Project crashing using a fuzzy multi-objective model considering time,cost,quality and risk under fast tracking technique: A case study

Abstract

Project managers normally investigate how to reduce the total completion time of projects undertaken subject to the pre-determined objectives. The purpose of this paper is compression of total projects’ duration subject to influencing factors such as cost, time, quality and risk. In the present study, by considering factors affecting project success such as cost, time, quality and risk, project crashing and fast tracking are both employed then a fuzzy multi-objective non-linear model is proposed. Each project is associated with uncertainty and the lack of consideration of these uncertainties might lead to project failure. In addition, a fuzzy approach has been employed for incorporating the uncertainties associated with activities. In the proposed model, crashing and fast tracking of projects were considered simultaneously for the first time. According to a real case study considered in this paper, after using the proposed model, the cost of compression is also reduced. In addition, we determined which activities have to be crashed or fast tracked in order to attain the objective. The proposed methodology can be practically applied through mega projects such as construction and “Engineering, Procurement, and Construction” (EPC) projects where the deadline closes to being achieved. As another contribution, a unique feature of the proposed model is its capability for taking both crashing and fast-tracking simultaneously into consideration.

Keywords

Fuzzy multi-objective model project crashing fast tracking time cost

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References

Project Management Institute., A guide to the project management body of knowledge (PMBOK^® guide). Project Management Institute, 2013.

Javed

S.A.

and Liu

, Evaluation of project management knowledge areas using grey incidence model and AHP, 2017 International Conference on Grey Systems and Intelligent Services (GSIS), 2017, pp. 120–120.

Project Management Institute, Practice Standard for Scheduling—second edition, Project Management Institute, 2011.

Keeney

R.L.

, Raiffa

, Decisions with multiple objectives: Preferences and value tradeoffs, Cambridge University Press, 1993.

Farquhar

P.H.

, State of the art—utility assessment methods, Manage Sci 30(11) (1984), 1283–1300.

Shafer

, A mathematical theory of evidence, Princeton University Press, 1976.

Steuer

R.E.

, Multiple criteria optimization: Theory, computation, and application, Wiley, 1986.

Mahdavi-Amiri

and Nasseri

S.H.

, Duality results and a dual simplex method for linear programming problems with trapezoidal fuzzy variables, Fuzzy Sets Syst 158(17) (2007), 1961–1978.

Zadeh

L.A.

, Fuzzy sets, Inf Control 8(3) (1965), 338–353.

10.

Tanaka

, Okuda

and Asai

, On fuzzy-mathematical programming, J Cybern 3(4) (1973), 37–46.

11.

Zimmermann

H.-J.

, Fuzzy programming and linear programming with several objective functions, Fuzzy Sets Syst 1(1) (1978), 45–55.

12.

Dyson

R.G.

, Maximin programming, fuzzy linear programming and multi-criteria decision making, J Oper Res Soc 31(3) (1980), 263–267.

13.

Lai

Y.-J.

, Hwang

C.-L.

, Fuzzy mathematical programming, 1992, pp. 74–186.

14.

Tanaka

, Guo

and Zimmermann

H.-J.

, Possibility distributions of fuzzy decision variables obtained from possibilistic linear programming problems, Fuzzy Sets Syst 113(2) (2000), 323–332.

15.

Maleki

H.R.

, Tata

and Mashinchi

, Linear programming with fuzzy variables, Fuzzy Sets Syst 109(1) (2000), 21–33.

16.

Buckley

J.J.

and Feuring

, Evolutionary algorithm solution to fuzzy problems: Fuzzy linear programming, Fuzzy Sets Syst 109(1) (2000), 35–53.

17.

Allahdadi

and Nehi

H.M.

, Fuzzy linear programming with interval linear programming approach, 13(1) (2011), 1–12.

18.

Allahviranloo

, Lotfi

F.H.

, Kiasary

M.K.

, Kiani

N.A.

and Alizadeh

, Solving fully fuzzy linear programming problem by the ranking function, Appl Math Sci 2(1) (2008), 19–32.

