The integration of resource allocation and time buffering for bi-objective robust project scheduling

Abstract

In the recent decades, the recognition that uncertainty lies at the heart of modern project management has induced considerable research efforts on robust project scheduling for dealing with uncertainty in a scheduling environment. The literature generally provides two main strategies for the development of a robust predictive project schedule, namely robust resource allocation and time buffering. Yet, the previous studies seem to have neglected the potential benefits of an integration between the two. Besides, few efforts have been made to protect simultaneously the project due date and the activity start times against disruptions during execution, which is desperately demanded in practice. In this paper, we aim at constructing a proactive schedule that is not only short in time but also less vulnerable to disruptions. Firstly, a bi-objective optimisation model with a proper normalisation of the two components is proposed in the presence of activity duration variability. Then a two-stage heuristic algorithm is developed which deals with a robust resource allocation problem in the first stage and optimally determines the position and the size of time buffers using a simulated annealing algorithm in the second stage. Finally, an extensive computational experiment on the PSPLIB network instances demonstrates the superiority of the combination between resource allocation and time buffering as well as the effectiveness of the proposed two-stage algorithm for generating proactive project schedules with composite robustness.

Keywords:

• robust project scheduling
• bi-objective optimisation
• robust resource allocation
• time buffering
• two-stage algorithm

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their invaluable comments and suggestions, which help to improve this paper. We would also like to acknowledge the work of Liang et al. (2019) for providing a research basis for this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant numbers 71701067, 71801218, and 71271097]. It was also funded by the Natural Science Foundation of Hunan Province, China [Grant No. 2019JJ50039].