Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times

References

• Alkaff, A., M. N. Qomarudin, and S. E. Wiratno. 2020. “Matrix-Analytic Solutions in Production Lines Without Buffers.” Computers & Operations Research 119: 104903.
   Web of Science ®Google Scholar
• Angelopoulos, A., E. T. Michailidis, N. Nomikos, P. Trakadas, A. Hatziefremidis, S. Voliotis, and T. Zahariadis. 2020. “Tackling Faults in The Industry 4.0 Era – A Survey of Machine-Learning Solutions and Key Aspects.” Sensors 20 (1): 109.
   Web of Science ®Google Scholar
• Arinez, J. F., Q. Chang, R. X. Gao, C. Xu, and J. Zhang. 2020. “Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook.” Journal of Manufacturing Science and Engineering142 (11): 110804.
   Web of Science ®Google Scholar
• Boulas, K., G. Dounias, and C. Papadopoulos. 2017. “Approximating Throughput of Small Production Lines Using Genetic Programming.” In Operational Research in Business and Economics, 185–204. Springer.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
   Web of Science ®Google Scholar
• Buzacott, J. A., and J. G. Shanthikumar. 1993. Stochastic Models of Manufacturing Systems. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Cadavid, J. P. U., S. Lamouri, B. Grabot, R. Pellerin, and A. Fortin. 2020. “Machine Learning Applied in Production Planning and Control: A State-of-the-Art in the Era of Industry 4.0.” Journal of Intelligent Manufacturing 31: 1531–1558.
   Web of Science ®Google Scholar
• Can, B., and C. Heavey. 2012. “A Comparison of Genetic Programming and Artificial Neural Networks in Metamodeling of Discrete-Event Simulation Models.” Computers & Operations Research 39 (2): 424–436.
   Web of Science ®Google Scholar
• Chen, J., Z. Jia, L. Huang, and Y. Dai. 2020. “Transient Performance Evaluation of Flexible Production Lines with Two Bernoulli Machines and Dedicated Buffers.” In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 836–841. IEEE.
   Google Scholar
• Dallery, Y., and S. B. Gershwin. 1992. “Manufacturing Flow Line Systems: A Review of Models and Analytical Results.” Queueing Systems 12 (1-2): 3–94.
   Google Scholar
• Diamantidis, A., J.-H. Lee, C. T. Papadopoulos, J. Li, and C. Heavey. 2020. “Performance Evaluation of Flow Lines with Non-Identical and Unreliable Parallel Machines and Finite Buffers.” International Journal of Production Research 58 (13): 3881–3904.
   Web of Science ®Google Scholar
• Fan, R.-N., F.-Q. Ma, and Q.-L. Li. 2020. “Optimization Strategies for Dockless Bike Sharing Systems via Two Algorithms of Closed Queuing Networks.” Processes 8 (3): 345.
   Web of Science ®Google Scholar
• Fowler, J. W., and O. Rose. 2004. “Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems.” Simulation 80 (9): 469–476.
   Google Scholar
• Ghosh, S., and A. D. Banik. 2018. “Computing Conditional Sojourn Time of A Randomly Chosen Tagged Customer in a BMAP/MSP/1 Queue Under Random Order Service Discipline.” Annals of Operations Research 261 (1-2): 185–206.
   Web of Science ®Google Scholar
• Govil, M. K., and M. C. Fu. 1999. “Queueing Theory in Manufacturing: A Survey.” Journal of Manufacturing Systems 18 (3): 214–240.
   Web of Science ®Google Scholar
• Grosof, I., Z. Scully, and M. Harchol-Balter. 2018. “SRPT for Multiserver Systems.” Performance Evaluation 127: 154–175.
   Web of Science ®Google Scholar
• Gurney, K. 1997. An Introduction to Neural Networks. London: CRC Press.