19.

Ezzati

, Khorram

and Enayati

, A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem, Appl Math Model 39(12) (2015), 3183–3193.

20.

Alavidoost

M.H.

, Babazadeh

and Sayyari

S.T.

, An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem, Appl Soft Comput 40 (2016), 221–235.

21.

Babu

A.J.G.

and Suresh

, Project management with time, cost, and quality considerations, Eur J Oper Res 88(2) (1996), 320–327.

22.

Khang

D.B.

and Myint

Y.M.

, Time, cost and quality trade-off in project management: A case study, Int J Proj Manage 17(4) (1999), 249–256.

23.

Leu

, Chen

and Yang

, A GA-based fuzzy optimal model for construction time ± cost, 19 (2001), 47–58.

24.

Abbasi

G.Y.

and Mukattash

A.M.

, Crashing PERT networks using mathematical programming, Int J Proj Manage 19(3) (2001), 181–188.

25.

Tareghian

H.R.

and Taheri

S.H.

, On the discrete time, cost and quality trade-off problem, Appl Math Comput 181(2) (2006), 1305–1312.

26.

Chen

, Mei

, Luo

, X. Y. ICIA’07. International, and undefined 2007, Identification of nonlinear system based on a new hybrid gradient-based PSO algorithm, 2007. ieeexplore.ieee.org.

27.

, Ma

and Ni

, Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem, Appl Math Comput 215(1) (2009), 308–313.

28.

, Ma

and Chen

, Modeling stochastic project time– cost trade-offs with time-dependent activity durations, Appl Math Comput 218(18) (2012), 9462–9469.

29.

Hazir

Ö.

, Haouari

and Erel

, Robust scheduling and robustness measures for the discrete time/cost trade-off problem, Eur J Oper Res 207(2) (2010), 633–643.

30.

Sonmez

and Bettemir

Ö.H.

, A hybrid genetic algorithm for the discrete time– cost trade-off problem, Expert Syst Appl 39(13) (2012), 11428–11434.

31.

Kim

, Kang

and Hwang

, A practical approach to project scheduling: Considering the potential quality loss cost in the time– cost tradeoff problem, Int J Proj Manage 30(2) (2012), 264–272.

32.

Tavana

, Abtahi

and Khalili-Damghani

, A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems, Expert Syst Appl 41(4) (2014), 1830–1846.

33.

and Ma

, Modeling project time– cost trade-off in fuzzy random environment, Appl Soft Comput 19 (2014), 80–85.

34.

Monghasemi

, Nikoo

M.R.

, Khaksar Fasaee

M.A.

and Adamowski

, A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects, Expert Syst Appl 42(6) (2015), 3089–3104.

35.

Aminbakhsh

and Sonmez

, Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem, Expert Syst Appl 51 (2016), 177–185.

36.

Salari

, Bagherpour

and Reihani

M.H.

, A time-cost trade-off model by incorporating fuzzy earned value management: A statistical based approach, J Intell Fuzzy Syst 28(4) (2015), 1909–1919.

37.

Saif

, Abbas

and Fayed

, The PDBO algorithm for discrete time, cost and quality trade-off in software projects with expressing quality by defects, Proc Comput Sci 65(Iccmit) (2015), 930–939.

38.

Mahmoudi

and Feylizadeh

M.R.

, A mathematical model for crashing projects by considering time, cost, quality and risk, J Proj Manage 2 (2017), 27–36.

39.

Mahmoudi

and Feylizadeh

M.R.

, A grey mathematical model for crashing of projects by considering time, cost, quality, risk and law of diminishing returns, Grey Syst Theory Appl 8(3) (2018), 272–294.

40.

Arikan

and Güngör

, An application of fuzzy goal programming to a multiobjective project network problem, Fuzzy Sets Syst 119(1) (2001), 49–58.

41.

Zhang

and Xing

, Fuzzy-multi-objective particle swarm optimization for time– cost– quality tradeoff in construction, Autom Constr 19(8) (2010), 1067–1075.

