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Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints

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Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints

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Min, D.; Tang, D.; Giret Boggino, AS.; Salido, MA. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing. 59:143-157. https://doi.org/10.1016/j.rcim.2019.04.006

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Título: Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints
Autor: Min, Dai Tang, Dunbing Giret Boggino, Adriana Susana Salido, Miguel A.
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing ...[+]
Palabras clave: Flexible job shop scheduling problems , Multi-objective optimization , Energy consumption , Genetic algorithm , Transportation time
Derechos de uso: Cerrado
Fuente:
Robotics and Computer-Integrated Manufacturing. (issn: 0736-5845 )
DOI: 10.1016/j.rcim.2019.04.006
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.rcim.2019.04.006
Código del Proyecto:
info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//NP2017105/
...[+]
info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//NP2017105/
info:eu-repo/grantAgreement/Natural Science Research of Jiangsu Higher Education Institutions of China//17KJB460018/
info:eu-repo/grantAgreement/YZU//2016CXJ020/
info:eu-repo/grantAgreement/YZU//2017CXJ018/
info:eu-repo/grantAgreement/Yangzhou Science and Technology Bureau//YZ2017278/
info:eu-repo/grantAgreement/YZU//YZUJX2018-28B/
info:eu-repo/grantAgreement/NSFC//U1637211/
info:eu-repo/grantAgreement/Aeronautical Science Foundation of China//20161652015/
info:eu-repo/grantAgreement/MINECO//TIN2015-65515-C4-1-R/ES/ARQUITECTURA PERSUASIVA PARA EL USO SOSTENIBLE E INTELIGENTE DE VEHICULOS EN FLOTAS URBANAS/
info:eu-repo/grantAgreement/MINECO//TIN2016-80856-R/ES/TECNOLOGIAS INTELIGENTES PARA LA RESOLUCION CENTRALIZADA Y DISTRIBUIDA DE PROBLEMAS DE SCHEDULING SOSTENIBLE EN PROCESOS INDUSTRIALES Y LOGISTICOS/
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Agradecimientos:
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant no. U1637211, Aeronautical Science Foundation of China under Grant no. 20161652015, the Fundamental Research Funds for the ...[+]
Tipo: Artículo

References

EIA, U.S.Energy Information Administration. International Energy Outlook 2016.

Liu, Y., Dong, H., Lohse, N., Petrovic, S., & Gindy, N. (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87-96. doi:10.1016/j.jclepro.2013.07.060

Apostolos, F., Alexios, P., Georgios, P., Panagiotis, S., & George, C. (2013). Energy Efficiency of Manufacturing Processes: A Critical Review. Procedia CIRP, 7, 628-633. doi:10.1016/j.procir.2013.06.044 [+]
EIA, U.S.Energy Information Administration. International Energy Outlook 2016.

Liu, Y., Dong, H., Lohse, N., Petrovic, S., & Gindy, N. (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87-96. doi:10.1016/j.jclepro.2013.07.060

Apostolos, F., Alexios, P., Georgios, P., Panagiotis, S., & George, C. (2013). Energy Efficiency of Manufacturing Processes: A Critical Review. Procedia CIRP, 7, 628-633. doi:10.1016/j.procir.2013.06.044

Peng, T., & Xu, X. (2014). Energy-efficient machining systems: a critical review. The International Journal of Advanced Manufacturing Technology, 72(9-12), 1389-1406. doi:10.1007/s00170-014-5756-0

Moreira, L. C., Li, W. D., Lu, X., & Fitzpatrick, M. E. (2019). Energy-Efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies. Robotics and Computer-Integrated Manufacturing, 58, 1-12. doi:10.1016/j.rcim.2019.01.011

Gadaleta, M., Pellicciari, M., & Berselli, G. (2019). Optimization of the energy consumption of industrial robots for automatic code generation. Robotics and Computer-Integrated Manufacturing, 57, 452-464. doi:10.1016/j.rcim.2018.12.020

Gahm, C., Denz, F., Dirr, M., & Tuma, A. (2016). Energy-efficient scheduling in manufacturing companies: A review and research framework. European Journal of Operational Research, 248(3), 744-757. doi:10.1016/j.ejor.2015.07.017

Giret, A., Trentesaux, D., & Prabhu, V. (2015). Sustainability in manufacturing operations scheduling: A state of the art review. Journal of Manufacturing Systems, 37, 126-140. doi:10.1016/j.jmsy.2015.08.002

Akbar, M., & Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866-883. doi:10.1016/j.jclepro.2018.09.100

Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271. doi:10.1080/00207540701450013

Mouzon, G., & Yildirim, M. B. (2008). A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering, 1(2), 105-116. doi:10.1080/19397030802257236

