A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

Abstract

Resource allocation is one of the core issues in project scheduling to ensure the effective use of scare renewable resources, and has been regularly encountered in production systems in the manufacturing and service industries. The transfers of renewable resources between activities generally incur certain scheduling costs and affect the robustness of a certain schedule in an uncertain environment. To address this issue, a bi-objective optimisation model is proposed to make the resource transfer decisions, which aims to minimise the transfer cost and maximise solution robustness in the presence of activity duration variability. The proposed model employs a novel resource-oriented flow formulation that is different from those of the previous literature. A NSGA-II and a Pareto simulated annealing (PSA) algorithm have been applied as the solution methodologies. Besides, the effectiveness of the metaheuristics are evaluated in comparison with a $\epsilon$-constraint method. In detail, the algorithms are carried out on a set of benchmarks and are compared to test their efficiencies based on four performance metrics: number of non-dominated solutions, general distance, hypervolume and spacing. Finally, a case study of a real project further indicates that the suggested model and algorithms are applicable and beneficial to the problem in practice.

Keywords:

• resource-constrained project scheduling problem
• robust resource allocation
• resource transfer cost
• multi-objective optimisation
• duration uncertainty

Acknowledgements

This research was supported by the National Natural Science Foundation of China [grant numbers 71701067, 71801218, and 71572010]. It was also funded by the Natural Science Foundation of Hunan Province, China [grant number 2019JJ50039] and by the Research Project of National University of Defense Technology (grant number ZK18-03-16).

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Xuejun Hu http://orcid.org/0000-0002-1742-0584

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant numbers 71701067, 71801218, and 71572010]. It was also funded by the Natural Science Foundation of Hunan Province, China [grant number 2019JJ50039] and by the Research Project of National University of Defense Technology (grant number ZK18-03-16).