Cost Minimization with Project Crashing: Comparison of the Traditional Solution and Genetic Algorithm Approach

Yıl 2024, Cilt: 28 Sayı: 5, 959 - 977, 25.10.2024

Semih Caglayan , Sadik Yıgıt

https://doi.org/10.16984/saufenbilder.1467829

Öz

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields.

Anahtar Kelimeler

Project crashing, Resource allocation, Project scheduling, Cost minimization, Metaheuristic algorithms, Genetic algorithm

Kaynakça

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