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Determination of early warning time window for bottleneck resource buffer

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Abstract

As an advanced warning system, resource buffer can notify the use of resources and provide added protection to critical chain activities. However, most of the existing studies in the literature focus on locating actual time buffers at various locations. In this study, we locate bottleneck resource buffers along the critical chain and determines optimal time windows for the first time. Drawing on the theory of constraints, we take into account factors such as bottleneck resource sensitivity, the idle cost, the start time flexibility and the work flow on the critical chain, and develop a quantitative model for the optimal time window determination of resource buffer. The proposed method is tested and compared with the currently widely adopted buffer management method. Results of the computational experiment of the proposed method demonstrates its relative dominance over the predominant buffer management approach in terms of overall completion time, project cost, the probability of delay and the average buffer consumption ratio.

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References

  • Ashtiani, B., Jalali, G. R., Aryanezhad, M. B., & Makui, A. (2007, December). A new approach for buffer sizing in critical chain scheduling. In 2007 IEEE International conference on industrial engineering and engineering management (pp. 1037–1041). IEEE.

  • Bendavid, I., & Golany, B. (2011). Predetermined intervals for start times of activities in the stochastic project scheduling problem. Annals of Operations Research, 186(1), 429–442.

    Article  Google Scholar 

  • Bie, L., Cui, N., & Zhang, X. (2012). Buffer sizing approach with dependence assumption between activities in critical chain scheduling. International Journal of Production Research, 50(24), 7343–7356.

    Article  Google Scholar 

  • Caramia, M. (2020). Project management and scheduling. Annals of Operations Research, 285(1–2), 1–8.

    Article  Google Scholar 

  • Chu, C. (2008). Buffer sizing and critical chain project management. Computer integrated manufacturing Systems, 14(5), 1029–1035.

    Google Scholar 

  • Craft, R. C., & Leake, C. (2002). The Pareto principle in organizational decision making. Management Decision, 40(8), 729–733.

    Article  Google Scholar 

  • Creemers, S., Demeulemeester, E., & Van de Vonder, S. (2014). A new approach for quantitative risk analysis. Annals of Operations Research, 213(1), 27–65.

    Article  Google Scholar 

  • Ding, W., Hou, L., & Zhang, A. (2011). A bottleneck resource identification method for completing the workpiece based on the shortest delay time. In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (pp. 9–13). IEEE.

  • 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.

    Article  Google Scholar 

  • Goldratt, E. M. (1997). Critical chain. Great Barrington, MA: The North River Press.

    Google Scholar 

  • Goldratt, E. M., & Cox, J. (2016). The goal: A process of ongoing improvement. London: Routledge.

    Book  Google Scholar 

  • Gross, D. (2008). Fundamentals of queueing theory. New York: Wiley.

    Book  Google Scholar 

  • Guide, V. D. R., Jr., & Srivastava, R. (2000). A review of techniques for buffering against uncertainty with MRP systems. Production Planning & Control, 11(3), 223–233.

    Article  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.

    Article  Google Scholar 

  • Hameri, A. P., & Heikkilä, J. (2002). Improving efficiency: time-critical interfacing of project tasks. International Journal of Project Management, 20(2), 143–153.

    Article  Google Scholar 

  • Hartmann, S., & Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127(2), 394–407.

    Article  Google Scholar 

  • Hazır, Ö., Haouari, M., & Erel, E. (2010). Robust scheduling and robustness measures for the discrete time/cost trade-off problem. European Journal of Operational Research, 207(2), 633–643.

    Article  Google Scholar 

  • Herroelen, W., & Leus, R. (2001). On the merits and pitfalls of critical chain scheduling. Journal of Operations Management, 19(5), 559–577.

    Article  Google Scholar 

  • Herroelen, W., Leus, R., & Demeulemeester, E. (2002). Critical chain project scheduling: Do not oversimplify. Project Management Journal, 33(4), 48–60.

    Article  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.

    Article  Google Scholar 

  • Kulkarni, V. G., & Adlakha, V. G. (1986). Markov and Markov-regenerative PERT networks. Operations Research, 34(5), 769–781.

    Article  Google Scholar 

  • Leach, L. P. (1999). Critical chain project management improves project performance. Project Management Journal, 30, 39–51.

    Article  Google Scholar 

  • Leach, L. P. (2014). Critical chain project management. Boston, MA: Artech House.

    Google Scholar 

  • Lechler, T. G., Ronen, B., & Stohr, E. A. (2005). Critical chain: A new project management paradigm or old wine in new bottles? Engineering Management Journal, 17(4), 45–58.

    Article  Google Scholar 

  • Li, A., & Yin, L. (1998). A model for determining the size of time buffers in DBR. Chinese Journal of Management Science., 6(1), 16–20.

