the features of the problem, the encoding and decoding strategies for solutions to MABC algorithm are redesigned. Probability selection-based updating method is also introduced to enhance the neighborhood search and preserve the good fragments. The experimental results show that MABC can obtain better results than PSO and GA algorithm, and is a competitive approach for AS/RS scheduling optimization. 2020-06-22 /pmc/articles/PMC7354788/ http://dx.doi.org/10.1007/978-3-030-53956-6_9 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Yan, Xiaohui
Chan, Felix T. S.
Zhang, Zhicong
Lv, Cixing
Li, Shuai
A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title_full A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title_fullStr A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title_full_unstemmed A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title_short A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System
title_sort modified artificial bee colony algorithm for scheduling optimization of multi-aisle as/rs system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354788/
http://dx.doi.org/10.1007/978-3-030-53956-6_9
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