Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm

<p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container tran...
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Gespeichert in:

Autor*in:	Anita Gudelj [verfasserIn] Danko Kezić [verfasserIn] Stjepan Vidačić [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization

Übergeordnetes Werk:	In: Journal of Information and Organizational Sciences - University of Zagreb, Faculty of organization and informatics, 2009, 36(2012), 2
Übergeordnetes Werk:	volume:36 ; year:2012 ; number:2

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ004809599

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allfields	(DE-627)DOAJ004809599 (DE-599)DOAJ973f5b194f344705b678153ed3382f33 DE-627 ger DE-627 rakwb eng Q350-390 Anita Gudelj verfasserin aut Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p< Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization Information theory Danko Kezić verfasserin aut Stjepan Vidačić verfasserin aut In Journal of Information and Organizational Sciences University of Zagreb, Faculty of organization and informatics, 2009 36(2012), 2 (DE-627)603489141 (DE-600)2501910-7 18469418 nnns volume:36 year:2012 number:2 https://doaj.org/article/973f5b194f344705b678153ed3382f33 kostenfrei http://jios.foi.hr/index.php/jios/article/view/596 kostenfrei https://doaj.org/toc/1846-3312 Journal toc kostenfrei https://doaj.org/toc/1846-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2012 2
spelling	(DE-627)DOAJ004809599 (DE-599)DOAJ973f5b194f344705b678153ed3382f33 DE-627 ger DE-627 rakwb eng Q350-390 Anita Gudelj verfasserin aut Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p< Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization Information theory Danko Kezić verfasserin aut Stjepan Vidačić verfasserin aut In Journal of Information and Organizational Sciences University of Zagreb, Faculty of organization and informatics, 2009 36(2012), 2 (DE-627)603489141 (DE-600)2501910-7 18469418 nnns volume:36 year:2012 number:2 https://doaj.org/article/973f5b194f344705b678153ed3382f33 kostenfrei http://jios.foi.hr/index.php/jios/article/view/596 kostenfrei https://doaj.org/toc/1846-3312 Journal toc kostenfrei https://doaj.org/toc/1846-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2012 2
allfields_unstemmed	(DE-627)DOAJ004809599 (DE-599)DOAJ973f5b194f344705b678153ed3382f33 DE-627 ger DE-627 rakwb eng Q350-390 Anita Gudelj verfasserin aut Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p< Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization Information theory Danko Kezić verfasserin aut Stjepan Vidačić verfasserin aut In Journal of Information and Organizational Sciences University of Zagreb, Faculty of organization and informatics, 2009 36(2012), 2 (DE-627)603489141 (DE-600)2501910-7 18469418 nnns volume:36 year:2012 number:2 https://doaj.org/article/973f5b194f344705b678153ed3382f33 kostenfrei http://jios.foi.hr/index.php/jios/article/view/596 kostenfrei https://doaj.org/toc/1846-3312 Journal toc kostenfrei https://doaj.org/toc/1846-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2012 2
allfieldsGer	(DE-627)DOAJ004809599 (DE-599)DOAJ973f5b194f344705b678153ed3382f33 DE-627 ger DE-627 rakwb eng Q350-390 Anita Gudelj verfasserin aut Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p< Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization Information theory Danko Kezić verfasserin aut Stjepan Vidačić verfasserin aut In Journal of Information and Organizational Sciences University of Zagreb, Faculty of organization and informatics, 2009 36(2012), 2 (DE-627)603489141 (DE-600)2501910-7 18469418 nnns volume:36 year:2012 number:2 https://doaj.org/article/973f5b194f344705b678153ed3382f33 kostenfrei http://jios.foi.hr/index.php/jios/article/view/596 kostenfrei https://doaj.org/toc/1846-3312 Journal toc kostenfrei https://doaj.org/toc/1846-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2012 2
allfieldsSound	(DE-627)DOAJ004809599 (DE-599)DOAJ973f5b194f344705b678153ed3382f33 DE-627 ger DE-627 rakwb eng Q350-390 Anita Gudelj verfasserin aut Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p< Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization Information theory Danko Kezić verfasserin aut Stjepan Vidačić verfasserin aut In Journal of Information and Organizational Sciences University of Zagreb, Faculty of organization and informatics, 2009 36(2012), 2 (DE-627)603489141 (DE-600)2501910-7 18469418 nnns volume:36 year:2012 number:2 https://doaj.org/article/973f5b194f344705b678153ed3382f33 kostenfrei http://jios.foi.hr/index.php/jios/article/view/596 kostenfrei https://doaj.org/toc/1846-3312 Journal toc kostenfrei https://doaj.org/toc/1846-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2012 2
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author	Anita Gudelj
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topic_title	Q350-390 Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization
topic	misc Q350-390 misc Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization misc Information theory
topic_unstemmed	misc Q350-390 misc Container terminal, AGV job scheduling, Petri net, Genetic algorithm, Deadlock avoidance, Optimization misc Information theory
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title	Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm
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title_full	Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm
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title_sort	planning and optimization of agv jobs by petri net and genetic algorithm
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title_auth	Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm
abstract	<p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p<
abstractGer	<p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p<
abstract_unstemmed	<p class="Abstract"<The following article presents the possibilities of job optimization on a maritime container terminal, in order to increase the system productivity and optimize the terminal capacity. Automated guided vehicles (AGVs) are now becoming popular mode of container transport in seaport terminals. The moving of vehicles can be described as the set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and block the vehicles’ moving only in the case of dangerous situation.<br /<This paper addresses the use a Petri net as modeling and scheduling tool in this context. The aim of AGV scheduling is to dispatch a set of AGVs to improve the productivity of a system and reduce delay in a batch of pickup/drop-off jobs under certain constraints such as deadlines, priority, etc. The final goals are related to optimization of processing time and minimization of the number of AGVs involved while maintaining the system throughput.<br /<To find better solutions, the authors propose the integration MRF1 class of Petri net (MRF1PN) with a genetic algorithm. Also, the use of a matrix based formal method is proposed to analyze discrete event dynamic system (DEDS). The algorithm is described to deal with multi-project, multi-constrained scheduling problem with shared resources. The developed model was tested and validated by simulation of typical scenarios of the container terminal of Port Koper. Modularity and simplicity of the approach allow using the model to monitor and test the efficiency of the processes, and also to propose future alternative solutions to optimize the schedule of operations and the employment of AGV at the terminal.</p<
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title_short	Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm
url	https://doaj.org/article/973f5b194f344705b678153ed3382f33 http://jios.foi.hr/index.php/jios/article/view/596 https://doaj.org/toc/1846-3312 https://doaj.org/toc/1846-9418
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Planning and Optimization of AGV Jobs by Petri Net and Genetic Algorithm

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