Abstract
The time–cost trade-off has been recognized as a very significant aspect of construction management. Generally, time–cost trade-off can be modeled as a fuzzy linear programming problem with symmetric or non-symmetric fuzzy numbers. However, it was successfully solved when the fuzzy membership functions are only symmetric. In the present work, a novel approach is introduced to solve fuzzy linear programming problem with non-symmetric fuzzy membership functions by transforming it to its corresponding nearest symmetric one. The transformed problem is then converted to its crisp linear programming problem and then solved by the standard primal simplex method. Two examples are presented to show the effectiveness of the proposed approach, and the results are discussed.
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References
O. Moselhi, Schedule Compression using the direct stiffness method. Can. J. Civ. Eng. 20, 65–72 (1993). https://doi.org/10.1139/l93-007
C.-W. Feng, L. Liu, S.A. Burns, Using genetic algorithms to solve construction time–cost trade-off problems. J. Comput. Civ. Eng. 11, 184–189 (1997)
D.X.M. Zheng, S.T. Ng, M.M. Kumaraswamy, Applying a genetic algorithm based multi objective approach for time cost optimization. J. Constr. Eng. Manag. 130, 168–176 (2004). https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(168)
E. Fallah-mehdipour, O. Bozorg, M.M. Rezapour, M.A. Mariño, Expert Systems with Applications Extraction of decision alternatives in construction management projects: Application and adaptation of NSGA-II and MOPSO. Expert Syst. Appl. 39, 2794–2803 (2012). https://doi.org/10.1016/j.eswa.2011.08.139
F.A. Zammori, M. Braglia, M. Frosolini, A fuzzy multi-criteria approach for critical path definition. Int. J. Proj. Manag. 27, 278–291 (2009). https://doi.org/10.1016/j.ijproman.2008.03.006
M.J. Liberatore, Project schedule uncertainty analysis using fuzzy logic. Proj. Manag. J. 33, 15–22 (2002)
M.J. Liberatore, Critical path analysis with fuzzy activity times. IEEE Trans. Eng. Manag. 55, 329–337 (2008). https://doi.org/10.1109/TEM.2008.919678
P. Lorterapong, O. Moselhi, Project Network analysis using fuzzy set theory. J. Constr. Eng. Manag. 122, 308–318 (1996)
C.-W. Feng, L. Liu, S.A. Burns, Stochastic construction time–cost trade-off analysis. J. Comput. Civ. Eng. 14, 117–126 (2000). https://doi.org/10.1061/(ASCE)0887-3801(2000)14:2(117)
S.S. Leu, A.T. Chen, C.H. Yang, A GA-based fuzzy optimal model for construction time–cost trade-off. Int. J. Proj. Manag. 19, 47–58 (2001). https://doi.org/10.1016/S0263-7863(99)00035-6
D.X.M. Zheng, S.T. Ng, Stochastic time-cost optimization model incorporating fuzzy sets theory and nonreplaceable front. J. Constr. Eng. Manag. 131, 176–186 (2005). https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(176)
H.R. Tareghian, H. Taheri, An application of randomized minimum cut to the project time/cost tradeoff problem. Appl. Math. Comput. 173, 1200–1207 (2006). https://doi.org/10.1016/j.amc.2005.04.063
F.T. Lin, Fuzzy crashing problem on project management based on confidence-interval estimates, in Proc.—8th Int. Conf. Intell. Syst. Des. Appl. ISDA 2008, 2008, pp. 164–169. https://doi.org/https://doi.org/10.1109/ISDA.2008.190.
E. Eshtehardian, A. Afshar, R. Abbasnia, Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost. Constr. Manag. Econ. 26, 679–691 (2008). https://doi.org/10.1080/01446190802036128
E. Eshtehardian, A. Afshar, R. Abbasnia, Automation in construction fuzzy-based MOGA approach to stochastic time–cost trade-off problem. Autom. Constr. 18, 692–701 (2009). https://doi.org/10.1016/j.autcon.2009.02.001
K.A. Haque, A.A. Hasin, Genetic algorithm for project time-cost optimization in fuzzy environment. J. Ind. Eng. Manag. 5, 364–381 (2012). https://doi.org/10.3926/jiem.410
R.M. Ahari, S.T.A. Niaki, Fuzzy optimization in cost, time and quality trade-off in software projects with quality obtained by fuzzy rule base. Int. J. Model. Optim. 3, 176–179 (2013). https://doi.org/10.7763/IJMO.2013.V3.262
A. Gholipour, M.R. Feylizadeh, Extend time–cost trade off model with weighted fuzzy goal additive programming. Tech. J. Eng. Appl. Sci. ©2013 TJEAS J. 3, 336 (2013)
H. Ke, W. Ma, Y. Ni, Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem. Appl. Math. Comput. 215, 308–313 (2009). https://doi.org/10.1016/j.amc.2009.05.004
R.A. Al Haj, S.M. El-Sayegh, time cost optimization model considering float consumption impact. J. Constr. Eng. Manag. 141, 04015001 (2015). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000966
S. Hossain, S. Mahmud, M. Hossain, Fuzzy multi-objective linear programming for project management decision under uncertain environment with AHP based weighted average method. J. Mod. Sci. Technol. 4, 163–178 (2016)
H. Naseri, M. Ghasbeh, Time–cost trade off to compensate delay of project using genetic algorithm and linear programming. Int. J. Innov. Manag. Technol. 9, 285–290 (2018). https://doi.org/10.18178/ijimt.2018.9.6.826
B. Abinaya, M. Evangeline Jebaseeli, E. Henry Amirtharaj, An approach to solve fuzzy time cost trade off problems. Int. J. Res. Advent Technol. (IJRAT). Special Issue, (2019) 5–8.
