Using infinite server queues with partial information for occupancy prediction

References

• Aktekin, T., & Soyer, R. (2011). Call center arrival modeling: A Bayesian state space approach. Naval Research Logistics (NRL), 58(1), 28–42. https://doi.org/10.1002/nav.20436
   Web of Science ®Google Scholar
• Bekker, R., & de Bruin, A. (2010). Time-dependent analysis for refused admissions in clinical wards. Annals of Operations Research, 178(1), 45–65. https://doi.org/10.1007/s10479-009-0570-z
   Web of Science ®Google Scholar
• Blanghaps, N., Nov, Y., & Weiss, G. (2013). Sojourn time estimation in an M/G/∞ queue with partial information. Journal of Applied Probability, 50(4), 1044–1056.
   Web of Science ®Google Scholar
• Bravo, F., Rudin, C., Rudin, C., Shaposhnik, Y., & Yuan, Y. (2019). Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs. https://doi.org/10.2139/ssrn.3384148
   Google Scholar
• Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), 1–32. https://doi.org/10.18637/jss.v076.i01
   PubMed Web of Science ®Google Scholar
• Cheung, E., Rabehasaina, L., Woo, J., & Xu, R. (2019). Asymptotic correlation structure of discounted Incurred But Not Reported claims under fractional Poisson arrival process. European Journal of Operational Research, 276(2), 582–601. https://doi.org/10.1016/j.ejor.2019.01.033
   Web of Science ®Google Scholar
• Crowhurst, E., Harwich, E. (2016). Unlocking Prison Performance. https://reform.uk/sites/default/files/2018-10/Unlocking\%20prison\%20performance\%20web.pdf.
   Google Scholar
• Demir, E., & Southern, D. (2017). Enabling better management of patients: Discrete event simulation combined with the star approach. Journal of the Operational Research Society, 68(5), 577–590. https://doi.org/10.1057/s41274-016-0029-y
   Web of Science ®Google Scholar
• Dong, J., & Perry, O. (2020). Queueing Models for Patient-Flow Dynamics in Inpatient Wards. Operations Research, 68(1), 250–275. https://doi.org/10.1287/opre.2019.1845
   Web of Science ®Google Scholar
• Duffield, N., & Whitt, W. (1997). Control and recovery from rare congestion events in a large multi-server system. Queueing Systems, 26(1/2), 69–104. https://doi.org/10.1023/A:1019168821588
   Web of Science ®Google Scholar
• Eick, S., Massey, W., & Whitt, W. (1993). The physics of the Mt/G/∞ queue. Operations Research, 41(4), 731–742.
   Web of Science ®Google Scholar
• Gans, N., Koole, G., & Mandelbaum, A. (2003). Telephone Call Centers: Tutorial, Review, and Research Prospects. Manufacturing & Service Operations Management, 5(2), 79–141. https://doi.org/10.1287/msom.5.2.79.16071
   Google Scholar
• Goldberg, D., & Whitt, W. (2008). The last departure time from an Mt/G/∞ queue with a terminating arrival process. Queueing Systems, 58(2), 77–104.
   Web of Science ®Google Scholar
• Goldenshluger, A., & Koops, D. (2019). Nonparametric Estimation of Service Time Characteristics in Infinite-Server Queues with Nonstationary Poisson Input. Stochastic Systems, 9(3), 183–207. https://doi.org/10.1287/stsy.2018.0026
   Google Scholar
• Grieco, L., Utley, M., & Crowe, S. (2021). Operational research applied to decisions in home health care: A systematic literature review. Journal of the Operational Research Society, 72(9), 1960–1991. https://doi.org/10.1080/01605682.2020.1750311
   Web of Science ®Google Scholar
• Hall, P., & Park, J. (2004). Nonparametric Inference about Service Time Distribution from Indirect Measurements. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), 861–875. https://doi.org/10.1111/j.1467-9868.2004.B5725.x
   Google Scholar
• Huang, C. Y., & Huang, W. C. (2008). Software reliability analysis and measurement using finite and infinite server queueing models. IEEE Transactions on Reliability, 57(1), 192–203.
