Optimization of Stochastic Repetitive Construction Projects: Minimizing Duration Uncertainties

Various studies aimed to optimize the schedules of repetitive projects using line of balance (LOB) via different meta-heuristic optimization techniques. However, most studies considered constant productivity rates to complete activities' units, disregarding the stochastic nature of the construction methods. This paper aims to minimize the uncertainties of the total project duration in non-serial repetitive projects using non-dominated sorting genetic algorithm II optimization (NSGA II). This is achieved through (1) developing a generic scheduling module for repetitive projects using best practices for LOB, (2) utilizing Monte Carlo simulation to assess the effect of uncertainties of crews' productivity rates on the total project duration, and (3) implementing NSGA II to optimize the total project duration and minimize the overall standard deviation of the stochastic project schedule. The presented model would thus help planners and decision-makers to define the optimal schedules that decrease the uncertainties in meeting the scheduled project due date, thus decreasing the overall project risk.