Integrated buffer monitoring and control based on grey neural network

References

• Abdulshahed, A. M., Longstaff, A. P., Fletcher, S., & Potdar, A. (2016). Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model. Journal of Manufacturing Systems, 41, 130–142.10.1016/j.jmsy.2016.08.006
   Web of Science ®Google Scholar
• Alvisi, S., & Franchini, M. (2012). Grey neural networks for river stage forecasting with uncertainty. Physics and Chemistry of the Earth, 42–44(208), 108–118.10.1016/j.pce.2011.04.002
   Web of Science ®Google Scholar
• Bie, L., & Cui, N. (2010). Research on dynamic buffer monitoring in critical chain project management. Chinese Journal of Management Science, 18(6), 97–103.
   Google Scholar
• Cheng, C. S., Hsu, Y. T., & Wu, C. C. (1998). Grey neural network. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 81(11), 2433–2442.
   Google Scholar
• Choudhury, V., & Sabherwal, R. (2003). Portfolios of control in outsourced software development projects. Information Systems Research, 14(3), 291–314.10.1287/isre.14.3.291.16563
   Web of Science ®Google Scholar
• Cui, J., Liu, S. F., Zeng, B., & Xie, N. M. (2013). A novel grey forecasting model and its optimization. Applied Mathematical Modelling, 37(6), 4399–4406.10.1016/j.apm.2012.09.052
   Web of Science ®Google Scholar
• Diebold, F. X., & Lopez, J. A. (1996). 8 Forecast evaluation and combination. Handbook of Statistics, 14(96), 241–268.10.1016/S0169-7161(96)14010-4
   Google Scholar
• Elmaghraby, S. E. (2000). On criticality and sensitivity in activity networks. European Journal of Operational Research, 127(2), 220–238.10.1016/S0377-2217(99)00483-X
   Web of Science ®Google Scholar
• Ghaffari, M., & Emsley, M. W. (2015). Current status and future potential of the research on Critical Chain Project Management. Surveys in Operations Research and Management Science, 20(2), 43–54.10.1016/j.sorms.2015.10.001
   Google Scholar
• Goldratt, E. M. (1997). Critical chain: A business novel. Great Barrington, MA: North River Press.
   Google Scholar
• Goyal, V., Deolia, V. K., & Sharma, T. N. (2016). Neural network based sliding mode control for uncertain discrete-time nonlinear systems with time-varying delay. International Journal of Computational Intelligence Research, 12(2), 125–138.
   Google Scholar
• Gustafsson, J., & Salo, A. (2005). Contingent portfolio programming for the management of Risky projects. Operations Research, 53(6), 946–956.10.1287/opre.1050.0225
   Web of Science ®Google Scholar
• Gutierrez, G. J., & Kouvelis, P. (1991). Parkinson’s law and its implications for project management. Management Science, 37(8), 990–1001.10.1287/mnsc.37.8.990
   Web of Science ®Google Scholar
• Gutierrez, G., & Paul, A. (2000). Analysis of the effects of uncertainty, risk-pooling, and subcontracting mechanisms on project performance. Operations Research, 48(6), 927–938.10.1287/opre.48.6.927.12398
   Web of Science ®Google Scholar
• Hall, N. G., Long, D. Z., Qi, J., & Sim, M. (2015). Managing underperformance risk in project portfolio selection. Operations Research, 63(3), 660–675.10.1287/opre.2015.1382
   Web of Science ®Google Scholar
• Herroelen, W., & Leus, R. (2001). On the merits and pitfalls of critical chain scheduling. Journal of Operations Management, 19(5), 559–577.10.1016/S0272-6963(01)00054-7
   Web of Science ®Google Scholar
• Hu, X., Cui, N., Demeulemeester, E., & Bie, L. (2016). Incorporation of activity sensitivity measures into buffer management to manage project schedule risk. European Journal of Operational Research, 249(2), 717–727.10.1016/j.ejor.2015.08.066
   Web of Science ®Google Scholar
• Hu, Z. Y., Chen, Z. M., & Zhang, W. (2015). Regional industrial water demand prediction based on improved series grey neural network. In Proceedings of the 2nd International Conference on Modelling, Identification and Control (pp. 85–90). August 9-10, 2015, Paris, France: Atlantis Press.
   Google Scholar
• Huchzermeier, A., & Loch, C. H. (2001). Project management under risk: Using the real options approach to evaluate flexibility in R…D. Management Science, 47(1), 85–101.10.1287/mnsc.47.1.85.10661
   Web of Science ®Google Scholar
• Jin, W., Li, Z. J., Wei, L. S., & Zhen, H. (2000). The improvements of BP neural network learning algorithm. In Signal processing proceedings, 2000. WCCC-ICSP 2000. 5th international conference on (Vol. 3, pp. 1647–1649). IEEE.