   Web of Science ®Google Scholar
• Hulshof, P. J., Kortbeek, N., Boucherie, R. J., Hans, E. W., & Bakker, P. J. (2012). Taxonomic classification of planning decisions in health care: A structured review of the state of the art in or/ms. Health Systems, 1(2), 129–175. https://doi.org/10.1057/hs.2012.18
   Google Scholar
• Ibrahim, R., Ye, H., L’Ecuyer, P., & Shen, H. (2016). Modeling and forecasting call center arrivals: A literature survey and a case study. International Journal of Forecasting, 32(3), 865–874. https://doi.org/10.1016/j.ijforecast.2015.11.012
   Web of Science ®Google Scholar
• Jongbloed, G., & Koole, G. (2001). Managing uncertainty in call centres using poisson mixtures. Applied Stochastic Models in Business and Industry, 17(4), 307–318. https://doi.org/10.1002/asmb.444
   Web of Science ®Google Scholar
• Kegel, L., Hahmann, M., & Lehner, W. (2017). Generating what-if scenarios for time series data [Paper presentation].Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM ’17, In, New York, NY, USA. Association for Computing Machinery. https://doi.org/10.1145/3085504.3085507
   Google Scholar
• Korhan Aras, A., Liu, Y., & Whitt, W. (2017). Heavy-traffic limit for the initial content process. Stochastic Systems, 7(1), 95–142. https://doi.org/10.1287/15-SSY175
   Google Scholar
• Li, D., Hu, Q., Wang, L., & Yu, D. (2019). Statistical inference for Mt/G/∞ queueing systems under incomplete observations. European Journal of Operational Research, 279(3), 882–901.
   Web of Science ®Google Scholar
• Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021). Forecast methods for time series data: A survey. IEEE Access. 9, 91896–91912. https://doi.org/10.1109/ACCESS.2021.3091162
   Web of Science ®Google Scholar
• Mandelbaum, A., Momčilović, P., Trichakis, N., Kadish, S., Leib, R., & Bunnell, C. (2019). Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center. Management Science, 1909, 1–28.
   Google Scholar
• Mandjes, M., & Uraniewski, P. (2011). M/G/∞ transience, and its applications to overload detection. Performance Evaluation, 68(6), 507–527.
   Web of Science ®Google Scholar
• Massey, W., & Whitt, W. (1993). Networks of infinite-server queues with nonstationary Poisson input. Queueing Systems, 13(1-3), 183–250. https://doi.org/10.1007/BF01158933
   Google Scholar
• Master, N., Reiman, M., Wang, C., & Wein, L. (2018). A Continuous-Class Queueing Model With Proportional Hazards-Based Routing. SSRN Electronic Journal, 1–44. https://doi.org/10.2139/ssrn.3390476
   Google Scholar
• Ministry of Justice (2016). Story of the Prison Population: 1993 – 2016 England and Wales.
   Google Scholar
• Ministry of Justice (2018). Guide to Offender Management Statistics England and Wales Ministry of Justice Guidance Documentation, pages 1–45.
   Google Scholar
• Ministry of Justice (2019). Prison population projections 2019 to 2024. England and Wales.
   Google Scholar
• Ministry of Justice (2020a). (Accessed August 3, Criminal Justice System statistics quarterly: June 2019. https://www.gov.uk/government/statistics/criminal-justice-system-statistics-quarterly-june-2019.
   Google Scholar
• Ministry of Justice (2020b). (Accessed August 3Offender management statistics quarterly. https://www.gov.uk/government/collections/offender-management-statistics-quarterly.
   Google Scholar
• O’Hagan, A. (1998). Eliciting expert beliefs in substantial practical applications. Journal of the Royal Statistical Society Series D: The Statistician, 47(1), 21–35.
