Please use this identifier to cite or link to this item: http://bspace.buid.ac.ae/handle/1234/1024
Title: An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling
Authors: ALKETBI, SAIF
Keywords: genetic algorithm
project management
project scheduling
Issue Date: Oct-2016
Publisher: The British University in Dubai (BUiD)
Abstract: This research is tackling the issue of complex resources scheduling in project management. In traditional planning tools, resource allocation is sequence based. This normally results in a very simple baseline schedule. However, in reality, the problem of project scheduling is more complex and it depends on a multitude of factors. For example, project scheduling when combined with resources constraints and activities duration uncertainty is an interesting research problem that has recently has attracted the effort many researchers. Previous research has developed a simulation-based approach to solve the problem by optimizing resources resource allocation decisions on starting specific project activities at specific times. Several nonlinear optimization models were developed for this purpose assuming uniform resource availability and sequence based project tasks. The work presented in thesis add to the existing literature in a proposing the use of a genetic algorithm uncertain approach to resource- scheduling in projects. This research focuses on one of the most important aspects, which is uncertainty. The uncertainty aspect was not incorporated effectively in in the previous resource modeling models. The uncertainty of time estimation is one of the most important problems which reduce any resource scheduler effectiveness. Genetic algorithm was chosen as the main methodology to build resource scheduler. The results showed the proposed methodology outperformed existing algorithms in optimizing project durations and resources allocation. The main contribution of the proposed scheduler is its ability to incorporate uncertainty in scheduling process. Results proofed effectiveness and outperformance of the proposed solution. The genetic algorithm was tested on several projects from the existing databases and on one new project to the validity of the approach. The proposed algorithm out performed fairly well the results that exists from previous studies. One major contribution of this research is the incorporation of uncertainty to optimize project duration based on resource allocation.
URI: http://bspace.buid.ac.ae/handle/1234/1024
Appears in Collections:Thesis for Project Management

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