Proactive policies for the stochastic resource-constrained project scheduling problem

• Published in: European journal of operational research Vol. 214; no. 2; pp. 308 - 316
• Main Authors: Deblaere, Filip, Demeulemeester, Erik, Herroelen, Willy
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.10.2011; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Applied sciences; Deviation; Exact sciences and technology; Execution policies; Mathematical analysis; Operational research; Operational research and scientific management; Operational research. Management science; Operations research; Policies; Proactive scheduling; Project management; Project scheduling; Project scheduling Proactive scheduling Execution policies Stochastic RCPSP; Scheduling; Scheduling, sequencing; Stochastic models; Stochastic RCPSP; Stochasticity; Studies; Vectors (mathematics); Execution policies; Project scheduling; Proactive scheduling; Stochastic RCPSP; Project management; Variability; Minimization; Scheduling; Precedence constraint; Stochastic programming; Completion; Completeness; Resource constraint; Cost function; Proactive service; Resource management; Mathematical programming
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2011.04.019

► In this paper we study the RCPSP when activity durations are uncertain. ► We consider positive and negative deviations from the predictive activity starting times of inflexible activities. ► Costs are also associated with exceeding the project due date and a bonus is awarded when finishing early. ► We derive optimal predictive starting times based on the newsvendor problem. ► Our procedure is vastly superior than the current state-of-the-art for this problem. The resource-constrained project scheduling problem involves the determination of a schedule of the project activities, satisfying the precedence and resource constraints while minimizing the project duration. In practice, activity durations may be subject to variability. We propose a stochastic methodology for the determination of a project execution policy and a vector of predictive activity starting times with the objective of minimizing a cost function that consists of the weighted expected activity starting time deviations and the penalties or bonuses associated with late or early project completion. In a computational experiment, we show that our procedure greatly outperforms existing algorithms described in the literature.