Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking
journal contributionposted on 2018-11-23, 11:28 authored by Pablo Ballesteros-Perez, Kamel Mohamed Elamrousy, M. Carmen Gonzalez-Cruz
When shortening a project’s duration, activity crashing, fast-tracking and substitution are the three most commonly employed compression techniques. Crashing generally involves allocating extra resources to an activity with the intention of reducing its duration. To date, the activity time-cost relationship has for the most part been assumed to be linear, however, a few studies have suggested that this is not necessarily the case in practice. This paper proposes two non-linear theoretical models which assume either collaborative or non-collaborative resources. These models closely depict the two most common situations occurring during construction projects. The advantages of these models are that they allow for both discrete and continuous, as well as deterministic and stochastic configurations. Additionally, the quantity of resources required for crashing the activity can be quantified. Comparisons between the models and another recent fast-tracking model from the literature are discussed, and a Genetic Algorithm is implemented for a fictitious application example involving both compression techniques.
This research was supported by the CIOB Bowen Jenkins Legacy Research Fund (reference BLJ2016/BJL.01) and by NERC under the Environmental Risks to Infrastructure Innovation Programme (reference NE/R008876/1) at the University of Reading.
- Architecture, Building and Civil Engineering
Published inAutomation in Construction
Pages229 - 240
CitationBALLESTEROS-PEREZ, P., ELAMROUSY, K.M. and GONZALEZ-CRUZ, M.C., 2018. Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking. Automation in Construction, 97, pp. 229-240.
- AM (Accepted Manuscript)
Publisher statementThis paper was accepted for publication in the journal Automation in Construction and the definitive published version is available at https://doi.org/10.1016/j.autcon.2018.11.001.