Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation
- Published
- Accepted
- Subject Areas
- Algorithms and Analysis of Algorithms, Computer Aided Design
- Keywords
- Resource-constrain project scheduling, Genetic algorithm, Strength Pareto evolutionary approach II, Resource leveling, Scheduling
- Copyright
- © 2015 Nie et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation. PeerJ PrePrints 3:e1299v1 https://doi.org/10.7287/peerj.preprints.1299v1
Abstract
The resource-constrained project scheduling problem has received broad attentions and was evolved into various sub-problems such as resource-constrained discrete time-cost tradeoff problem. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently with RCPSP. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.
Author Comment
This preprint will be submitted to a peer reviewed journal