A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments. (May 2019)

• Record Type: Journal Article
• Authors: Ma, Zhiqiang; Demeulemeester, Erik; He, Zhengwen; Wang, Nengmin
• Is Part Of: Computers & industrial engineering. Volume 131(2019)
• Page Start: 382
• Page End: 390
• Publication Date: 2019-05
• Subjects: Proactive project scheduling -- Solution robustness -- Surrogate robustness measure -- Stochastic resource availabilities -- Stochastic activity durations; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.04.014
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A general framework of the slack-based surrogate robustness measures is proposed. The best surrogate measures are found for two kinds of uncertain environments. The improvement is 13.79% under the environment of stochastic activity durations. The impact of parameters on the performance of the robustness measure is analyzed. The time buffering strategies are different under the two uncertain environments. Abstract: This paper addresses the proactive resource-constrained project scheduling problem, aiming to explore better surrogate robustness measures for project managers that want to generate robust baseline schedules under uncertain environments. The contribution of this paper is threefold. First, we propose a general framework of slack-based surrogate robustness measures and introduce three parameters to distinguish different alternative calculations of the measure. A computational experiment based on reactive simulation is constructed where the performance of the surrogate measures is evaluated by the reactive cost and two types of uncertain environments, i.e. stochastic resource availabilities and stochastic activity durations, are taken into account. Second, we analyze the impact of the three parameters as well as the two uncertain environments on the surrogate robustness measures and find the best measures for different situations. The proposed surrogate robustness measures are shown to be effective. Compared with benchmark measures, the improvements are respectively 2.67% and 13.79% under the two uncertain environments. Third, we investigate the difference of buffering strategies between the two uncertain environments. For the environment of stochastic resource availabilities, it turns out to be better to have a uniform distribution of time buffers throughout the schedule, while the reverse is true for the environment of stochastic activity durations.