42.

Bagherpour

, Feylizadeh

M.R.

and Cioffi

D.F.

, Time, cost, and quality trade-offs in material requirements planning using fuzzy multi-objective programming, Proc Inst Mech Eng Part B J Eng Manuf 226(3) (2012), 560–564.

43.

Feylizadeh

M.R.

, Hendalianpour

and Bagherpour

, A fuzzy neural network to estimate at completion costs of construction projects, Int J Ind Eng Comput 3(3) (2012), 477–484.

44.

Jebaseeli

M.E.

and Dhayabaran

D.P.

, Solution procedure for fuzzy project crashing problem through goal programming technique, 4(5) (2015), 158–170.

45.

Hossain

M.S.

, Mahmud

and Hossain

M.M.

, Fuzzy multi-objective linear programming for project management decision under uncertain environment with AHP based weighted average method, doi.org, 9(20) (2016), 53–60.

46.

Chutia

, Mahanta

and Datta

, Uncertainty modelling of atmospheric dispersion model using fuzzy set and imprecise probability, J Intell Fuzzy Syst 25(3) (2013), 737–746.

47.

Zare

, Feylizadeh

M.R.

, Mahmoudi

and Liu

, Suitable computerized maintenance management system selection using grey group TOPSIS and fuzzy group VIKOR: A case study, Decis Sci Lett 7(4) (2018), 341–358.

48.

Liu

, Zhang

and Jin

, A multi-attribute group decision-making method based on interval-valued trapezoidal fuzzy numbers hybrid harmonic averaging operators, J Intell Fuzzy Syst 23(5) (2012), 159–168.

49.

Liu

, Chen

S.-M.

and Liu

, Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators, Inf Sci (NY) 411 (2017), 98–121.

50.

Milenkovic

, Bojovic

, Ribeiro

R.A.

and Glisovic

, A fuzzy simulated annealing approach for project time-cost tradeoff, J Intell Fuzzy Syst 23(5) (2012), 203–215.

51.

Yan

and Ma

Z.M.

, Modeling fuzzy information in fuzzy extended entity-relationship model and fuzzy relational databases, J Intell Fuzzy Syst 27(4) (2014), 1881–1896.

52.

Mahmoudi

, Feylizadeh

M.R.

, Darvishi

and Liu

, Grey-fuzzy solution for multi-objective linear programming with interval coefficients, Grey Syst Theory Appl 8(3) (2018), 312–327.

53.

Liu

, Liu

and Merigó

J.M.

, Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making, Appl Soft Comput 62 (2018), 395–422.

54.

Liu

and Wang

, Some improved linguistic intuitionistic fuzzy aggregation operators and their applications to multiple-attribute decision making, Int J Inf Technol Decis Mak 16(3) (2017), 817–850.

55.

Liu

and Liu

, An approach to multiple attribute group decision making based on intuitionistic trapezoidal fuzzy power generalized aggregation operator, Int J Comput Intell Syst 7(2) (2014), 291–304.

56.

Chen

, Wang

and Wu

, Strategic decisions using the fuzzy PROMETHEE for IS outsourcing, Expert Syst Appl 38(10) (2011), 13216–13222.

57.

Project Management Institute, Practice standard for project risk management, Project Management Institute, 2009.

58.

Kumar

, Kaur

and Singh

, A new method for solving fully fuzzy linear programming problems, Appl Math Model 35(2) (2011), 817–823.

59.

Parsaei

, Keramati

M.A.

, Zorriassatine

and Feylizadeh

M.R.

, An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry, Int J Ind Eng Comput 3(2) (2012), 211–224.

60.

Zadeh

L.A.

, A note on Z-numbers, Inf Sci (NY) 181(14) (2011), 2923–2932.

61.

Azadeh

, Saberi

, Atashbar

N.Z.

, Chang

and Pazhoheshfar

, Z-AHP: A Z-number extension of fuzzy analytical hierarchy process, 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST), 2013, pp. 141–147.