Stochastic scheduling optimization of repetitive construction projects to minimize project duration and cost

Abstract

Considering risk and uncertainty during the scheduling optimization of repetitive construction projects is a challenging task. This article presents the development of a novel multi-objective stochastic scheduling optimization model for minimizing the expected project duration and cost of repetitive construction projects. The developed model integrated Monte Carlo simulation with a multi-objective genetic algorithm to: (1) consider uncertainties associated with the production rates of the construction crews; and (2) identify an optimal crew formation and crew deployment date for each activity that minimize the project duration and cost. The performance of the developed model was analyzed using a real-life case study that highlighted the novel model capabilities in generating a set of optimal trade-off solutions between expected project duration and cost while considering all uncertainties in crew production rates. These novel capabilities of the developed model enable construction planners to identify and implement an optimal resource utilization plan that account for inherent project risks.