D. Golenko-Ginzburg, On the distribution of activity time in pert. J. Oper. Res. Soc. 39, 767–771 (1988). https://doi.org/10.1057/jors.1988.132
H.J. Zimmermann, Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst. 1, 45–55 (1978). https://doi.org/10.1016/0165-0114(78)90031-3
H. Tanaka, T. Okuda, K. Asai, On fuzzy-mathematical programming. J. Cybern. 3, 37–46 (1973). https://doi.org/10.1080/01969727308545912
H.J. Rommelfanger, The advantages of fuzzy optimization models in practical use. Fuzzy Optim. Decis. Mak. 3, 295–309 (2004). https://doi.org/10.1007/s10700-004-4200-6
A. Amid, S.H. Ghodsypour, C. O’Brien, Fuzzy multiobjective linear model for supplier selection in a supply chain. Int. J. Prod. Econ. 104, 394–407 (2006). https://doi.org/10.1016/j.ijpe.2005.04.012
K. Ganesan, P. Veeramani, Fuzzy linear programs with trapezoidal fuzzy numbers. Ann. Oper. Res. 143, 305–315 (2006). https://doi.org/10.1007/s10479-006-7390-1
N. Mahdavi-Amiri, S.H. Nasseri, Duality in fuzzy number linear programming by use of a certain linear ranking function. Appl. Math. Comput. 180, 206–216 (2006). https://doi.org/10.1016/j.amc.2005.11.161
N. Mahdavi-Amiri, S.H. Nasseri, Duality results and a dual simplex method for linear programming problems with trapezoidal fuzzy variables. Fuzzy Sets Syst. 158, 1961–1978 (2007). https://doi.org/10.1016/j.fss.2007.05.005
S.H.N.N. Mahdavi-Amiri, Some duality results on linear programming problems with symmetric fuzzy numbers. Fuzzy Inf. Eng. 1, 59–66 (2009)
S.H. Nasseri, A. Ebrahimnejad, S. Mizuno, Duality in fuzzy linear programming with symmetric trapezoidal numbers. Appl. Appl. Math. 05, 1467–1482 (2010)
A. Ebrahimnejad, S.H. Nasseri, A dual simplex method for bounded linear programmes with fuzzy numbers. Int. J. Math. Oper. Res. 2, 762–779 (2010). https://doi.org/10.1504/IJMOR.2010.035498
A. Ebrahimnejad, S.H. Nasseri, S.M. Mansourzadeh, Bounded primal simplex algorithm for bounded linear programming with fuzzy cost coefficients. Int. J. Oper. Res. Inf. Syst. 2, 96–120 (2011). https://doi.org/10.4018/joris.2011010105
A. Ebrahimnejad, S.H. Nasseri, S.M. Mansourzadeh, Modified bounded dual network simplex algorithm for solving minimum cost flow problem with fuzzy costs based on ranking functions. J. Intell. Fuzzy Syst. 24, 191–198 (2013). https://doi.org/10.3233/IFS-2012-0545
A. Ebrahimnejad, M. Tavana, A novel method for solving linear programming problems with symmetric trapezoidal fuzzy numbers. Appl. Math. Model. 38, 4388–4395 (2014). https://doi.org/10.1016/j.apm.2014.02.024
A. Kumar, P. Kaur, J. Kaur, Fuzzy optimal solution of fully fuzzy project crashing problems with new representation of LR flat fuzzy numbers, in Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6743 LNAI (2011), pp. 171–174. https://doi.org/10.1007/978-3-642-21881-1_28.
C. Veeramani, C. Duraisamy, M. Sumathi, Nearest symmetric trapezoidal fuzzy number approximation preserving expected interval. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 21, 777–794 (2013). https://doi.org/10.1142/S0218488513500372
P. Bonnal, D. Gourc, G. Lacoste, Where do we stand with fuzzy project scheduling? J. Constr. Eng. Manag. 130, 114–123 (2004). https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(114)
S.N. Sivanandam, S. Sumathi, S.N. Deepa, Introduction to Fuzzy Logic Using MATLAB (Springer, Berlin, 2007) https://doi.org/10.1007/978-3-540-35781-0
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Elkalla, I., Elbeltagi, E. & El Shikh, M. Solving Fuzzy Time–Cost Trade-Off in Construction Projects Using Linear Programming. J. Inst. Eng. India Ser. A 102, 267–278 (2021). https://doi.org/10.1007/s40030-020-00489-7
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DOI: https://doi.org/10.1007/s40030-020-00489-7