Abstract
The purpose of this paper is to present an applicable approach for simultaneous optimization of response variables in a warehouse process. Through simulation and the Response Surface Methodology, supported by information and communication technologies. The methodology applied to this problem considers the design of a simulation model, defined by discrete events to represent real-life activities in a warehouse process; this will allow to simulate different alternatives, in order to collect the results of the response variables. In addition, the Response Surface Methodology is applied to analyze the effects of the factors, and to define an empirical model able to appropriately describe the behavior of a multivariate system. A prediction model was established empirically through a case study; this scenario showed that the simultaneous optimization of the response variables is plausible.
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References
Kumar, V., Mishra, N., Chan, F.T., Verma, A.: Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach, vol. 49, no. 21 (2011)
Ramaa, A., Subramanya, K.N., Rangaswamy, T.M.: Impact of warehouse management system in a supply chain. Int. J. Comput. Appl. 54(1), 14–20 (2012)
Kumar, M., Veeramachaneni, R., Kare, S.: Warehousing in theory and practice: a case study at ÖoB, Clas Ohlson, Stadium, Åhlens, vol. Master thesis. University of Borås, Borå, Suecia (2008)
Hompel, M.T., Schmidt, T.: Warehouse Management Automation and Organisation of Warehouse and Order Picking Systems. Springer, New York (2007)
Hossein, M.: Optimization in simulation: current issues and the future outlook. Nav. Res. Logistics 37(6), 807–825 (1990)
Straube, F., Ma, S., Bohn, M.: Internationalisation of Logystics Systems. Springer, Berlin (2008)
Kelton, W.D., Sadowski, R.P., Zupick, N.B.: Simulation with Arena. McGrawHill, USA (2014)
Frazelle, E.: World-Class Warehousing and Material Handling. McGraw-Hill, New York (2002)
Hoover, R.F., Perry, S.V.: Simulation: A Problem Solving Approach. Prentice Hall, Estados Unidos (1989)
Wang, C., Guan, Z., Shao, X., Ullah, S.: Simulation-based of logistics distribution systems for an assembly line with path constraints. Int. J. Prod. Res. 53(12), 3538–3551 (2014)
Kaban, A.K., Othman, Z., Rohmah, D.S.: Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int. J. Simul. Modell. 11(3), 129–140 (2012)
Giraldo, J.A., Sarache, W.A., Castrillón, O.D.: Metodología integral soportada en simulación para el mejoramiento de sistemas de producción Job Shop. Aplicaciones en pymes metalmecánicas. Ingenieria e Investigación 30(1), 97–106, April 2010
Carson, Y., Maria, A.: Simulation optimization: methods and applications. In: Proceedings of the 29th Conference on Winter simulation, pp. 118–126. IEEE Computer Society (1997)
Montgomery, D.: Analisis y Diseño de Experimentos. Limusa, Mexico (2010)
Khuri, A., Mukhopadhyay, S.: Advanced Review. Response Surface Methodology, vol. 2, no. 2, pp. 128–149. Wiley, Hoboken (2010)
Lind, E., Goldin, J., Hickman, J.: Fitting yield and cost response surfaces. Chem. Eng. Prog. 56, 62–68 (1960)
Harrington, E.: The desirabilty functions. Ind. Qual. Control 12, 494–498 (1965)
Wexler, L., Perez, A.M., Cubero-Castillo, E., Vaillant, F.: Use of response surface methodology to compare vacuum and atmospheric deep-fat frying of papaya chips impregnated with blackberry juice. J. Food 14(4), 578–586 (2016)
Rahimi, M., Falla, E., Maghsoud, A.: Optimization using simulation and response surface methodology with an application on subway train scheduling. Int. Trans. Oper. Res. 23(4), 797–811 (2016)
Aldemir, A., Hapoglu, H.: Optimization of Generalizad Predictive Control (GPC) tuning parameters by Response Surface Methodology (RSM). Int. J. Control Autom. 8(2), 393–408 (2015)
Ramanujam, R., Raju, R., Muthurkrishnan, N.: Taguchi multi-machining characteristics optimization in turning of A1-15%SiCp composites using desirabilty function analysis. J. Stud. Manuf. 1(2–3), 120–125 (2010)
Gutierrez, H., De la Vara, R.: Analisis y diseño de experimentos. McGraw Hill, México (2008)
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González, J., Híjar, H., Sánchez-Leal, J., Hernández, D.E. (2018). Simulation and Surface Response Methodology for Simultaneous Optimization of Response Variables: Case Study in a Warehousing Process. In: Kantola, J., Barath, T., Nazir, S. (eds) Advances in Human Factors, Business Management and Leadership. AHFE 2017. Advances in Intelligent Systems and Computing, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-319-60372-8_41
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DOI: https://doi.org/10.1007/978-3-319-60372-8_41
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