    Google Scholar 

  • Liao, C. J., & Shyu, C. H. (1991). An analytical determination of lead time with normal demand. International Journal of Operations & Production Management, 11(9), 72–78.

    Article  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.

    Article  Google Scholar 

  • Newbold, R. C. (1998). Project management in the fast lane: Applying the theory of constraints. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Pantouvakis, J. P., & Manoliadis, O. G. (2006). A practical approach to resource-constrained project scheduling. Operational Research, 6(3), 299–309.

    Article  Google Scholar 

  • Partovi, F. Y., & Burton, J. (1993). Timing of monitoring and control of CPM projects. IEEE Transactions on Engineering Management, 40(1), 68–75.

    Article  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.

    Article  Google Scholar 

  • Pi-Fang, H., & Miao-Hsueh, S. (2005). Using the theory of constraints to improve the identification and solution of managerial problems. International Journal of Management, 22(3), 415.

    Google Scholar 

  • Piney, C. (2000). Critical path or critical chain-combining the best of both. PM NETWORK, 14(12), 51–55.

    Google Scholar 

  • Radovilsky, Z. D. (1998). A quantitative approach to estimate the size of the time buffer in the theory of constraints. International Journal of Production Economics, 55(2), 113–119.

    Article  Google Scholar 

  • Rahman, S. U. (1998). Theory of constraints: A review of the philosophy and its applications. International Journal of Operations & Production Management, 18(4), 336–355.

    Article  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.

    Article  Google Scholar 

  • Ribera, J., Sachon, M., & Grasas, A. (2003). Putting the core elements ofcritical chain project management into perspective: A general framework for buffer management. In G. Spina & A. Vinelli (Eds.), One world? One view of OM? The challenges of integrating research and practice. Padova: ServiziGrafici Editoriali (SGE).

    Google Scholar 

  • Schwarz, P. (1991). The art of the long view: Planning for the future in an uncertain world. New York: Currency Doubleday.

    Google Scholar 

  • Steele, D. C., Philipoom, P. R., Malhotra, M. K., & Fry, T. D. (2005). Comparisons between Drum–Buffer–Rope and material requirements planning: A case study. International Journal of Production Research, 43(15), 3181–3208.

    Article  Google Scholar 

  • Trietsch, D. (2005). The effect of systemic errors on optimal project buffers. International Journal of Project Management, 23(4), 267–274.

    Article  Google Scholar 

  • Tukel, O. I., & Rom, W. (2006). Analysis of resource buffer management in critical chain scheduling. In Project Management Institute, PMI research conference 2006 proceedings. Pennsylvania: Newtown Square.

  • 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.

    Article  Google Scholar 

  • Vanhoucke, M. (2010). Using activity sensitivity and network topology information to monitor project time performance. Omega, 38(5), 359–370.

    Article  Google Scholar 

  • Wei, C. C., Liu, P. H., & Tsai, Y. C. (2002). Resource-constrained project management using enhanced theory of constraint. International Journal of Project Management, 20(7), 561–567.

    Article  Google Scholar 

  • Yang, L., Fu, Y., Li, S., Huang, B., & Peng, T. (2008). A buffer sizing approach in critical chain scheduling with attributes dependent. In 2008 4th International conference on wireless communications, networking and mobile computing (pp. 1–4). IEEE.

  • Yang, X., & Hu, H. (2005). Multi-project management based on critical chain method. Industrial Engineering and Management, 10(2), 48–52.

    Google Scholar 

  • Zhang, J., Song, X., Chen, H., & Shi, R. (2016b). Determination of critical chain project buffer based on information flow interactions. Journal of the Operational Research Society, 67(9), 1146–1157.

    Article  Google Scholar 

  • Zhang, J., Song, X., & Díaz, E. (2014). Buffer sizing of critical chain based on attribute optimization. Concurrent Engineering, 22(3), 253–264.

    Article  Google Scholar 

  • Zhang, J., Song, X., & Díaz, E. (2016a). Project buffer sizing of a critical chain based on comprehensive resource tightness. European Journal of Operational Research, 248(1), 174–182.

    Article  Google Scholar 

  • Zhang, J., & Wan, D. (2019). Integrated buffer monitoring and control based on grey neural network. Journal of the Operational Research Society, 70(3), 516–529.

    Article  Google Scholar 

  • Zhao, Y., Cui, N., & Tian, W. (2020). A two-stage approach for the critical chain project rescheduling. Annals of Operations Research, 285(1), 67–95.

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 71572010).

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  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China

    Junguang Zhang & Dan Wan

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  1. Junguang Zhang
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  2. Dan Wan
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Correspondence to Junguang Zhang.

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Zhang, J., Wan, D. Determination of early warning time window for bottleneck resource buffer. Ann Oper Res 300, 289–305 (2021). https://doi.org/10.1007/s10479-021-03960-1

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