   Google Scholar
• Pagel, C., Banks, V., Pope, C., Whitmore, P., Brown, K., Goldman, A., & Utley, M. (2017). Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit. Operations Research for Health Care, 15, 19–31. https://doi.org/10.1016/j.orhc.2017.08.003
   Web of Science ®Google Scholar
• Pender, J. (2016). Risk measures and their application to staffing nonstationary service systems. European Journal of Operational Research, 254(1), 113–126. https://doi.org/10.1016/j.ejor.2016.03.012
   Web of Science ®Google Scholar
• Petris, G., Petrone, S., & Campagnoli, P. (2009). Model specification. In Dynamic Linear Models with R., pages 85–142 Springer.
   Google Scholar
• Picklands, J. (1997). Estimation for an M/G/∞ queue with incomplete information. Biometrika, 84(2), 295–308.
   Web of Science ®Google Scholar
• Schwarz, J., Selinka, G., & Stolletz, R. (2016). Performance analysis of time-dependent queueing systems: Survey and classification. Omega, 63, 170–189. https://doi.org/10.1016/j.omega.2015.10.013
   Web of Science ®Google Scholar
• Shumway, R. H., Stoffer, D. S., & Stoffer, D. S. (2000). Time series analysis and its applications., volume 3 Springer.
   Google Scholar
• Spencer, J., Sudan, M., & Xu, K. (2014). Queueing with future information. ACM SIGMETRICS Performance Evaluation Review, 41(3), 40–42. https://doi.org/10.1145/2567529.2567542
   Google Scholar
• Spinner, S., Casale, G., Brosig, F., & Kounev, S. (2015). Evaluating approaches to resource demand estimation. Performance Evaluation, 92, 51–71. https://doi.org/10.1016/j.peva.2015.07.005
   Web of Science ®Google Scholar
• Treasury, H. (2015). The aqua book: Guidance on producing quality analysis for government. https://www.gov.uk/government/publications/the-aqua-book-guidance-on-producing-quality-analysis-for-government.
   Google Scholar
• Tuominen, J., Lomio, F., Oksala, N., Palomäki, A., Peltonen, J., Huttunen, H., & Roine, A. (2022). Forecasting daily emergency department arrivals using high-dimensional multivariate data: A feature selection approach. BMC Medical Informatics and Decision Making, 22(1), 1–12. https://doi.org/10.1186/s12911-022-01878-7
   PubMed Web of Science ®Google Scholar
• Usta, M., & Wein, L. (2015). Assessing risk-based policies for pretrial release and split sentencing in Los Angeles County Jails. Plos One, 10(12), e0144967–16. https://doi.org/10.1371/journal.pone.0144967
   PubMed Web of Science ®Google Scholar
• Utley, M., Jit, M., & Gallivan, S. (2008). Restructuring routine elective services to reduce overall capacity requirements within a local health economy. Health Care Management Science, 11(3), 240–247. https://doi.org/10.1007/s10729-007-9052-5
   PubMed Web of Science ®Google Scholar
• Walton, N., & Xu, K. (2021). Learning and information in stochastic networks and queues. Tutorials in Operations Research: Emerging Optimization Methods and Modeling Techniques with Applications, pages 161–198.
   Google Scholar
• Weber, T. (2005). Conditional dynamics of non-markovian, infinite-server queues. Thesis, Massachusetts Institute of Technology
   Google Scholar
• Whitt, W. (1999). Predicting queueing delays. Management Science, 45(6), 870–888. https://doi.org/10.1287/mnsc.45.6.870
   Web of Science ®Google Scholar
• Whitt, W. (2002). Stochastic models for the design and management of customer contact centers: Some research directions. Technical report. Columbia University.
   Google Scholar
• Worthington, D., Utley, M., & Suen, D. (2020). Infinite-server queueing models of demand in healthcare: A review of applications and ideas for further work. Journal of the Operational Research Society, 71(8), 1145–1160. https://doi.org/10.1080/01605682.2019.1609878
   Web of Science ®Google Scholar
• Xie, W., Nelson, B., & Barton, R. (2014). A Bayesian framework for quantifying uncertainty in stochastic simulation. Operations Research, 62(6), 1439–1452. https://doi.org/10.1287/opre.2014.1316
   Web of Science ®Google Scholar