Genetic system for project support with the sequencing problem
One of the main problems faced by manufacturing companies in the production sequencing, also called scheduling, which consists of identifying the best way to order the production program on the machines for improving efficiency. This paper presents the integration of a simulation model with an optim...
- Autores:
-
Viloria, Amelec
Varela, Noel
Herazo-Beltran, Carlos
Pineda Lezama, Omar Bonerge
Mercado, Alberto
Martinez Ventura, Jairo
Hernandez Palma, Hugo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7704
- Acceso en línea:
- https://hdl.handle.net/11323/7704
https://doi.org/10.1007/978-981-15-7234-0_93
https://repositorio.cuc.edu.co/
- Palabra clave:
- Simulation
Programming
Dynamic sequencing
Job shop
Stochastic demand
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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| dc.title.spa.fl_str_mv |
Genetic system for project support with the sequencing problem |
| title |
Genetic system for project support with the sequencing problem |
| spellingShingle |
Genetic system for project support with the sequencing problem Simulation Programming Dynamic sequencing Job shop Stochastic demand |
| title_short |
Genetic system for project support with the sequencing problem |
| title_full |
Genetic system for project support with the sequencing problem |
| title_fullStr |
Genetic system for project support with the sequencing problem |
| title_full_unstemmed |
Genetic system for project support with the sequencing problem |
| title_sort |
Genetic system for project support with the sequencing problem |
| dc.creator.fl_str_mv |
Viloria, Amelec Varela, Noel Herazo-Beltran, Carlos Pineda Lezama, Omar Bonerge Mercado, Alberto Martinez Ventura, Jairo Hernandez Palma, Hugo |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Varela, Noel Herazo-Beltran, Carlos Pineda Lezama, Omar Bonerge Mercado, Alberto Martinez Ventura, Jairo Hernandez Palma, Hugo |
| dc.subject.spa.fl_str_mv |
Simulation Programming Dynamic sequencing Job shop Stochastic demand |
| topic |
Simulation Programming Dynamic sequencing Job shop Stochastic demand |
| description |
One of the main problems faced by manufacturing companies in the production sequencing, also called scheduling, which consists of identifying the best way to order the production program on the machines for improving efficiency. This paper presents the integration of a simulation model with an optimization method to solve the problem of dynamic programming with stochastic demand. |
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2021 |
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2021-01-15T21:46:36Z |
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2021-01-15T21:46:36Z |
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2021 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://hdl.handle.net/11323/7704 |
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https://doi.org/10.1007/978-981-15-7234-0_93 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7704 https://doi.org/10.1007/978-981-15-7234-0_93 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
1. Iassinovski S, Artiba A, Bachelet V (2003) Integration of simulation and optimization for solving complex decision-making problems. Int J Prod Econ 85(1): 3–10. 2. Hamid M, Hamid M, Musavi M, Azadeh A (2019) Scheduling elective patients based on sequence-dependent setup times in an open-heart surgical department using an optimization and simulation approach. Simulation 95(12):1141–1164 3. Zhang B, Yi L-X, Xiao S (2005) Study of stochastic job shop dynamic scheduling. In: Proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou, China, pp 18–21 4. Zhang B, Xu L, Zhang J (2020) A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. J Clean Prod 244:118845 5. Banks J (2000) Introduction to simulation. In: Proceedings of the winter simulation conference, Orlando, FL, USA 6. Mohammadi A, Asadi H, Mohamed S, Nelson K, Nahavandi S (2018) Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Syst Appl 92:73–81 7. Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21 8. Silva EB, Costa MG, Silva MFS (2014) Simulation study of dispatching rules in stochastic job shop dynamic scheduling. World J Modell Simul 10(3):231–240. 9. Mosadegh H, Ghomi SF, Süer GA (2020) Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics. Eur J Oper Res 282(2):530–544 10. Leal F, Costa RFS, Montevechi JAB (2011) A practical guide for operational validation of discrete simulation models. Pesquisa Operacional 31(1):57–77. 11. Kelton WD, Sadowski RP, Sadowski DA (2000) Simulation with ARENA, 2nd edn. McGraw Hill, Boston, USA, pp 385–396. ISBN: 978-0071122399 12. Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, New York, USA, pp 249–273. 