- -

Validation of production system throughput potential and simulation experiment design

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Validation of production system throughput potential and simulation experiment design

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Standridge, C. es_ES
dc.contributor.author Wynne, M. es_ES
dc.date.accessioned 2021-02-17T10:40:28Z
dc.date.available 2021-02-17T10:40:28Z
dc.date.issued 2021-01-29
dc.identifier.uri http://hdl.handle.net/10251/161641
dc.description.abstract [EN] The throughput potential of a production system must be designed and validated before implementation.  Design includes creating product flow by setting the takt time consistent with meeting customer demand per time period and the average cycle time at each workstation being less than the takt time.  Creating product flow implies that the average waiting time preceding each workstation is no greater than the takt time.  Kingman’s equation for the average waiting time can be solved for the variation component given the utilization, and the cycle time.  The variation component consists of the variation in the demand and the variation in cycle time.  Given the variation in demand, the maximum allowable variation in cycle time to create flow can be determined.  Throughput potential validation is often performed using discrete event simulation modeling and experimentation.  If the variation in cycle time at every workstation is small enough to create flow, then a deterministic simulation experiment can be used.  An industrial example concerning a tier-1 automotive supplier with two possible production systems designs and various levels of variation in demand assumed is used to demonstrate the effectiveness of throughput validation using deterministic discrete event simulation modeling and experimentation. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof International Journal of Production Management and Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Throughput potential validation es_ES
dc.subject Kingman’s equation es_ES
dc.subject Discrete event simulation es_ES
dc.title Validation of production system throughput potential and simulation experiment design es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/ijpme.2021.14483
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Standridge, C.; Wynne, M. (2021). Validation of production system throughput potential and simulation experiment design. International Journal of Production Management and Engineering. 9(1):15-23. https://doi.org/10.4995/ijpme.2021.14483 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2021.14483 es_ES
dc.description.upvformatpinicio 15 es_ES
dc.description.upvformatpfin 23 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 2340-4876
dc.relation.pasarela OJS\14483 es_ES
dc.description.references Atalan, A., Dönmez, C.C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), e2020854. https://doi.org/10.14488/BJOPM.2020.026 es_ES
dc.description.references Askin, R.G., Standridge, C.R. (1993). Modeling and analysis of manufacturing systems. New York: John Wiley and Sons. es_ES
dc.description.references Dagkakis, G., Rotondo, A., Heavey, C. (2019). Embedding optimization with deterministic discrete event simulation for assignment of cross-trained operators: an assembly line case study. Computers and Operations Research, 111, 99-115. https://doi.org/10.1016/j.cor.2019.06.008 es_ES
dc.description.references Ferrin, D.M., Miller M.J., Muthler D. (2005). Lean sigma and simulation, so what's the correlation?, in Proceedings of the 2005 Winter Simulation Conference, IEEE, USA. Retrieved July 22, 2020 from: https://informs-sim.org/wsc05papers/249.pdf es_ES
dc.description.references Hopp, W.J., Spearman, M.L. (2011). Factory Physics: Foundations of manufacturing management, 3rd ed. Long Grove, IL: Waveland Press. es_ES
dc.description.references Jayaraman, A., Gunal, A.K. (1997). Applications of discrete event simulation in the design of automotive powertrain manufacturing systems". In Proceedings of the 1997 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/268437.268620 es_ES
dc.description.references Khan, S., Standridge, C.R. (2019). Aggregate simulation modeling with application to setting the CONWIP limit in an HMLV cell. International Journal of Industrial Engineering Computation, 10(2), 149-160. https://doi.org/10.5267/j.ijiec.2018.10.002 es_ES
dc.description.references Kingman, J.F.C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902. https://doi.org/10.1017/S0305004100036094 es_ES
dc.description.references Kleijnen, J.P.C. (2015). Design and analysis of simulation experiments. New York: Springer. https://doi.org/10.1007/978-3-319-18087-8 es_ES
dc.description.references Kleijnen, J.P.C., Standridge, C.R. (1988). Experimental design and regression analysis in simulation: an FMS case study. European Journal of Operations Research, 33, 257-261. https://doi.org/10.1016/0377-2217(88)90168-3 es_ES
dc.description.references Law, A.M. (2014). Simulation modeling and analysis, 5th ed. New York: McGraw-Hill. es_ES
dc.description.references Little, J.D.C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383-387. https://doi.org/10.1287/opre.9.3.383 es_ES
dc.description.references Marvel, J.H., Standridge, C.R. (2009). A simulation enhanced lean design process. Journal of Industrial Engineering and Management, 2(1), 90-113. https://doi.org/10.3926/jiem.2009.v2n1.p90-113 es_ES
dc.description.references Mourtzis, D. (2019) Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949. https://doi.org/10.1080/00207543.2019.1636321 es_ES
dc.description.references Pinheiro, N.M.G, Cleto, M.G., Zattar, I.C., Muller, S.I.M.G. (2019). Performance evaluation of pulled, pushed and hybrid production through simulation: a case study. Brazilian Journal of Operations & Production Management, 16, 685-697. https://doi.org/10.14488/BJOPM.2019.v16.n4.a13 es_ES
dc.description.references Pritsker, A.A.B. (1989). Why simulation works. In Proceedings of the 1989 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/76738.76739 es_ES
dc.description.references Puvanasvaran, P., Teoh, Y.S., Ito, K. (2020). Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company. Journal of Industrial Engineering and Management, 13(1), 1-17. https://doi.org/10.3926/jiem.2755 es_ES
dc.description.references Sanchez, S.M., Sanchez, P.J., Wan, H. (2020). Work smarter, not harder: a tutorial on designing and conducting simulation experiments. In Proceedings of the 2020 Winter Simulation Conference, IEEE, USA. Retrieved December 23, 2020 from https://informs-sim.org/ wsc20papers/135.pdf es_ES
dc.description.references Schruben, L. (1983). Simulation modeling with event graphs. Communications of the A.C.M., 26(11). https://doi.org/10.1145/182.358460 es_ES
dc.description.references Spearman, M.L., Woodruff, D.L., Hopp, W.J. (1990). CONWIP: A pull alternative to Kanban, International Journal of Production Research, 28(5), 879-894. https://doi.org/10.1080/00207549008942761 es_ES
dc.description.references Standridge, C.R. (2019). Introduction to production: philosophies, flow, and analysis. Allendale Michigan: Grand Valley State University Libraries. Retrieved July 22, 2020 from: https://scholarworks.gvsu.edu/books/22/ es_ES
dc.description.references Tapping, D., Luyster, T., Shuker, T. (2002). Value stream management. Boca Raton: CRC Press. https://doi.org/10.4324/9781482278163 es_ES
dc.description.references Tribastone, M., Vandin, A. (2018). Speeding up stochastic and deterministic simulation by aggregation: an advanced tutorial. . In Proceedings of the 2018 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1109/WSC.2018.8632364 es_ES
dc.description.references Uriarte, A.G., Ng, A.H.C., Moris, M.U. (2020). Bringing together Lean and simulation: a comprehensive review, International Journal of Production Research, 58(1), 87-117. https://doi.org/10.1080/00207543.2019.1643512 es_ES
dc.description.references Zupan, H., Herakovic. N. (2015). Production line balancing with discrete event simulation: a case study", IFAC-PapersOnLine, 48(3), 2305- 2311. https://doi.org/10.1016/j.ifacol.2015.06.431 es_ES


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

Mostrar el registro sencillo del ítem