effects of project uncertainties on nonlinear time-cost tradeoff profile

this study presents the effects of project uncertainties on nonlinear time-cost tradeoff (tct) profile of real life engineering projects by the fusion of fuzzy logic and artificial neural network (ann) models with hybrid meta-heuristic (hmh) technique, abridged as fuzzy-ann-hmh. nonlinear time-cost relationship of project activities is dealt with ann models. ann models are then integrated with hmh technique to search for pareto-optimal nonlinear tct profile. hmh technique incorporates simulated annealing in the selection process of multiobjective genetic algorithm. moreover, in real life engineering projects, uncertainties like management experience, labor skills, and weather conditions are commonly involved, which affect the duration and cost of the project activities. fuzzy-ann-hmh analyses responsiveness of nonlinear tct profile with respect to these uncertainties. a comparison of fuzzy-ann-hmh is made with another method in literature to solve nonlinear tct problem and the superiority of fuzzy-ann-hmh is demonstrated by results. the study gives project planners to carry out the best plan that optimizes time and cost to complete a project under uncertain environment.