This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.
The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.
The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.
There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.
Khalilzadeh, M. (2022), "Resource levelling in projects considering different activity execution modes and splitting", Journal of Engineering, Design and Technology, Vol. 20 No. 5, pp. 1073-1100. https://doi.org/10.1108/JEDT-11-2020-0463
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