Enhancing project scheduling with neutrosophic sets: New solution approaches for solving neutro-sophic CPM/PERT

Authors

  • Pratyasha Dash Department of Mathematics, Rajendra University, Balangir, 767002, Odisha, India.
  • Kshitish Kumar Mohanta * Department of Mathematics, Rajendra University, Balangir, 767002, Odisha, India. https://orcid.org/0000-0003-2681-4785

https://doi.org/10.48313/uda.vi.61

Abstract

The Critical Path Method (CPM) is an important tool in project management. However, the traditional form of CPM deals with complications associated with the ambiguity and uncertainty in estimating the duration of activities. This paper presents two new methods to solve the Neutrosophic Critical Path Method (Neu-CPM), utilizing Triangular Neutrosophic Numbers (TNNs) to define activity durations under indeterminacy. The methods are designed to conduct a forward pass and backward pass simultaneously to find the earliest and latest time for each event while at the same time to find the total float for each activity, enabling project scheduling under uncertain conditions. Neu-CPM provides a more improved approach to handling non-precise and incomplete data compared to the traditional fuzzy or intuitionistic approaches, based on its inclusion of membership degrees of truth, indeterminacy, and falsity. A numerical example is provided showing the methodology’s ability to identify the project’s critical path in a Neutrosophic environment while studying the effect of various risk elements on the critical path. The results show that Neu-CPM provides the opportunity of more flexibility, accuracy, and reliability in project scheduling in uncertain conditions, with useful applications to practice.

Keywords:

Neutrosophic set, Project scheduling, Triangular neutrosophic number, Critical path method

References

  1. [1] Kerzner, H. (2025). Project management: A systems approach to planning, scheduling, and controlling. John Wiley &

  2. [2] Sons. https://b2n.ir/mu9992

  3. [3] Project Management Institute. (2021). A Guide to the Project Management Body of Knowledge (PMBOK®Guide)--

  4. [4] Seventh Edition and The Standard for Project Management. Project management institute. https://books.google.nl/books/about/A_Guide_to_the_Project_Management_Body_o.html? id=lKsxEAAAQBAJ&redir_esc=y

  5. [5] Mubarak, S. A. (2015). Construction project scheduling and control. John Wiley & Sons. https://books.google.nl/books/about/Construction_Project_Scheduling_and_Cont.html?id=kSu9BgAAQBAJ&redir_esc=y

  6. [6] Shtub, A., Bard, J. F., & Globerson, S. (2005). Project management: Processes, methodologies, and economics. Pearson

  7. [7] Prentice Hall. https://cir.nii.ac.jp/crid/1971430859852190223

  8. [8] Larson, E., & Gray, C. (2017). Project management: The managerial process. McGraw Hill.

  9. [9] https://www.amazon.com/Project-Management-Managerial-Mcgraw-hill-Operations/dp/1259666093

  10. [10] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  11. [11] Ross, T. J. (2005). Fuzzy logic with engineering applications. John Wiley & Sons.

  12. [12] https://home.iitk.ac.in/~avrs/ManyValuedLogic/FuzzyLogicforEngineers.pdf

  13. [13] Chen, S. M., & Chen, J. H. (2009). Fuzzy risk analysis based on similarity measures between interval-valued fuzzy

  14. [14] numbers and interval-valued fuzzy number arithmetic operators. Expert systems with applications, 36(3), 6309–6317. https://doi.org/10.1016/j.eswa.2008.08.017

  15. [15] Li, L., & Wang, J. (2015). Uncertain engineering critical path solving method based on interval number theory. 2015

  16. [16] international conference on mechatronics, electronic, industrial and control engineering (MEIC-15) (pp. 1409–1412). Atlantis

  17. [17] Press. https://www.atlantis-press.com/proceedings/meic-15/19956

  18. [18] Nasution, S. H. (2002). Fuzzy critical path method. IEEE transactions on systems, man, and cybernetics, 24(1), 48–57.

  19. [19] https://doi.org/10.1109/21.259685

  20. [20] Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20(1), 87–96. https://doi.org/10.1016/S0165-

  21. [21] (86)80034-3

  22. [22] Garg, H., & Kumar, K. (2019). Improved possibility degree method for ranking intuitionistic fuzzy numbers and their

  23. [23] application in multiattribute decision-making. Granular computing, 4(2), 237–247. https://doi.org/10.1007/s41066-018-0092-7 [13] Smarandache, F. (1999). A unifying field in logics: Neutrosophic logic. American Research Press. https://web-

  24. [24] archive.southampton.ac.uk/cogprints.org/1919/3/eBook-Neutrosophics2.pdf

  25. [25] Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets.

  26. [26] Journal of intelligent & fuzzy systems, 26(5), 2459–2466. https://doi.org/10.3233/IFS-130916

  27. [27] Broumi, S., Bakali, A., & Bahnasse, A. (2018). Neutrosophic sets: An overview. Infinite Study.