Yildirim, M. B., & Mouzon, G. (2012). Single-Machine Sustainable Production Planning to Minimize Total Energy Consumption and Total Completion Time Using a Multiple Objective Genetic Algorithm. IEEE Transactions on Engineering Management, 59(4), 585-597. doi:10.1109/tem.2011.2171055

Shrouf, F., Ordieres-Meré, J., García-Sánchez, A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197-207. doi:10.1016/j.jclepro.2013.12.024

Che, A., Wu, X., Peng, J., & Yan, P. (2017). Energy-efficient bi-objective single-machine scheduling with power-down mechanism. Computers & Operations Research, 85, 172-183. doi:10.1016/j.cor.2017.04.004

Fang, K., Uhan, N., Zhao, F., & Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234-240. doi:10.1016/j.jmsy.2011.08.004

Fang, K., Uhan, N. A., Zhao, F., & Sutherland, J. W. (2013). Flow shop scheduling with peak power consumption constraints. Annals of Operations Research, 206(1), 115-145. doi:10.1007/s10479-012-1294-z

Ding, J.-Y., Song, S., & Wu, C. (2016). Carbon-efficient scheduling of flow shops by multi-objective optimization. European Journal of Operational Research, 248(3), 758-771. doi:10.1016/j.ejor.2015.05.019

Mansouri, S. A., Aktas, E., & Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788. doi:10.1016/j.ejor.2015.08.064

Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (2017). Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm. Journal of Cleaner Production, 144, 228-238. doi:10.1016/j.jclepro.2017.01.011

Bruzzone, A. A. G., Anghinolfi, D., Paolucci, M., & Tonelli, F. (2012). Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP Annals, 61(1), 459-462. doi:10.1016/j.cirp.2012.03.084

Luo, H., Du, B., Huang, G. Q., Chen, H., & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal of Production Economics, 146(2), 423-439. doi:10.1016/j.ijpe.2013.01.028

Yan, J., Li, L., Zhao, F., Zhang, F., & Zhao, Q. (2016). A multi-level optimization approach for energy-efficient flexible flow shop scheduling. Journal of Cleaner Production, 137, 1543-1552. doi:10.1016/j.jclepro.2016.06.161

Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418-429. doi:10.1016/j.rcim.2013.04.001

Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, 259-272. doi:10.1016/j.ijpe.2016.06.019

May, G., Stahl, B., Taisch, M., & Prabhu, V. (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071-7089. doi:10.1080/00207543.2015.1005248

Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112, 3361-3375. doi:10.1016/j.jclepro.2015.09.097

Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2015). A genetic algorithm for energy-efficiency in job-shop scheduling. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1303-1314. doi:10.1007/s00170-015-7987-0

Mokhtari, H., & Hasani, A. (2017). An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Computers & Chemical Engineering, 104, 339-352. doi:10.1016/j.compchemeng.2017.05.004

Liu, Q., Zhan, M., Chekem, F. O., Shao, X., Ying, B., & Sutherland, J. W. (2017). A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. Journal of Cleaner Production, 168, 668-678. doi:10.1016/j.jclepro.2017.09.037

Wang, H., Jiang, Z., Wang, Y., Zhang, H., & Wang, Y. (2018). A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. Journal of Cleaner Production, 188, 575-588. doi:10.1016/j.jclepro.2018.03.254

Gong, G., Deng, Q., Gong, X., Liu, W., & Ren, Q. (2018). A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. Journal of Cleaner Production, 174, 560-576. doi:10.1016/j.jclepro.2017.10.188

Rossi, A., & Dini, G. (2007). Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robotics and Computer-Integrated Manufacturing, 23(5), 503-516. doi:10.1016/j.rcim.2006.06.004

Zhang, Q., Manier, H., & Manier, M.-A. (2013). A modified shifting bottleneck heuristic and disjunctive graph for job shop scheduling problems with transportation constraints. International Journal of Production Research, 52(4), 985-1002. doi:10.1080/00207543.2013.828164

Karimi, S., Ardalan, Z., Naderi, B., & Mohammadi, M. (2017). Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 41, 667-682. doi:10.1016/j.apm.2016.09.022

He, Y., Li, Y., Wu, T., & Sutherland, J. W. (2015). An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. Journal of Cleaner Production, 87, 245-254. doi:10.1016/j.jclepro.2014.10.006

Liu, F., Xie, J., & Liu, S. (2015). A method for predicting the energy consumption of the main driving system of a machine tool in a machining process. Journal of Cleaner Production, 105, 171-177. doi:10.1016/j.jclepro.2014.09.058

Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4), 1309-1318. doi:10.1016/j.cie.2008.07.021

Liou, C.-D., & Hsieh, Y.-C. (2015). A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times. International Journal of Production Economics, 170, 258-267. doi:10.1016/j.ijpe.2015.10.002

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