Modeling the rhythm of human work in a simulation project through multiple statistical probability distributions
DOI:
https://doi.org/10.14488/1676-1901.v21i4.4496Keywords:
Pace of Work, Computational Validation, Discrete Event SimulationAbstract
Normally, discrete event simulation (DES) projects consider employees as common resources. This considered premise becomes a problem in the modeling of production systems, especially if the process to be modeled presents a high amount of manual work. In this context, this research applies a computational validation method to analyze the outputs of a simulation model when different statistical distributions, represented in four scenarios, are used in the input times. The simulated model was obtained from an assembly line of an electronics company located in the city of Santa Rita do Sapucaí-MG. Therefore, the objective of this research is to define and apply an approach to consider the variation in the pace of human work in DES projects. For this purpose, four scenarios were created, each with their respective distributions. These statistical distributions were generated from the StatFit® tool, which used timed data from the workday in which the assembly line was submitted. As research method, modeling and simulation using real data was used. Finally, the results of the computational validation show that there was validation only for Scenario 3 in all considerations for the level of confidence used in the validation, which corroborates the initial assumptions about the variation in the pace of human work and its influence on the validation of the computational model.
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BAINES, T.; MASON, S.; PEER-OLAF, S.; LADBROOK, J. Humans: the missing link in manufacturing simulation? Simulation Modelling Practice and Theory, v.12, p.515-526, 2004. https://doi.org/10.1016/S1569-190X(03)00094-7
BRAILSFORD, S.C. Modeling human behavior –an (id)entity crisis? Proceedings of the Winter Simulation Conference, Savannah, USA, 2014. https://doi.org/10.1109/WSC.2014.7020006
CHWIF, L.; MEDINA, A.C. Modelagem e simulação de eventos discretos, teoria & aplicações. 3. ed. São Paulo: Ed do autor, 2010.DIGIESI, S.; KOCK, A.A.A.; MUMMOLO, G.; ROODA, J. E. The Effect of Dynamic Worker Behavior on Flowtime Performance. International Journal of Production Economics, v.120, n.2, p. 368-377, 2009. https://doi.org/10.1016/j.ijpe.2008.12.012
HILLIER, F.S.; LIEBERMAN, G.J. Introduction to operations research. McGraw-Hill, 2001.KLEIJNEN, J. P. C. Verification and validation of simulations models. European Journal of Operational Research, v.82, n.1, 1995. https://doi.org/10.1016/0377-2217(94)00016-6
LEAL, F.; SILVA, R.F.; MONTEVECHI, J.A.B.; ALMEIDA, D.A.; MARINS, F.A.S. A practical guide for operational validation of discrete simulation models. Sociedade Brasileira de Pesquisa Operacional, SBPO, 2011.https://doi.org/10.1590/S0101-74382011000100005
MONTEVECHI, J.A.B.; LEAL, F.; PINHO, A.F.; COSTA, R.F.S.; OLIVEIRA, M.L.M.; SILVA, A.L.F. Conceptual modeling in simulation projects by mean adapted IDEF: anapplication in a Brazilian tech company. Proceedings of Winter Simulation Conference. Baltimore, USA, 2010.https://doi.org/10.1109/WSC.2010.5678908
NELSON, B.L.; SONG, E.; PEGDEN, C.D. Advanced tutorial:input uncertainty quantification. Proceedings of the Winter Simulation Conference, Savannah, USA, 2014. https://doi.org/10.1109/WSC.2014.7019886
RAUNAK, M.; OLSEN, M. Quantifying validation of discrete event simulation models. Proceedings of the Winter Simulation Conference, Savannah, USA, 2014.https://doi.org/10.1109/WSC.2014.7019927
REILLY, T.; ATKINSON, G.; WATERHOUSE, J. Chronobiology and Physical Performance, in: Garret WE. Kirkendall DT (ed). Exercise and sport science. 1ed. Philadelphia: Willians &Wilkins, 2000. p.351-372.
SARGENT, R. G. Verification and validation of simulation models are discussed in this paper. Journal of Simulation, v. 7, n.1, p. 12–24, 2013.https://doi.org/10.1057/jos.2012.20S
ARGENT, R.G. Validation and verification of simulation models. Proceedings of the Winter Simulations Conference, Washington, DC, USA, 2004.
SILVA, C.E.S.; SALGADO, E.G.; MELLO, C.H.P.; OLIVEIRA, E.S.; LEAL, F. Integration of computer simulation in design for manufacturing and assembly. International Journal of Production Research, 2014, v. 52, n. 10, p. 2851–2866. https://doi.org/10.1080/00207543.2013.853887
SLACK, N; CHAMBERS, S; JOHNSTON, R. Administração da Produção. 2.ed. São Paulo: Atlas, 2002.STANSFIELD, T.; MASSEY, R.; JAMISON, D. Simulation can improve reality: get more from the future. Industrial Engineer, v. 46, n. 3, p. 38-42, 2014.
TURRIONI, J. B.; MELLO, C. H. P. Metodologia de pesquisa em engenharia de produção: estratégias, métodos e técnicas para condução de pesquisas quantitativas e qualitativas. 2012. Programa de Pós-graduação em Engenharia de Produção, Universidade Federal de Itajubá, Itajubá, 2012.
WANG, Z. Selecting verification and validation techniques for simulation projects: a planning and tailoring strategy. Proceedings of the Winter Simulation Conference, Washington, DC, USA, 2013.https://doi.org/10.1109/WSC.2013.6721511
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