A two-stage adjustable robust optimization approach for multi-work package project scheduling problem under uncertain environments

Haohua Zhang, Lubo Li, Erik Demeulemeester and Sijun Bai

Journal of the Operational Research Society, 2025, vol. 76, issue 11, 2210-2226

Abstract: A robust project schedule is essential to defend against the interference of uncertainty factors. However, generating robust schedules for the Multi-Work Package Project Scheduling Problem (MWPPSP) in an uncertain environment is challenging because of the precedence constraints between activities and work packages. Moreover, traditional static robust optimization methods are so conservative that project schedules are inefficient. This study proposes a new two-stage adjustable robust optimization approach to generate schedules with a different robustness level according to the manager’s risk attitude, which combines priority rule (PR)-based heuristics and an exact approach that can efficiently solve large-scale problems. The first stage of the proposed approach obtains the expected durations of work packages under different scenarios through simulation. The second stage is the multi-work package project’s adjustable robust optimization, which extends the static robust optimization approaches to a dynamic setting to overcome their shortcomings of being too conservative. Numerical experiments are conducted based on the modified datasets, which demonstrate the effectiveness of the proposed approach.

Date: 2025
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DOI: 10.1080/01605682.2025.2463556

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