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A hybrid projection method for resource-constrained project scheduling problem under uncertainty

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Abstract

Resource constraint project scheduling problem (RCPSP) is one of the most important problems in the scheduling environment. This paper introduces a new framework to collect the activities’ duration and resource requirement by group decision-making, solve the RCPSP with variable durations, and obtain the buffer to protect the schedule. Firstly, the duration and resources of the project’s activities are determined by a new expert weighting method. In the group decision-making, hybrid projection measure is introduced to construct the aggregated decision about some RCPSP parameters. The hybrid projection includes the projection, normalized projection, and bi-directional projection. In the second step, a RCPSP model is presented where the duration of activities can change within certain intervals. Thus, the problem is called the RCPSP with variable durations. The intervals for activities’ duration and resource requirements are obtained from the group decision-making in the first step. Genetic algorithm and vibration damping optimization are applied to solve the RCPSP with variable durations. In the third step, the project’s buffer is determined to protect the schedule. In this step, the intervals for activities’ duration are converted into interval-valued fuzzy (IVF) numbers and the buffer sizing method is extended using IVF numbers. Finally, the presented framework is solved for a practical example and the results are reported.

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References

  1. Almeida BF, Correia I, Saldanha-da-Gama F (2016) Priority-based heuristics for the multi-skill resource constrained project scheduling problem. Expert Syst Appl 57:91–103

    Article  Google Scholar 

  2. Altarazi FM (2017) Multi-mode resource constrained project scheduling using differential evolution algorithm

  3. Habibi F, Barzinpour F, Sadjadi S (2018) Resource-constrained project scheduling problem: review of past and recent developments. J Proj Manag 3(2):55–88

    Google Scholar 

  4. Bianco L, Caramia M, Giordani S (2019) A chance constrained optimization approach for resource unconstrained project scheduling with uncertainty in activity execution intensity. Comput Ind Eng 128:831–836

    Article  Google Scholar 

  5. Tao S, Dong ZS (2017) Scheduling resource-constrained project problem with alternative activity chains. Comput Ind Eng 114:288–296

    Article  Google Scholar 

  6. Agarwal A, Colak S, Erenguc S (2011) A neurogenetic approach for the resource-constrained project scheduling problem. Comput Oper Res 38(1):44–50

    Article  MathSciNet  MATH  Google Scholar 

  7. Chu Z, Xu Z, Li H (2019) New heuristics for the RCPSP with multiple overlapping modes. Comput Ind Eng 131:146–156

    Article  Google Scholar 

  8. Talbot FB (1982) Resource-constrained project scheduling with time-resource tradeoffs: the nonpreemptive case. Manag Sci 8(10):1197–1210

    Article  MATH  Google Scholar 

  9. Zhang Z, Zhong X (2018) Time/resource trade-off in the robust optimization of resource-constraint project scheduling problem under uncertainty. J Ind Prod Eng 35(4):243–254

    Google Scholar 

  10. Long LD, Ohsato A (2008) Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. Int J Proj Manag 26(6):688–698

    Article  Google Scholar 

  11. Ahn BS (2015) Extreme point-based multi-attribute decision analysis with incomplete information. Eur J Oper Res 240(3):748–755

    Article  MathSciNet  MATH  Google Scholar 

  12. Chen T-Y (2014) A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: a comparative perspective. Inf Sci 281:97–112

    Article  MathSciNet  MATH  Google Scholar 

  13. Deng X et al (2014) An evidential game theory framework in multi-criteria decision making process. Appl Math Comput 244:783–793

    MathSciNet  MATH  Google Scholar 

  14. Fan Z-P et al (2013) A method for stochastic multiple attribute decision making based on concepts of ideal and anti-ideal points. Appl Math Comput 219(24):11438–11450

    MathSciNet  MATH  Google Scholar 

  15. Krohling RA, de Souza TTM (2012) Combining prospect theory and fuzzy numbers to multi-criteria decision making. Expert Syst Appl 39(13):11487–11493

