Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

Abstract A modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as alloca...

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Main Authors: Xiangjie Xiao, Yaohui Pan, Lingling Lv, Yanjun Shi
Format: Article
Language:English
Published: Wiley 2021-06-01
Series:IET Collaborative Intelligent Manufacturing
Online Access:https://doi.org/10.1049/cim2.12016
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spelling doaj-9b9af8000898455884e35624b25e723e2021-06-09T14:38:13ZengWileyIET Collaborative Intelligent Manufacturing2516-83982021-06-01329310410.1049/cim2.12016Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithmXiangjie Xiao0Yaohui Pan1Lingling Lv2Yanjun Shi3Mechanical Engineering Department Dalian University of Technology ChinaMechanical Engineering Department Dalian University of Technology ChinaMechanical Engineering Department Dalian University of Technology ChinaMechanical Engineering Department Dalian University of Technology ChinaAbstract A modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple‐AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi‐mode resource‐constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.https://doi.org/10.1049/cim2.12016
collection DOAJ
language English
format Article
sources DOAJ
author Xiangjie Xiao
Yaohui Pan
Lingling Lv
Yanjun Shi
spellingShingle Xiangjie Xiao
Yaohui Pan
Lingling Lv
Yanjun Shi
Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
IET Collaborative Intelligent Manufacturing
author_facet Xiangjie Xiao
Yaohui Pan
Lingling Lv
Yanjun Shi
author_sort Xiangjie Xiao
title Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
title_short Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
title_full Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
title_fullStr Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
title_full_unstemmed Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
title_sort scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
publisher Wiley
series IET Collaborative Intelligent Manufacturing
issn 2516-8398
publishDate 2021-06-01
description Abstract A modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple‐AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi‐mode resource‐constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.
url https://doi.org/10.1049/cim2.12016
work_keys_str_mv AT xiangjiexiao schedulingmultimoderesourceconstrainedtasksofautomatedguidedvehicleswithanimprovedparticleswarmoptimizationalgorithm
AT yaohuipan schedulingmultimoderesourceconstrainedtasksofautomatedguidedvehicleswithanimprovedparticleswarmoptimizationalgorithm
AT linglinglv schedulingmultimoderesourceconstrainedtasksofautomatedguidedvehicleswithanimprovedparticleswarmoptimizationalgorithm
AT yanjunshi schedulingmultimoderesourceconstrainedtasksofautomatedguidedvehicleswithanimprovedparticleswarmoptimizationalgorithm
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