An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations

• Published in: Omega (Oxford) Vol. 71; pp. 66 - 84
• Main Authors: Bruni, M.E., Di Puglia Pugliese, L., Beraldi, P., Guerriero, F.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2017
• Subjects: Benders decomposition; Project scheduling; Resource constraints; Robust optimization; Resource constraints; Project scheduling; Robust optimization; Benders decomposition
• ISSN: 0305-0483; 1873-5274
• DOI: 10.1016/j.omega.2016.09.009

This paper addresses the resource-constrained project scheduling problem with uncertain activity durations. An adaptive robust optimization model is proposed to derive the resource allocation decisions that minimize the worst-case makespan, under general polyhedral uncertainty sets. The properties of the model are analyzed, assuming that the activity durations are subject to interval uncertainty where the level of robustness is controlled by a protection factor related to the risk aversion of the decision maker. A general decomposition approach is proposed to solve the robust counterpart of the resource-constrained project scheduling problem, further tailored to address the uncertainty set with the protection factor. An extensive computational study is presented on benchmark instances adapted from the PSPLIB. •We consider the resource-constrained project scheduling problem with uncertain activity durations.•A two-stage mathematical model is provided to formulate the robust version.•A decomposition approach is defined for general uncertainty sets.•Computational analysis is carried out on instances inspired by the scientific literature.