A hybrid metaheuristic algorithm for resource-constrained proactive project scheduling with uncertainty-handling effort

This paper addresses a resource-constrained proactive project scheduling problem, where activity durations are stochastic variables and uncertainty-handling effort is devoted to decreasing the standard deviation of activity duration. The task is to determine the optimal level of uncertainty-handling effort and the corresponding baseline schedule under the constraints of renewable resources and project deadline, minimizing the total cost which consists of the uncertainty-handling cost and the robustness and adjustment costs of the baseline schedule. Based on the problem formulation, an optimization model is constructed, in which there exist two kinds of trade-off relationships among related costs. Then, for the NP-hardness of the problem, a hybrid metaheuristic algorithm is designed by combining a variable neighborhood search with a tabu search. In light of the characteristics of the problem, four measures are proposed to improve the searching efficiency of the algorithm. Finally, an extensive computational experiment is conducted on a randomly generated dataset. Based on the obtained results, the algorithm and improvement measures are evaluated, and a sensitivity analysis of the effect of key parameters on the objective function value is also carried out. The research conclusions are as follows: the algorithm equipped with all the improvement measures is the most promising algorithm for the studied problem, and the key parameters may generate an effect on the costs relevant to uncertainty.