- -

Digital twin for supply chain master planning in zero-defect manufacturing

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Digital twin for supply chain master planning in zero-defect manufacturing

Mostrar el registro completo del ítem

Serrano, JC.; Mula, J.; Poler, R. (2021). Digital twin for supply chain master planning in zero-defect manufacturing. IFIP Advances in Information and Communication Technology. 626:102-111. https://doi.org/10.1007/978-3-030-78288-7_10

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/188312

Ficheros en el ítem

Metadatos del ítem

Título: Digital twin for supply chain master planning in zero-defect manufacturing
Autor: Serrano, Julio C. Mula, Josefa Poler, R.
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Fecha difusión:
Resumen:
[EN] Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply ...[+]
Palabras clave: Supply chain 4.0 , Master production schedule , Digital twin , Reinforcement learning , Zero-defect manufacturing , Conceptual framework
Derechos de uso: Reserva de todos los derechos
Fuente:
IFIP Advances in Information and Communication Technology. (issn: 1868-4238 )
DOI: 10.1007/978-3-030-78288-7_10
Editorial:
Springer
Versión del editor: https://doi.org/10.1007/978-3-030-78288-7_10
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-101344-B-I00/ES/OPTIMIZACION DE TECNOLOGIAS DE PRODUCCION CERO-DEFECTOS HABILITADORAS PARA CADENAS DE SUMINISTRO 4.0/
info:eu-repo/grantAgreement/EC/H2020/825631/EU
info:eu-repo/grantAgreement/EC/H2020/958205/EU
Agradecimientos:
The research leading to these results received funding from the EuropeanUnion H2020 Program with grant agreement No. 825631 Zero Defect Manufacturing Platform(ZDMP) and with grant agreement No. 958205 Industrial Data ...[+]
Tipo: Artículo

References

Marmolejo-Saucedo, J.A., Hartmann, S.: Trends in digitization of the supply chain: a brief literature review. EAI End. Trans. Energy Web 7(29), e8 (2020)

Büyüközkan, G., Göçer, F.: Digital supply chain: literature review and a proposed framework for future research. Comput. Ind. 97, 157–177 (2018)

Feldt, J., Kourouklis, T., Kontny, H., Wagenitz, A., Teti, R., D’Addona, D.M.: Digital twin: revealing potentials of real-time autonomous decisions at a manufacturing company. Procedia CIRP 88, 185–190 (2020) [+]
Marmolejo-Saucedo, J.A., Hartmann, S.: Trends in digitization of the supply chain: a brief literature review. EAI End. Trans. Energy Web 7(29), e8 (2020)

Büyüközkan, G., Göçer, F.: Digital supply chain: literature review and a proposed framework for future research. Comput. Ind. 97, 157–177 (2018)

Feldt, J., Kourouklis, T., Kontny, H., Wagenitz, A., Teti, R., D’Addona, D.M.: Digital twin: revealing potentials of real-time autonomous decisions at a manufacturing company. Procedia CIRP 88, 185–190 (2020)

Marmolejo-Saucedo, J.A., Hurtado-Hernandez, M., Suarez-Valdes, R.: Digital twins in supply chain management: a brief literature review. In: Vasant, P., Zelinka, I., Weber, G.-W. (eds.) ICO. AISC, vol. 1072, pp. 653–661. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33585-4_63

Angione, G., Cristalli, C., Barbosa, J., Leitão, P.: Integration challenges for the deployment of a multi-stage zero-defect manufacturing architecture. In: International Conference on Industrial Informatics (INDIN), Institute Electrical and Electronics Engineers Inc., pp. 1615–1620 (2019)

Usuga, J.P., Lamouri, S., Grabot, B., Pellerin, R., Fortin, A.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf 31(6), 1531–1558 (2020)

Abu-Bakar, M., Abbas, I., Kalal, M., Alsattar, H., Bakhayt, A.G., Kalaf, B.: Solution for multi-objective optimisation master production scheduling problems based on swarm intelligence algorithms. J. Comput. Theoret. Nanosci. 14, 5184–5194 (2017)

Ould-Louly, M.A., Dolgui, A.: The MPS planning under lead time uncertainty. In: Proceeding of the Workshop on Production Planning and Control, pp. 148–155 (2020)

Stich, V., Adema, J., Blum, M., Reschke, J.: Supply chain 4.0: Logistikdienstleister im Kontext der vierten industriellen Revolution. In: Voß, P.H. (ed.) Logistik – eine Industrie, die (sich) bewegt, pp. 63–76. Springer, Wiesbaden (2015). https://doi.org/10.1007/978-3-658-10609-6_6

Frederico, G.F., Garza-Reyes, J.A., Anosike, A., Kumar, V.: Supply chain 4.0: concepts, maturity and research agenda. Supply Chain Manage. 25, 262–282 (2019)

Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y., Garza-Reyes, J.A.: Supply chain management 4.0: a literature review and research framework. Benchmarking 28, 465–501 (2020)

Maryniak, A., Bulhakova, Y., Lewoniewski, W., Bal, M.: Diffusion of knowledge in the supply chain over thirty years - thematic areas and sources of publications. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds.) ICIST. CCIS, vol. 1283, pp. 113–126. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59506-7_10

Chern, C.-C., Lei, S.-T., Huang, K.-L.: Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network. J. Intell. Manuf. 25, 1–25 (2014)

Grillo, H., Peidro, D., Alemany, M.M.E., Mula, J.: Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. Int. J. Bio-Inspired Comput. 7, 157–169 (2015)

Sutthibutr, N., Chiadamrong, N.: Applied fuzzy multi-objective with α-cut analysis for optimizing supply chain master planning problem. In: ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 84–91 (2019)

Arani, H.V., Torabi, S.A.: Integrated material-financial supply chain master planning under mixed uncertainty. Inf. Sci. 423, 96–114 (2018)

Ghasemy-Yaghin, R., Sarlak, P., Ghareaghaji, A.A.: Robust master planning of a socially responsible supply chain under fuzzy-stochastic uncertainty (a case study of clothing industry). Eng. Appl. Artif. Intell. 94, 103715 (2020)

Martín, A.G., Díaz-Madroñero, M., Mula, J.: Master production schedule using robust optimization approaches in an automobile second-tier supplier. CEJOR 28(1), 143–166 (2020)

Peidro, D., Mula, J., Alemany, M.M.E., Lario, F.-C.: Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. Int. J. Prod. Res. 50, 3011–3020 (2012)

Orozco-Romero, A., Arias-Portela, C.Y., Saucedo, J.: The use of agent-based models boosted by digital twins in the supply chain: a literature review. In: Vasant, P., Zelinka, I., Weber, G.-W. (eds.) ICO. AISC, vol. 1072, pp. 642–652. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33585-4_62

Barykin, S.Y., Bochkarev, A.A., Kalinina, O.V., Yadykin, V.K.: Concept for a supply chain digital twin. Int. J. Math. Eng. Manage. Sci. 5, 1498–1515 (2020)

Ivanov, D., Dolgui, A., Das, A., Sokolov, B.: Digital supply chain twins: managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds.) Handbook of Ripple Effects in the Supply Chain. ISORMS, vol. 276, pp. 309–332. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14302-2_15

Ivanov, D., Das, A.: Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note. Int. J. Integr. Supply Manage. 13, 90–102 (2020)

Dolgui, A., Ivanov, D., Sokolov, B.: Reconfigurable supply chain: the X-network. Int. J. Prod. Res. 58, 4138–4163 (2020)

Park, K.T., Son, Y.H., Noh, S.D.: The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. Int. J. Prod. Res., 1–22 (2020)

Wang, Y., Wang, X., Liu, A., Gao, R.X., Ehmann, K.: Digital twin-driven supply chain planning. Procedia CIRP 93, 198–203 (2020)

Alves, J.C., Mateus, G.R.: Deep reinforcement learning and optimization approach for multi-echelon supply chain with uncertain demands. In: Lalla-Ruiz, E., Mes, M., Voß, S. (eds.) ICCL. LNCS, vol. 12433, pp. 584–599. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59747-4_38

Peng, Z., Zhang, Y., Feng, Y., Zhang, T., Wu, Z., Su, H.: Deep reinforcement learning approach for capacitated supply chain optimization under demand uncertainty. In: Proceedings Chinese Automation Congress, CAC, Institute of Electrical and Electronics Engineers Inc., pp. 3512–3517 (2019)

Lauer, T., Legner, S.: Plan instability prediction by machine learning in master production planning. In: International Conference on Automation Science and Engineering (CASE), Institute of Electrical and Electronics Engineers Inc., pp. 703–708 (2019)

Siddh, M.M., Soni, G., Gadekar, G., Jain, R.: Integrating lean six sigma and supply chain approach for quality and business performance. In: International Conference on Business and Information Management, ICBIM, Institute of Electrical and Electronics Engineers Inc., pp. 53–57 (2014)

Pardamean, G.D., Wibisono, E.: A framework for the impact of lean six sigma on supply chain performance in manufacturing companies. In: IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing Ltd., vol. 528 (2019)

Poornachandrika, V., Venkatasudhakar, M.: Quality transformation to improve customer satisfaction: using product, process, system and behaviour model. In: IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing Ltd., vol. 923 (2020)

Thakur, V., Mangla, S.K.: Change management for sustainability: evaluating the role of human, operational and technological factors in leading Indian firms in home appliances sector. J. Clean. Prod. 213, 847–862 (2019)

Lindström, J., Kyösti, P., Birk, W., Lejon, E.: An initial model for zero defect manufacturing. Appl. Sci. 10, 4570 (2020)

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem