Author:

Keywords:

Science & Technology, Technology, Computer Science, Interdisciplinary Applications, Engineering, Industrial, Computer Science, Engineering, Proactive project scheduling, Solution robustness, Surrogate robustness measure, Stochastic resource availabilities, Stochastic activity durations, TRADE-OFF, HEURISTIC PROCEDURES, MANAGEMENT, STABILITY, CLASSIFICATION, CONSTRUCTION, 01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering, Industrial Engineering & Automation, 40 Engineering, 46 Information and computing sciences, 49 Mathematical sciences

Abstract:

© 2019 Elsevier Ltd 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.