  28. [28] https://books.google.nl/books/about/Neutrosophic_Sets_An_Overview.html?id=pftuDwAAQBAJ&redir_esc=y

  29. [29] Khan, Z., Gulistan, M., Kausar, N., & Park, C. (2021). Neutrosophic Rayleigh model with some basic characteristics and

  30. [30] engineering applications. IEEE access, 9, 71277–71283. https://doi.org/10.1109/ACCESS.2021.3078150

  31. [31] Mohanta, K. K., Sharanappa, D. S., & Aggarwal, A. (2023). Value and ambiguity Index-based ranking approach for

  32. [32] solving neutrosophic data envelopment analysis. Neutrosophic sets and systems, 57(1), 370–397.

  33. [33] https://digitalrepository.unm.edu/nss

  34. [34] Khalifa, N. E. M., Smarandache, F., Manogaran, G., & Loey, M. (2024). A study of the neutrosophic set significance on deep transfer learning models: An experimental case on a limited covid-19 chest x-ray dataset. Cognitive computation, 16(4),

  35. [35] –1611. https://doi.org/10.1007/s12559-020-09802-9

  36. [36] Mohanta, K. K., & Sharanappa, D. S. (2023). Neutrosophic data envelopment analysis: A comprehensive review and

  37. [37] current trends. Optimality, 1(1), 10–22. https://doi.org/10.22105/opt.v1i1.19

  38. [38] Khan, M., Son, L. H., Ali, M., Chau, H. T. M., Na, N. T. N., & Smarandache, F. (2018). Systematic review of decision

  39. [39] making algorithms in extended neutrosophic sets. Symmetry, 10(8), 314. https://doi.org/10.3390/sym10080314

  40. [40] Mohanta, K. K., & Toragay, O. (2023). Enhanced performance evaluation through neutrosophic data envelopment

  41. [41] analysis leveraging pentagonal neutrosophic numbers. Journal of operational and strategic analytics, 1(2), 70–80. https://doi.org/10.56578/josa010204

  42. [42] Kahraman, C., & Otay, İ. (2019). Fuzzy multi-criteria decision-making using neutrosophic sets (Vol. 16). Springer.

  43. [43] https://doi.org/10.1007/978-3-030-00045-5

  44. [44] Mohamed, M., Abdel-Baset, M., Smarandache, F., & Zhou, Y. (2017). A critical path problem in neutrosophic

  45. [45] environment. Infinite study: EL segundo, CA, USA, 1. https://fs.unm.edu/neut/ACriticalPathProblemInNeutrosophic.pdf [24] Kungumaraj, E. (2024). An evaluation of triangular neutrosophic PERT analysis for real-life project time and cost

  46. [46] estimation. Neutrosophic sets and systems, 63(1), 5. https://digitalrepository.unm.edu/nss_journal/vol63/iss1/5?

  47. [47] utm_source=digitalrepository.unm.edu%2Fnss_journal %2Fvol63%2Fiss1%2F5&utm_medium=PDF&utm_campaign=PDFCoverPages

  48. [48] Pratyusha, M. N., & Kumar, R. (2024). Advancements in critical path method using neutrosophic theory: A review.

  49. [49] Uncertainty discourse and applications, 1(1), 73–78. https://doi.org/10.48313/uda.v1i1.28

  50. [50] Sinika, S., & Ramesh, G. (2024). Trapezoidal neutrosophic program evaluation and review technique using interval

  51. [51] arithmetic operations. IAENG international journal of applied mathematics, 54(3), 324–341. https://B2n.ir/jr5854

  52. [52] Pratyusha, M. N., & Kumar, R. (2024). Solving neutrosophic critical path problem using Python. Journal of information

  53. [53] and optimization sciences, 45(4), 897–911. https://doi.org/10.47974/jios-1614

  54. [54] Romero Fernández, A., Moreira Rosales, L. V., Arciniegas Paspuel, O. G., Jarrín López, W. B., & Sotolongo León, A. R.

  55. [55] (2021). Neutrosophic statistics for project management. application to a computer system project. Neutrosophic sets and

  56. [56] systems, 44(1), 34. https://digitalrepository.unm.edu/nss_journal/vol44/iss1/34/

  57. [57] Deli, I., & Şubaş, Y. (2017). A ranking method of single valued neutrosophic numbers and its applications to multi-

  58. [58] attribute decision making problems. International journal of machine learning and cybernetics, 8(4), 1309–1322. https://doi.org/10.1007/s13042-016-0505-3

  59. [59] Khatter, K. (2020). Neutrosophic linear programming using possibilistic mean. Soft computing, 24(22), 16847–16867.

  60. [60] https://doi.org/10.1007/s00500-020-04980-y

  61. [61] Mohanta, K. K., Sharanappa, D. S., & Aggarwal, A. (2023). A novel modified Khatter’s approach for solving

  62. [62] Neutrosophic Data Envelopment Analysis. Croatian operational research review, 14(1), 15–28.

  63. [63] https://ojs.srce.hr/index.php/crorr/article/view/22530

Published

2025-06-12

How to Cite

Dash, P. ., & Mohanta, K. K. (2025). Enhancing project scheduling with neutrosophic sets: New solution approaches for solving neutro-sophic CPM/PERT. Uncertainty Discourse and Applications, 2(2), 111-123. https://doi.org/10.48313/uda.vi.61

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