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dc.contributor.authorRomero-Silva, Rodrigo
dc.contributor.authorHurtado Hernández, Margarita
dc.contributor.otherCampus Ciudad de Méxicoes
dc.coverage.spatialMéxico
dc.creatorRODRIGO ROMERO SILVA;468237
dc.creatorMARGARITA MARIA ELVIRA HURTADO HERNANDEZ;348257
dc.date.accessioned2020-09-30T21:19:22Z
dc.date.available2020-09-30T21:19:22Z
dc.date.issued2020-03-10
dc.identifier.citationRomero-Silva, R., Shaaban, S., Marsillac, E. y Hurtado-Hernandez, M. (2020). Studying the effects of the skewness of inter-arrival and service times on the probability distribution of waiting times. Pesquisa Operacional, (40), 1-29. DOI: http://dx.doi.org/10.1590/0101-7438.2020.040.00223190en_US
dc.identifier.issn0101-7438
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5325
dc.identifier.urihttp://dx.doi.org/10.1590/0101-7438.2020.040.00223190
dc.description.abstractPrevious studies have shown that the mean queue length of a GI/G/1 system is significantly influenced by the skewness of inter-arrival times, but not by the skewness of service times. These results are limited because all the distributions considered in previous studies were positively skewed. To address this limitation, this paper investigates the effects of the skewness of inter-arrival and service times on the probability distribution of waiting times, when a negatively skewed distribution is used to model inter-arrival and service times. Subsequent to a series of experiments on a GI/G/1 queue using discrete-event simulation, results have shown that the lowest mean waiting time and the lowest variance of waiting times can be attained with a combination of positive inter-arrival skewness and negative service skewness. Results also show an interesting effect of the skewness of service times in the probability of no-delay in environments with a higher utilization factor. © 2020 Brazilian Operations Research Society.en_US
dc.language.isoeng
dc.publisherSociedade Brasileira de Pesquisa Operacional
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofREPOSITORIO NACIONAL CONACYTes
dc.relation.ispartofOPENAIREes
dc.relation.ispartofseries;40
dc.rightsAcceso Abiertoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0*
dc.rights.urihttps://www.scielo.br/revistas/pope/iaboutj.htm
dc.rights.urihttps://portal.issn.org/resource/ISSN/1678-5142#
dc.sourcePesquisa Operacional
dc.subjectGI/G/1 queueen
dc.subjectQueueing theoryen
dc.subjectSkewnessen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.titleStudying the effects of the skewness of inter-arrival and service times on the probability distribution of waiting timesen
dc.typeArtículoes
dcterms.audienceInvestigadoreses
dcterms.audienceEstudianteses
dcterms.audienceMaestroses
dcterms.bibliographicCitationAl-Hanbali A, Mandjes M, Nazarathy Y & Whitt W. 2011. The Asymptotic Variance of Departures in Critically Loaded Queues. Adv. Appl. Probab. 43: 243–263. https://doi.org/http://www.jstor.org/stable/23024534en
dcterms.bibliographicCitationBhat V N. 1993. Approximation for the variance of the waiting time in a GI/G/1 queue. Microelectron. Reliab. 33: 1997–2002. https://doi.org/http://dx.doi.org/10.1016/0026-2714(93)90356-4en
dcterms.bibliographicCitationBolch G, Greiner S, De Meer H & Trivedi K S. 1998. Queueing Networks and Markov Chains, 1st ed. John Wiley & Sons, Inc., New York, NY.en
dcterms.bibliographicCitationBrandwajn A & Begin T. 2009. A Note on the Effects of Service Time Distribution in the M/G/1 Queue, in: Kaeli D, Sachs K. (Eds.), Computer Performance Evaluation and Benchmarking. Springer Berlin Heidelberg, pp. 138–144. https://doi.org/10.1007/978-3-540-93799-9 9en
dcterms.bibliographicCitationBuzacott J & Shanthikumar J G. 1993. Stochastic Models of Manufacturing Systems, 1st ed. Prentice-Hall, Englewood Cliffs, New Jersey.en
