A computational study of exact approaches for the adjustable robust resource-constrained project scheduling problem. (November 2018)

• Record Type: Journal Article
• Authors: Bruni, M.E.; Di Puglia Pugliese, L.; Beraldi, P.; Guerriero, F.
• Is Part Of: Computers & operations research. Volume 99(2018)
• Page Start: 178
• Page End: 190
• Publication Date: 2018-11
• Subjects: Project scheduling -- Resource constraints -- Robust optimization -- Benders decomposition -- Budget of uncertainty; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.06.016
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: The resource-constrained project scheduling problem under uncertainty is addressed. The activity durations are supposed to belong to the budgeted uncertainty polytope. Optimal solution approaches based on a decomposition strategy are defined. A computational study is carried out on a set of benchmark instances. Abstract: We study the robust resource-constrained project scheduling problem under budgeted uncertainty polytope. The problem can be seen as a very challenging variant of the resource-constrained project scheduling problem, where the objective function minimises the worst-case makespan, assuming that activity durations are subject to interval uncertainty. The model allows to control the level of robustness by means of a protection factor related to the risk aversion of the decision maker. The paper introduces two exact decomposition approaches to tackle the solution of this difficult problem. An extensive computational experimentation, on standard benchmark instances from the literature, is carried out to assess and compare the performance of the proposed methods, also with respect to the state-of-the-art exact solution approach.