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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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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1724471505422647296 |