   Google Scholar
• Harrison, J. M., and V. Nguyen. 1990. “The QNET Method for Two-Moment Analysis of Open Queueing Networks.” Queueing Systems 6 (1): 1–32.
   Google Scholar
• Harrison, P. 1984. “A Note on Cycle Times in Tree-like Queueing Networks.” Advances in Applied Probability 16 (1): 216–219.
   Web of Science ®Google Scholar
• Hopp, W. J., and M. L. Spearman. 2011. Factory Physics. Boston: Waveland Press.
   Google Scholar
• Horváth, A., G. Horváth, and M. Telek. 2010. “A Joint Moments Based Analysis of Networks of MAP/MAP/1 Queues.” Performance Evaluation 67 (9): 759–778.
   Web of Science ®Google Scholar
• Jeong, K.-C., and Y.-D. Kim. 1999. “An Approximation Method for Performance Analysis of Assembly/Disassembly Systems with Parallel-Machine Stations.” IIE Transactions 31 (4): 391–394.
   Web of Science ®Google Scholar
• Jiang, L., and R. E. Giachetti. 2008. “A Queueing Network Model to Analyze the Impact of Parallelization of Care on Patient Cycle Time.” Health Care Management Science 11 (3): 248–261.
   PubMed Web of Science ®Google Scholar
• Kimura, T. 1994. “Approximations for Multi-Server Queues: System Interpolations.” Queueing Systems17 (3-4): 347–382.
   Web of Science ®Google Scholar
• Kuehn, P. 1979. “Approximate Analysis of General Queuing Networks by Decomposition.” IEEE Transactions on Communications 27 (1): 113–126.
   Web of Science ®Google Scholar
• Kumar, S., and P. Kumar. 2001. “Queueing Network Models in the Design and Analysis of Semiconductor Wafer Fabs.” IEEE Transactions on Robotics and Automation 17 (5): 548–561.
   Google Scholar
• Kuo, Y.-H., and A. Kusiak. 2019. “From Data to Big Data in Production Research: The Past and Future Trends.” International Journal of Production Research 57 (15-16): 4828–4853.
   Web of Science ®Google Scholar
• Liu, M., S. Fang, H. Dong, and C. Xu. 2021. “Review of Digital Twin About Concepts, Technologies, and Industrial Applications.” Journal of Manufacturing Systems 58: 346–361.
   Web of Science ®Google Scholar
• Manafzadeh Dizbin, N. 2020. “On Performance Evaluation and Optimal Control of Manufacturing Systems.” PhD diss., Koç University, Istanbul, Turkey.
   Google Scholar
• Manafzadeh Dizbin, N., and B. Tan. 2019. “Modelling and Analysis of The Impact of Correlated Inter-Event Data on Production Control Using Markovian Arrival Processes.” Flexible Services and Manufacturing Journal 31 (4): 1042–1076.
   Web of Science ®Google Scholar
• Mönch, L., J. W. Fowler, and S. J. Mason. 2012. Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems. Vol. 52. Heidelberg: Springer.
   Google Scholar
• Nitz, M., D. Smith, B. Wysocki, D. Knoell, and T. Wysocki. 2021. “Modeling of an Immune Response: Queuing Network Analysis of the Impact of Zinc and Cadmium on Macrophage Activation.” Biotechnology and Bioengineering 118 (1): 412–422.
   PubMed Web of Science ®Google Scholar
• Patchong, A., and D. Willaeys. 2001. “Modeling and Analysis of an Unreliable Flow Line Composed of Parallel-Machine Stages.” IIE Transactions 33 (7): 559–568.
   Web of Science ®Google Scholar
• Priore, P., D. De La Fuente, A. Gomez, and J. Puente. 2001. “A Review of Machine Learning in Dynamic Scheduling of Flexible Manufacturing Systems.” Artificial Intelligence for Engineering Design Analysis and Manufacturing 15 (3): 251–263.
