Solving stochastic time-cost trade-off problems via modified double-loop procedure with adaptive domain decomposition method

Stochastic time-cost trade-off (TCT) problems are of significant concern to project managers because various uncertain factors have to be considered when making appropriate balance between project completion time and cost. In the paper we consider the stochastic TCT problem, where the project completion time (PCT) unreliability is involved and constrained to be less than a prescribed threshold. To tackle the concerned problem, previous studies have implemented the double loop procedure, where a genetic algorithm (GA) is used in the outer loop for optimization, and Monte Carlo simulation (MCS) is used in the inner loop for examining the unreliability constraint. The original double loop procedure is computationally inefficient, taking hours or days even for a small to medium project. The present study proposes an efficient simulation method, referred to as adaptive domain decomposition method (DDM), to replace MCS for credibly examining the unreliability constraint. By modifying the double loop procedure with adaptive DDM, the computational resources can be effectively allocated, and the computational efficiency can be greatly improved. As shown in the illustrative example, the modified procedure significantly outperforms the original procedure, and it is hundreds of times faster to obtain similar optimization results. With the great efficiency improvement, this study contributes to the widespread acceptance of stochastic TCT analysis in practical applications.