Abstract
Affected by various uncertain factors, the critical chain is random. The article studies the critical chain and its randomness, and points out that when there is no buffer, the critical chain method and the critical path method have the same identity, and the central limit law of the same distribution can solve the randomness of the critical chain. This conclusion is mainly based on the following research: (1) The paper analyze the technical points of the critical chain, and points out that the setting of the buffer can be replaced by the operation time of the process; (2) The principles and expressions are proposed for determining the critical chain; (3) The influence factors of the randomness of the critical chain are analyzed; (4) The randomness of the random critical chain is determined based on the central limit law of the same distribution.
Similar content being viewed by others
Data availability
The data used to support the findings of this study are included within this article. Requests for more details should be made to the corresponding author.
Abbreviations
- CPM:
-
Critical Path Methodology
- CCM:
-
Critical Chain Method
- ACTIM:
-
Activity Time(Heuristic Algorithm)
- T:
-
Total Time
- LF:
-
Late Fnish Date
- EF:
-
Early Fnish Date
- LS:
-
Late Start Date
- R:
-
Resource
- P:
-
Probability
References
Aramesh S, Mousavi SM, Mohagheghi V, Zavadskas EK, Antucheviciene J (2020) A soft computing approach based on critical chain for project planning and control in real-world applications with interval data. Appl Soft Comput 98(6):106915. https://doi.org/10.1016/J.ASOC.2020.106915
Burns J, Cao Q (2011) Deterministic, path-sensitive heuristics for project earned value management. Int J Proj Org Manag 3(1):1–21. https://doi.org/10.1504/IJPOM.2011.038861
Ciarapica FE, Mazzuto G, Bevilacqua M (2017) A heuristic scheduling algorithm based on fuzzy logic and critical chain project management. Int J Proj Org Manag 9(4):303. https://doi.org/10.1504/IJPOM.2017.088244
Dong T, Xue F, Xiao C, Zhang J (2021) Workflow scheduling based on deep reinforcement learning in the cloud environment. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02884-1
Gentili GB, Riminesi C, Tesi V (2006) Low cost microwave sensor for moisture content measurement in paper milling industry. Sens Imaging 7:155–173. https://doi.org/10.1007/s11220-006-0027-2
Goldratt E.M (1997a) Critical chain. The North River Press, Great Barrington
Goldratt E.M.(1997b) Critical chain. The North River Press, Great Barrington
Goto H (2017) Forward-compatible framework with critical-chain project management using a max-plus linear representation. Opsearch 54:201–216. https://doi.org/10.1007/s12597-016-0276-3
Han XK (2021) WBS-free scheduling method based on database relational model. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01106-x
Han XK, Yan WZ, Lu M (2021a) Research on reasoning concerning emergency measures for industrial project scheduling control. Adv Civil Eng. https://doi.org/10.1155/2021/5595354
Han XK, Yan WZ, Lu M (2021b) Intelligent critical path computation algorithm utilising ant colony optimisation for complex project scheduling. Complexity. https://doi.org/10.1155/2021/9930113
Han X, Yan W, Lu M (2021c) Research on the coordination mechanism of major industrial project engineering and construction multi-agents based on structural holes theory. PLOS ONE 16(8): e0255858. https://doi.org/10.1371/journal.pone.0255858
Hegazy T, Menesi R (2010) Critical path segments scheduling technique. J Constr Eng Manag 136(10):1078–1085. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000212
Hosseini-Motlagh SM, Samani MRG, Homaei S (2020) Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). J Ambient Intell Human Comput 11:1085–1104. https://doi.org/10.1007/s12652-019-01315-0
Keshavarz-Ghorbani F, Khamseh A (2021) A Modeling and optimizing a multi-period closed-loop supply chain for pricing, warranty period, and quality management. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-02971-x
Kim K, Garza JMDL (2005) Evaluation of the resource-constrained critical path method algorithms. J Constr Eng Manag 131(5):522–532. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:5(522)
Lucko G, Thompson RC, Yi S (2016) Simulating the balanced allocation of project float to the critical path in network schedules. Construct Res Congress. https://doi.org/10.1061/9780784479827.076
Ma G, Wang A, Nan L, Gu L, Qi A (2014) Improved critical chain project management framework for scheduling construction projects. J Const Eng Manag. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000908
Modieginyane KM, Malekian R, Letswamotse BB (2019) Flexible network management and application service adaptability in software defined wireless sensor networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0766-7
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. https://doi.org/10.1080/07408179808966521
Olivieri H, Seppänen O, Thais A (2019) Survey comparing critical path method, last planner system, and location-based techniques. J Construct Eng Manag. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001644
Qian S, Gong T (2009) An improved project buffer sizing approach to critical chain management under resources constraints and fuzzy uncertainty. Int Conf Artif Intell Comput Intell IEEE Comput Soc. https://doi.org/10.1109/AICI.2009.192
She B, Chen B, Hall NG (2020) Buffer sizing in critical chain project management by network decomposition. Omega 102:102382. https://doi.org/10.1016/j.omega.2020.102382
Taghipour M, Seraj F, Amin M, Delivand MC (2020) Evaluating CCPM method versus CPM in multiple petrochemical projects. Management. https://doi.org/10.31058/j.mana.2020.32004
Yang S, Lei F (2014) Critical chain and evidence reasoning applied to multi-project resource schedule in automobile r&d process. Int J Project Manage 32(1):166–177. https://doi.org/10.1016/j.ijproman.2013.01.010
Zhang R, Zhu Y, Xu S (2020) Scheduling risk evaluation for the integrated design of blanket system project for CFETR based on fuzzy PRET method. J Fusion Energy 39:156–162. https://doi.org/10.1007/s10894-020-00246-5
Zhao ZY, You WY, Zuo J (2010) Application of innovative critical chain method for project planning and control under resource constraints and uncertainty. J Constr Eng Manag 136(9):1056–1060. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000209
Acknowledgements
The authors thank the Editor, Associate Editor, and referees for their comments on the initial version of the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (51478384) and the Industrial Building Environment and Energy Conservation Innovation Team of China (2017KCT-14).
Author information
Authors and Affiliations
Contributions
XH and WY conceptualized the main idea of this research project; WY and TL designed and conducted the experiments; WY checked the results; XH wrote the whole paper. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics approval
Any submission that has data collected from human subjects requires ethics approval.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Han, X., Yan, W. & Liu, T. Critical chains and its randomness study for scheduling optimization. Int J Syst Assur Eng Manag 13, 844–854 (2022). https://doi.org/10.1007/s13198-021-01345-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01345-y