   Web of Science ®Google Scholar
• Quinonero-Candela, J., and C. E. Rasmussen. 2005. “A Unifying View of Sparse Approximate Gaussian Process Regression.” Journal of Machine Learning Research 6: 1939–1959.
   Web of Science ®Google Scholar
• Rasmussen, C. E., and C. K. Williams. 2006. Gaussian Processes for Machine Learning. Cambridge: MIT Press.
   Google Scholar
• Sakasegawa, H. 1977. “An Approximation Formula Lq≃α.ρβ/(1−ρ).” Annals of the Institute of Statistical Mathematics29 (1): 67–75.
   Web of Science ®Google Scholar
• Schömig, A. K., and M. Mittler. 1995. “Autocorrelation of Cycle Times in Semiconductor Manufacturing Systems.” In Proceedings of the 27th Conference on Winter Simulation, 865–872. IEEE Computer Society.
   Google Scholar
• Schrage, L. E., and L. W. Miller. 1966. “The Queue M/G/1 with the Shortest Remaining Processing Time Discipline.” Operations Research 14 (4): 670–684.
   Web of Science ®Google Scholar
• Shanthikumar, J. G., S. Ding, and M. T. Zhang. 2007. “Queueing Theory for Semiconductor Manufacturing Systems: A Survey and Open Problems.” IEEE Transactions on Automation Science and Engineering 4 (4): 513–522.
   Web of Science ®Google Scholar
• Shin, J., D. Grosbard, J. R. Morrison, and A. Kalir. 2019. “Decomposition Without Aggregation for Performance Approximation in Queueing Network Models of Semiconductor Manufacturing.” International Journal of Production Research 57 (22): 7032–7045.
   Web of Science ®Google Scholar
• Shin, Y. W., and D. H. Moon. 2021. “A Unified Approach for an Approximation of Tandem Queues with Failures and Blocking Under Several Types of Service-Failure Interactions.” Computers & Operations Research 127: 105161.
   Web of Science ®Google Scholar
• Takagi, H. 1996. “A Note on the Response Time in M/G/1 Queues with Service in Random Order and Bernoulli Feedback.” Journal of the Operations Research Society of Japan 39 (4): 486–500.
   Google Scholar
• Tan, B., and S. Khayyati. 2021. “Supervised Learning-Based Approximation Method for Single-Server Open Queueing Networks with Correlated Interarrival and Service Times.” International Journal of Production Research. doi:10.1080/00207543.2021.1887536.
   PubMed Web of Science ®Google Scholar
• Tan, B., and S. Lagershausen. 2017. “On the Output Dynamics of Production Systems Subject to Blocking.” IIE Transactions 49 (3): 268–284.
   Web of Science ®Google Scholar
• Wang, M., H. Huang, and J. Li. 2021. “Transient Analysis of Multiproduct Bernoulli Serial Lines with Setups.” IEEE Transactions on Automation Science and Engineering 18 (1): 135–150.
   Web of Science ®Google Scholar
• Wu, J.-S., and W. Chan. 1989. “Maximum Entropy Analysis of Multiple-Server Queueing Systems.” Journal of the Operational Research Society 40 (9): 815–825.
   Web of Science ®Google Scholar
• Yang, X., and A. S. Alfa. 2009. “A Class of Multi-Server Queueing System with Server Failures.” Computers & Industrial Engineering 56 (1): 33–43.
   Web of Science ®Google Scholar
• Zhang, H.-Y., Q.-X. Chen, J. M. Smith, N. Mao, A.-L. Yu, and Z.-T. Li. 2017. “Performance Analysis of Open General Queuing Networks with Blocking and Feedback.” International Journal of Production Research 55 (19): 5760–5781.
   Web of Science ®Google Scholar
• Zhang, R., and M. Pavone. 2016. “Control of Robotic Mobility-on-Demand Systems: A Queueing-Theoretical Perspective.” International Journal of Robotics Research 35 (1-3): 186–203.
   Web of Science ®Google Scholar