   Google Scholar
• Ju-Long, D. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.10.1016/S0167-6911(82)80025-X
   Web of Science ®Google Scholar
• Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784–1789.10.1016/j.eswa.2009.07.064
   Web of Science ®Google Scholar
• Kuo, T. C., Chang, S. H., & Huang, S. N. (2009). Due-date performance improvement using TOC’s aggregated time buffer method at a wafer fabrication factory. Expert Systems with Applications, 36(2), 1783–1792.10.1016/j.eswa.2007.12.038
   Web of Science ®Google Scholar
• Leach, L. P. (2014). Critical chain project management. Boston, MA: Artech House.
   Google Scholar
• Lin, Y. H., Lee, P. C., & Chang, T. P. (2009). Adaptive and high-precision grey forecasting model. Expert Systems with Applications, 36(6), 9658–9662.10.1016/j.eswa.2008.12.009
   Web of Science ®Google Scholar
• Long, L. D., & Ohsato, A. (2008). Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International Journal of Project Management, 26(6), 688–698.10.1016/j.ijproman.2007.09.012
   Google Scholar
• McKay, K. N., & Morton, T. E. (1998). Review of: “Critical Chain” Eliyahu M. Goldratt The North River Press Publishing Corporation, Great Barrington, MA, 1997. ISBN 0-88427-153-6. IIE Transactions, 30(8), 759–762.10.1080/07408179808966521
   Google Scholar
• Pan, Y., & Shi, Y. (2016). Short-term traffic forecasting based on grey neural network with particle swarm optimization. In Proceedings of the world congress on engineering and computer science (Vol. 2).
   Google Scholar
• Pich, M. T., Loch, C. H., & Meyer, A. D. (2002). On uncertainty, ambiguity, and complexity in project management. Management Science, 48(8), 1008–1023.10.1287/mnsc.48.8.1008.163
   Web of Science ®Google Scholar
• Raz, T., Barnes, R., & Dvir, D. (2004). A critical look at critical chain project management. IEEE Engineering Management Review, 32(2), 35–44.10.1109/EMR.2004.25048
   Google Scholar
• Salari, M., & Khamooshi, H. (2016). A better project performance prediction model using fuzzy time series and data envelopment analysis. Journal of the Operational Research Society, 67(10), 1274–1287.10.1057/jors.2016.20
   Web of Science ®Google Scholar
• Sarkar, D. (1995). Methods to speed up error back-propagation learning algorithm. ACM Computing Surveys, 27(4), 519–542.10.1145/234782.234785
   Web of Science ®Google Scholar
• Shewhart, W. A., & Deming, W. E. (1939). Statistical method from the viewpoint of quality control. N. Chelmsford, MA: Courier Corporation.
   Google Scholar
• Steyn, H. (2002). Project management applications of the theory of constraints beyond critical chain scheduling. International Journal of Project Management, 20(1), 75–80.10.1016/S0263-7863(00)00054-5
   Google Scholar
• Tukel, O. I., Rom, W. O., & Eksioglu, S. D. (2006). An investigation of buffer sizing techniques in critical chain scheduling. European Journal of Operational Research, 172(2), 401–416.10.1016/j.ejor.2004.10.019
   Web of Science ®Google Scholar
• Vanhoucke, M. (2011). On the dynamic use of project performance and schedule risk information during project tracking. Omega, 39(4), 416–426.10.1016/j.omega.2010.09.006
   Web of Science ®Google Scholar
• Xie, N. M., & Liu, S. F. (2005). Discrete GM (1, 1) and mechanism of grey forecasting model. Systems Engineering-theory & Practice, 25(1), 93–99.
   Google Scholar
• Yang, S., & Fu, L. (2014). Critical chain and evidence reasoning applied to multi-project resource schedule in automobile R&D process. International Journal of Project Management, 32(1), 166–177.10.1016/j.ijproman.2013.01.010
   Web of Science ®Google Scholar
• Zhang, J., Shi, R., & Diaz, E. (2015). Dynamic monitoring and control of software project effort based on an effort buffer. Journal of the Operational Research Society, 66(9), 1555–1565.10.1057/jors.2014.125
   Web of Science ®Google Scholar
• Zhang, J., Song, X., & Díaz, E. (2016). Project buffer sizing of a critical chain based on comprehensive resource tightness. European Journal of Operational Research, 248(1), 174–182.10.1016/j.ejor.2015.07.009
   Web of Science ®Google Scholar
• Zhang, J., Song, X., Chen, H., & Shi, R. S. (2016). Determination of critical chain project buffer based on information flow interactions. Journal of the Operational Research Society, 67(9), 1146–1157.10.1057/jors.2016.9
   Web of Science ®Google Scholar
• Zhang, Y., Yang, J., & Jiang, H. (2012). Machine tool thermal error modeling and prediction by grey neural network. The International Journal of Advanced Manufacturing Technology, 59(9), 1065–1072.10.1007/s00170-011-3564-3
   Web of Science ®Google Scholar