• DOI:10.48550/arXiv.2207.03330
• Corpus ID: 250334589

Empirical Evaluation of Project Scheduling Algorithms for Maximization of the Net Present Value

@article{Lacerda2022EmpiricalEO,
  title={Empirical Evaluation of Project Scheduling Algorithms for Maximization of the Net Present Value},
  author={Isac Mendes Lacerda and Eber Assis Schmitz and Jayme Luiz Szwarcfiter and Rosiane de Freitas},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.03330}
}

• I. M. Lacerda, E. Schmitz, +1 author Rosiane de Freitas
• Published 5 July 2022
• Computer Science
• ArXiv

This paper presents an empirical performance analysis of three project scheduling algorithms dealing with maximizing projects’ net present value with unrestricted resources. The selected algorithms, being the most recently cited in the literature, are: Recursive Search (RS), Steepest Ascent Approach (SAA) and Hybrid Search (HS). The main motivation for this research is the lack of knowledge about the computational complexities of the RS, SAA, and HS algorithms, since all studies to date show some gaps in the analysis. Furthermore, the empirical analysis performed to date does not consider the fact that one algorithm (HS) uses a dual search strategy, which markedly improved the algorithm’s performance, while the others don’t. In order to obtain a fair performance comparison, we implemented the dual search strategy into the other two algorithms (RS and SAA), and the new algorithms were called Recursive Search Forward-Backward (RSFB) and Steepest Ascent Approach Forward-Backward (SAAFB). The algorithms RSFB, SAAFB, and HS were submitted to a factorial experiment with three different project network sampling characteristics. The results were analyzed using the Generalized Linear Models (GLM) statistical modeling technique that showed: a) the general computational costs of RSFB, SAAFB, and HS; b) the costs of restarting the search in the spanning tree as part of the total cost of the algorithms; c) and statistically significant differences between the distributions of the algorithms’ results.