An Evolutionary Multi-objective Scenario- Based Approach for Stochastic Resource Investment Project Scheduling
Many planning problems, such as mission capability planning, can be modelled as project scheduling problems. Unlike conventional deterministic project scheduling problems, such problems involve uncertainty and the execution of the plan will definitely be perturbed by many factors. In other words, the circumstances under which the plan will be executed are changing and stochastic. In this paper, we [Xiong, Liu, Chen, and Abbass] first use scenarios to represent the stochastic elements in the problem; these are: perturbation strength and perturbation occurrence time. We define and explain the Stochastic Resource Investment Project Scheduling (SRIPS) problem. A multi-objective optimization model of SRIPS is proposed where three optimization objectives are considered simultaneously: makespan, cost, and robustness. A multi-objective genetic algorithm is employed to solve the problem. Finally, we generate two test problems with 30 and 60 nondummy activities to validate the performance of the proposed approach and analyze the sensitivity of the results to different parameter settings.