    Article  Google Scholar 

  16. Aramesh S, Mousavi SM, Mohagheghi V (2021) A new comprehensive project scheduling, monitoring, and management framework based on the critical chain under interval type-2 fuzzy uncertainty. Iran J Fuzzy Syst 18(1):151–170

    MathSciNet  Google Scholar 

  17. Khorshidi HA, Aickelin U (2020) Multicriteria group decision-making under uncertainty using interval data and cloud models. J Oper Res Soc 66:1–15

    Google Scholar 

  18. Mohagheghi V, Mousavi SM, Vahdani B (2017) Enhancing decision-making flexibility by introducing a new last aggregation evaluating approach based on multi-criteria group decision making and Pythagorean fuzzy sets. Appl Soft Comput 61:527–535

    Article  Google Scholar 

  19. Zavadskas EK et al (2017) Integrated group fuzzy multi-criteria model: case of facilities management strategy selection. Expert Syst Appl 82:317–331

    Article  Google Scholar 

  20. Al-Harbi A, Al-Subhi KM (2001) Application of the AHP in project management. Int J Proj Manag 19(1):19–27

    Article  Google Scholar 

  21. Lifson MW, Shaifer EF (1982) Decision and risk analysis for construction management. Wiley, New York

    Google Scholar 

  22. Schuyler J (1996) Decision analysis in projects. Project Management Institute, Upper Darby

  23. Ye J (2017) Simplified neutrosophic harmonic averaging projection-based method for multiple attribute decision-making problems. Int J Mach Learn Cybernet 8(3):981–987

    Article  Google Scholar 

  24. Blagojevic B et al (2016) Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm. Inf Sci 330:260–273

    Article  Google Scholar 

  25. Lourenzutti R, Krohling RA (2006) A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Inf Sci 330:1–18

    Article  Google Scholar 

  26. Zhang X, Xu Z (2014) The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment. Knowl Based Syst 61:48–58

    Article  Google Scholar 

  27. Liu P, You X (2019) Bidirectional projection measure of linguistic neutrosophic numbers and their application to multi-criteria group decision making. Comput Ind Eng 128:447–457

    Article  Google Scholar 

  28. Yue C (2019) A projection-based approach to software quality evaluation from the users’ perspectives. Int J Mach Learn Cybernet 10(9):2341–2353

    Article  Google Scholar 

  29. Tsao C-Y, Chen T-Y (2016) A projection-based compromising method for multiple criteria decision analysis with interval-valued intuitionistic fuzzy information. Appl Soft Comput 45:207–223

    Article  Google Scholar 

  30. Sun R et al (2018) A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization. Int J Fuzzy Syst 20(7):2144–2160

    Article  Google Scholar 

  31. Wang H, He S, Pan X (2018) A new bi-directional projection model based on pythagorean uncertain linguistic variable. Information 9(5):104

    Article  Google Scholar 

  32. Pramanik S et al (2017) Bipolar neutrosophic projection based models for solving multi-attribute decision making problems. Infinite Study 6:66

    Google Scholar 

  33. Boctor FF (1996) Resource-constrained project scheduling by simulated annealing. Int J Prod Res 34(8):2335–2351

    Article  MATH  Google Scholar 

  34. De Reyck B, Demeulemeester E, Herroelen W (1998) Local search methods for the discrete time/resource trade-off problem in project networks. Nav Res Logist 45(6):553–578

    Article  MathSciNet  MATH  Google Scholar 

  35. Hartmann S (2001) Project scheduling with multiple modes: a genetic algorithm. Ann Oper Res 102(1):111–135

    Article  MathSciNet  MATH  Google Scholar 

  36. Aramesh S et al (2021) A soft computing approach based on critical chain for project planning and control in real-world applications with interval data. Appl Soft Comput 98:106915

    Article  Google Scholar 

  37. Mehdizadeh E, Akbari H (2017) A novel vibration damping optimization algorithm for resource constrained multi-project scheduling problem. Econ Comput Econ Cybernet Stud Res 51(2):66

    Google Scholar 

  38. Mehdizadeh E, Nezhad Dadgar S (2014) Using vibration damping optimization algorithm for resource constraint project scheduling problem with weighted earliness-tardiness penalties and interval due dates. Econ Comput Econ Cybernet Stud Res 48(1):66

