PurposeInterrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.Design/methodology/approachThis paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.FindingsThe results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.Originality/valueAvailable studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.
Monte Carlo Simulation-Based Bat Algorithm for Solving Stochastic Multi-Objective Optimization Problems
