Time-cost trade-off model in GERT-type network with characteristic function for project management. (July 2022)

• Record Type: Journal Article
• Authors: Tao, Liangyan; Su, Xuebi; Javed, Saad Ahmed
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Time-cost trade-off -- Characteristic function -- GERT -- Endogenous uncertainties -- Simulation -- Project management; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108222
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: Minimizing mean duration doesn't imply maximization of on-time probability. A robust probability density function of project time/cost is obtained. A novel time–cost trade-off model in GERT network is proposed. A GA-based algorithm is designed to solve the model with endogenous uncertainties. Abstract: Activity crashing is an efficient way for project acceleration but always leads to time–cost trade-off issues. The current study proposes an optimal time–cost trade-off model in GERT (Graphical Evaluation and Review Technique) with consideration to both the probability of on-time delivery and under-budget. Firstly, the hypothesis in the previous literature that the minimization of expected completion time is equivalent to the maximization of the on-time completion probability is proven to be invalid. Secondly, to ensure successful project completion, the constraints on the probability of on-time delivery and under-budget are added in the trade-off model. Furthermore, the characteristic function-enhanced GERT is designed to derive the probability density function of project time and cost, thereby yielding the on-time probability and under-budget probability. Consequently, the stochastic programming model for time–cost trade-offs is presented to minimize the mean duration while ensuring a desired on-time completion probability and under-budget probability. To solve the proposed stochastic programming, a genetic algorithm-based solution procedure is designed. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed model. The study contributes to our understanding that minimizing mean duration cannot necessarily maximize on-time project completion. It also helps us reduce the mean project duration and guarantee high on-time delivery and under-budget performance simultaneously, which are two critical success factors in project management.