13. Wall M (1996) GALIB: A C++ library of genetic algorithm components. Mechanical Engineering Departament, Massachussetts Institute of Technology. 14. Seghir F, Khababa A (2018) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf 29(8):1773–1792 15. Rauf M, Guan Z, Sarfraz S, Mumtaz J, Shehab E, Jahanzaib M, Hanif M (2020) A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines. Robot Comput Integr Manuf 61:101844 16. Kumar M, Khatak P (2020) Development of a discretization methodology for 2.5 D milling toolpath optimization using genetic algorithm. In: Advances in computing and intelligent systems. Springer, Singapore, pp 93–104 17. Rajagopalan A, Modale DR, Senthilkumar R (2020) Optimal scheduling of tasks in cloud computing using hybrid firefly-genetic algorithm. In: Advances in decision sciences, image processing, security and computer vision. Springer, Cham, pp 678–687 18. Rekha PM, Dakshayini M (2019) Efficient task allocation approach using genetic algorithm for cloud environment. Cluster Comput 22(4):1241–1251 |
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Viloria, AmelecVarela, NoelHerazo-Beltran, CarlosPineda Lezama, Omar BonergeMercado, AlbertoMartinez Ventura, JairoHernandez Palma, Hugo2021-01-15T21:46:36Z2021-01-15T21:46:36Z2021https://hdl.handle.net/11323/7704https://doi.org/10.1007/978-981-15-7234-0_93Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One of the main problems faced by manufacturing companies in the production sequencing, also called scheduling, which consists of identifying the best way to order the production program on the machines for improving efficiency. This paper presents the integration of a simulation model with an optimization method to solve the problem of dynamic programming with stochastic demand.Viloria, AmelecVarela, NoelHerazo-Beltran, CarlosPineda Lezama, Omar BonergeMercado, AlbertoMartinez Ventura, JairoHernandez Palma, Hugoapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_93SimulationProgrammingDynamic sequencingJob shopStochastic demandGenetic system for project support with the sequencing problemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Iassinovski S, Artiba A, Bachelet V (2003) Integration of simulation and optimization for solving complex decision-making problems. Int J Prod Econ 85(1): 3–10.2. Hamid M, Hamid M, Musavi M, Azadeh A (2019) Scheduling elective patients based on sequence-dependent setup times in an open-heart surgical department using an optimization and simulation approach. Simulation 95(12):1141–11643. Zhang B, Yi L-X, Xiao S (2005) Study of stochastic job shop dynamic scheduling. In: Proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou, China, pp 18–214. Zhang B, Xu L, Zhang J (2020) A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. J Clean Prod 244:1188455. Banks J (2000) Introduction to simulation. In: Proceedings of the winter simulation conference, Orlando, FL, USA6. Mohammadi A, Asadi H, Mohamed S, Nelson K, Nahavandi S (2018) Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Syst Appl 92:73–817. Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–218. Silva EB, Costa MG, Silva MFS (2014) Simulation study of dispatching rules in stochastic job shop dynamic scheduling. World J Modell Simul 10(3):231–240.9. Mosadegh H, Ghomi SF, Süer GA (2020) Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics. Eur J Oper Res 282(2):530–54410. Leal F, Costa RFS, Montevechi JAB (2011) A practical guide for operational validation of discrete simulation models. Pesquisa Operacional 31(1):57–77.11. Kelton WD, Sadowski RP, Sadowski DA (2000) Simulation with ARENA, 2nd edn. McGraw Hill, Boston, USA, pp 385–396. ISBN: 978-007112239912. Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, New York, USA, pp 249–273.13. Wall M (1996) GALIB: A C++ library of genetic algorithm components. Mechanical Engineering Departament, Massachussetts Institute of Technology.14. Seghir F, Khababa A (2018) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf 29(8):1773–179215. Rauf M, Guan Z, Sarfraz S, Mumtaz J, Shehab E, Jahanzaib M, Hanif M (2020) A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines. Robot Comput Integr Manuf 61:10184416. Kumar M, Khatak P (2020) Development of a discretization methodology for 2.5 D milling toolpath optimization using genetic algorithm. In: Advances in computing and intelligent systems. Springer, Singapore, pp 93–10417. Rajagopalan A, Modale DR, Senthilkumar R (2020) Optimal scheduling of tasks in cloud computing using hybrid firefly-genetic algorithm. In: Advances in decision sciences, image processing, security and computer vision. Springer, Cham, pp 678–68718. Rekha PM, Dakshayini M (2019) Efficient task allocation approach using genetic algorithm for cloud environment. 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