dcterms.bibliographicCitationChen N & Zhou S. 2010. Simulation-based estimation of cycle time using quantile regression. IIE Trans. 43: 176–191. https://doi.org/10.1080/0740817X.2010.521806en
dcterms.bibliographicCitationDe Treville S, Shapiro R D & HamerI A.-P. 2004. From supply chain to demand chain: the role of lead time reduction in improving demand chain performance. J. Oper. Manag. 21: 613–627. https://doi.org/http://dx.doi.org/10.1016/j.jom.2003.10.001en
dcterms.bibliographicCitationDoerr K H, Freed T, Mitchell T R, Schriesheim C A & Zhou X (TRACY). 2004. Work Flow Policy and Within-Worker and Between-Workers Variability in Performance. J. Appl. Psychol. 89: 911–921. https://doi.org/http://dx.doi.org/10.1037/0021-9010.89.5.911en
dcterms.bibliographicCitationDudley N A. 1963. Work-Time Distributions. Int. J. Prod. Res. 2: 137–144. https://doi.org/10.1080/00207546308947819en
dcterms.bibliographicCitationDuncan D B. 1955. Multiple Range and Multiple F Tests. Biometrics 11: 1–42. https://doi.org/10.2307/3001478en
dcterms.bibliographicCitationGross D. 1999. Sensitivity of output performance measures to input distribution shape in modeling queues.3. Real data scenario. In: Simulation Conference Proceedings, 1999 Winter. pp. 452--457 vol.1. https://doi.org/10.1109/WSC.1999.823108en
dcterms.bibliographicCitationGross D & Juttijudata M. 1997. Sensitivity of output performance measures to input distributions in queueing simulation modeling.en
dcterms.bibliographicCitationGue K R & Kim H H. 2012. Predicting departure times in multi-stage queueing systems. Comput. Oper. Res. 39: 1734–1744. https://doi.org/http://dx.doi.org.ezproxy.depaul.edu/10.1016/j.cor.2011.10.011en
dcterms.bibliographicCitationHopp W & Spearman M. 2000. Factory Physics, Second. ed. McGraw-Hill.en
dcterms.bibliographicCitationInman R R. 1999. Empirical Evaluation of Exponential and Independence Assumptions in Queueing Models of Manufacturing Systems. Prod. Oper. Manag. 8: 409–432. https://doi.org/10.1111/j.1937-5956.1999.tb00316.xen
dcterms.bibliographicCitationJohnson M A. 1993. An empirical study of queueing approximations based on phase-type distributions. Commun. Stat. Model. 9: 531–561. https://doi.org/10.1080/15326349308807280en
dcterms.bibliographicCitationJohnson M A & Taaffe M R. 1991. A graphical investigation of error bounds for moment-based queueing approximations. Queueing Syst. 8: 295–312. https://doi.org/10.1007/BF02412257en
dcterms.bibliographicCitationKelton W D, Smith J S & Sturrock D T. 2014. Simio and Simulation: Modeling, Analysis, Applications, 1st ed. Simio LLC, Sewickley, PA.en
dcterms.bibliographicCitationKhalil R A, Stockton D J & Fresco J A. 2008. Predicting the effects of common levels of variability on flow processing systems. Int. J. Comput. Integr. Manuf. 21: 325–336. https://doi.org/http://dx.doi.org/10.1080/09511920701233472en
dcterms.bibliographicCitationKlincewicz J G & WHITT W. 1984. On approximations for queues, II: Shape constraints. AT&T Bell Lab. Tech. J. 63: 139–161. https://doi.org/10.1002/j.1538-7305.1984.tb00006.xen
dcterms.bibliographicCitationLagershausen S & Tan B. 2015. On the Exact Inter-departure, Inter-start, and Cycle Time Distribution of Closed Queueing Networks Subject to Blocking. IIE Trans. 47:673–692. https://doi.org/10.1080/0740817X.2014.982841en
dcterms.bibliographicCitationLatouche G & Ramaswami V. 1999a. PH Distributions. In: Latouche G, Ramaswami V. (Eds.), Introduction to Matrix Analytic Methods in Stochastic Modeling. Society for Industrial and Applied Mathematics, Philadelphia, PA, pp. 33–60. https://doi.org/http://dx.doi.org/10.1137/1.9780898719734en
dcterms.bibliographicCitationLatouche G & Ramaswami V. 1999b. Introduction to Matrix Analytic Methods in Stochastic Modeling, 1st ed. Society for Industrial and Applied Mathematics, Philadelphia, PA. https://doi.org/http://dx.doi.org/10.1137/1.9780898719734en