    Google Scholar 

  39. Atli O, Kahraman C (2012) Fuzzy resource-constrained project scheduling using taboo search algorithm. Int J Intell Syst 27(10):873–907

    Article  Google Scholar 

  40. Hu X et al (2016) Incorporation of activity sensitivity measures into buffer management to manage project schedule risk. Eur J Oper Res 249(2):717–727

    Article  Google Scholar 

  41. Skondras E et al (2016) An analytic network process and trapezoidal interval-valued fuzzy technique for order preference by similarity to ideal solution network access selection method. Int J Commun Syst 29(2):307–329

    Article  Google Scholar 

  42. Ghoddousi P, Ansari R, Makui A (2017) An improved robust buffer allocation method for the project scheduling problem. Eng Optim 49(4):718–731

    Article  MathSciNet  Google Scholar 

  43. Ye J (2017) Bidirectional projection method for multiple attribute group decision making with neutrosophic numbers. Neural Comput Appl 28(5):1021–1029

    Article  Google Scholar 

  44. Ghasemi M, Meysam Mousavi S, Aramesh S (2020) A new combination of multi-mode resource-constrained project scheduling and group decision-making process with interval-fuzzy information. J Ind Syst Eng 13(1):216–239

    Google Scholar 

  45. She B, Chen B, Hall NG (2021) Buffer sizing in critical chain project management by network decomposition. Omega 102:102382

    Article  Google Scholar 

  46. Xu Z (2015) Uncertain multi-attribute decision making: methods and applications. Springer

    Book  MATH  Google Scholar 

  47. Yue C (2017) Two normalized projection models and application to group decision-making. J Intell Fuzzy Syst 32(6):4389–4402

    Article  MATH  Google Scholar 

  48. Tirkolaee EB et al (2020) A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. J Clean Prod 250:119517

    Article  Google Scholar 

  49. Zahra SR, Chishti MA (2020) Fuzzy logic and fog based secure architecture for internet of things (flfsiot). J Ambient Intell Hum Comput 66:1–25

    Google Scholar 

  50. Yuan Y et al (2021) Multi-objective multi-mode resource-constrained project scheduling with fuzzy activity durations in prefabricated building construction. Comput Ind Eng 158:107316

    Article  Google Scholar 

  51. Ren Z, Liao H, Liu Y (2020) Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19. Comput Ind Eng 145:106517

    Article  Google Scholar 

  52. Bahri O, Mourhir A, Papageorgiou EI (2020) Integrating fuzzy cognitive maps and multi-agent systems for sustainable agriculture. Euro-Mediterr J Environ Integr 5(1):1–10

    Article  Google Scholar 

  53. Dixit V, Srivastava RK, Chaudhuri A (2014) Procurement scheduling for complex projects with fuzzy activity durations and lead times. Comput Ind Eng 76:401–414

    Article  Google Scholar 

  54. Alizdeh S, Saeidi S (2020) Fuzzy project scheduling with critical path including risk and resource constraints using linear programming. Int J Adv Intell Paradig 16(1):4–17

    Google Scholar 

  55. Sambuc R (1975) Fonctions and floues: application a l'aide au diagnostic en pathologie thyroidienne. Diss. Faculté de Médecine de Marseille

  56. Ashtiani B et al (2009) Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets. Appl Soft Comput 9(2):457–461

    Article  Google Scholar 

  57. Wei S-H, Chen S-M (2009) Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Syst Appl 36(2):2285–2299

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

    Saeed Aramesh

  2. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Uwe Aickelin & Hadi Akbarzadeh Khorshidi

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  1. Saeed Aramesh
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  2. Uwe Aickelin
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  3. Hadi Akbarzadeh Khorshidi
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Correspondence to Saeed Aramesh.

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Aramesh, S., Aickelin, U. & Akbarzadeh Khorshidi, H. A hybrid projection method for resource-constrained project scheduling problem under uncertainty. Neural Comput & Applic 34, 14557–14576 (2022). https://doi.org/10.1007/s00521-022-07321-2

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