Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper,...

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Main Authors: Moussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/18/4948
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spelling doaj-d7a109dfa43b4061bf22bfc5901076ca2020-11-25T03:54:58ZengMDPI AGEnergies1996-10732020-09-01134948494810.3390/en13184948Manufacturing 4.0 Operations Scheduling with AGV Battery Management ConstraintsMoussa Abderrahim0Abdelghani Bekrar1Damien Trentesaux2Nassima Aissani3Karim Bouamrane4Laboratoire d’Informatique d’Oran (LIO), Université Oran1, Oran 31000, AlgeriaLAMIH, UMR CNRS 8201, UPHF, 59300 Valenciennes, FranceLAMIH, UMR CNRS 8201, UPHF, 59300 Valenciennes, FranceLaboratoire de l’Ingénierie de la Sécurité Industrielle et du Développement Durable, Université Oran2, Oran 31000, AlgeriaLaboratoire d’Informatique d’Oran (LIO), Université Oran1, Oran 31000, AlgeriaThe industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.https://www.mdpi.com/1996-1073/13/18/4948energy optimizationjob-shop schedulingtransport constraintsautomated guided vehiclesbattery management
collection DOAJ
language English
format Article
sources DOAJ
author Moussa Abderrahim
Abdelghani Bekrar
Damien Trentesaux
Nassima Aissani
Karim Bouamrane
spellingShingle Moussa Abderrahim
Abdelghani Bekrar
Damien Trentesaux
Nassima Aissani
Karim Bouamrane
Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
Energies
energy optimization
job-shop scheduling
transport constraints
automated guided vehicles
battery management
author_facet Moussa Abderrahim
Abdelghani Bekrar
Damien Trentesaux
Nassima Aissani
Karim Bouamrane
author_sort Moussa Abderrahim
title Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
title_short Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
title_full Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
title_fullStr Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
title_full_unstemmed Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints
title_sort manufacturing 4.0 operations scheduling with agv battery management constraints
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-09-01
description The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.
topic energy optimization
job-shop scheduling
transport constraints
automated guided vehicles
battery management
url https://www.mdpi.com/1996-1073/13/18/4948
work_keys_str_mv AT moussaabderrahim manufacturing40operationsschedulingwithagvbatterymanagementconstraints
AT abdelghanibekrar manufacturing40operationsschedulingwithagvbatterymanagementconstraints
AT damientrentesaux manufacturing40operationsschedulingwithagvbatterymanagementconstraints
AT nassimaaissani manufacturing40operationsschedulingwithagvbatterymanagementconstraints
AT karimbouamrane manufacturing40operationsschedulingwithagvbatterymanagementconstraints
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