dcterms.bibliographicCitationLau H.-S & Martin G E. 1987. The effects of skewness and kurtosis of processing times in unpaced lines. Int. J. Prod. Res. 25: 1483–1492. https://doi.org/10.1080/00207548708919927en
dcterms.bibliographicCitationLaw A. 2014. Simulation Modeling and Analysis, 5th ed. McGraw-Hill, New York.en
dcterms.bibliographicCitationLemoine A J. 1976. On Random Walks and Stable GI/G/1 Queues. Math. Oper. Res. 1:159–164.en
dcterms.bibliographicCitationMyskja A. 1990. On approximations for the GI/GI/1 queue. Comput. Networks ISDN Syst. 20: 285–295. https://doi.org/http://dx.doi.org/10.1016/0169-7552(90)90037-Sen
dcterms.bibliographicCitationPowell S G & Pyke D F. 1994. An empirical investigation of the two-moment approximation for production lines. Int. J. Prod. Res. 32: 1137–1157. https://doi.org/10.1080/00207549408956992en
dcterms.bibliographicCitationRomero-Silva R, Hurtado M & Santos J. 2016. Is the scheduling task contextdependent? A survey investigating the presence of constraints in different manufacturing contexts. Prod. Plan. Control 27: 753–760. https://doi.org/10.1080/09537287.2016.1166274en
dcterms.bibliographicCitationRomero-Silva R, Marsillac E, Shaaban S & Hurtado-Hernández M. 2019. Reducing the variability of inter-departure times of a single-server queueing system – The effects of skewness. Comput. Ind. Eng. 135: 500–517. https://doi.org/10.1016/j.cie.2019.06.030en
dcterms.bibliographicCitationSahin I & Perrakis S. 1976. Moment Inequalities For A Class Of Single Server Queues. INFOR Inf. Syst. Oper. Res. 14: 144–152. https://doi.org/10.1080/03155986.1976.11731634en
dcterms.bibliographicCitationSlack N. 1982. Work time distributions in production system modelling. https://doi.org/10.1002/9781118785317.weom100178en
dcterms.bibliographicCitationTarasov V N. 2016. Analysis of queues with hyperexponential arrival distributions. Probl. Inf. Transm. 52: 14–23. https://doi.org/10.1134/S0032946016010038en
dcterms.bibliographicCitationTersine R J & Hummingbird E A. 1995. Lead-time reduction: the search for competitive advantage. Int. J. Oper. Prod. Manag. 15:, 8–18. https://doi.org/10.1108/01443579510080382en
dcterms.bibliographicCitationThe R Foundation. 2019. The R Project for Statistical Computing [WWW Document]. URL https://www.r-project.org/foundation/ (accessed 1/1/20).en
dcterms.bibliographicCitationWelch P D. 1983. The Statistical Analysis of Simulation Results. In: Lavenberg S S. (Ed.), The Computer Performance Modeling Handbook. Academic Press, New York, pp. 268–328.en
dcterms.bibliographicCitationWhitt W. 1989. An Interpolation Approximation for the Mean Workload in a GI/G/1 Queue. Oper. Res. 37: 936–952. https://doi.org/10.1287/opre.37.6.936en
dcterms.bibliographicCitationWhitt W. 1984a. On approximations for queues, III: Mixtures of exponential distributions. AT&T Bell Lab. Tech. J. 63: 163–175. https://doi.org/10.1002/j.1538-7305.1984.tb00007.xen
dcterms.bibliographicCitationWhitt W. 1984b. Minimizing Delays in the GI/G/1 Queue. Oper. Res. 32: 41–51. https://doi.org/10.1287/opre.32.1.41en
dcterms.bibliographicCitationWolff R W. 1970. Work-Conserving Priorities. J. Appl. Probab. 7: 327–337. https://doi.org/10.2307/3211968en
dcterms.bibliographicCitationWu K, Srivathsan S & Shen Y. 2018. Three-moment approximation for the mean queue time of a GI/G/1 queue. IISE Trans. 50: 63–73. https://doi.org/10.1080/24725854.2017.1357216en
dcterms.bibliographicCitationWu K, Zhou Y & Zhao N. 2016. Variability and the fundamental properties of production lines. Comput. Ind. Eng. 99: 364–371. https://doi.org/https://doi.org/10.1016/j.cie.2016.04.014en
dcterms.bibliographicCitationYang F, Ankenman B E & Nelson B L. 2008. Estimating Cycle Time Percentile Curves for Manufacturing Systems via Simulation. INFORMS J. Comput. 20: 628–643. https://doi.org/10.1287/ijoc.1080.0272en
dc.description.versionVersión del editores
dc.identifier.doi10.1590/0101-7438.2020.040.00223190
dc.identifier.pagenumber1-29


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