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Genetic algorithm for project time-cost optimization in fuzzy environment

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2012
DOI: 
http://dx.doi.org/10.3926/jiem.410

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Purpose: The aim of this research is to develop a more realistic approach to solve project time-cost optimization problem under uncertain conditions, with fuzzy time periods. Design/methodology/approach: Deterministic models for time-cost optimization are never efficient considering various uncertainty factors. To make such problems realistic, triangular fuzzy numbers and the concept of -cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, Genetic Algorithm (GA) has been used as a searching tool. Finally, Dev-C++ 4.9.9.2 has been used to code this solver. Findings: The solution has been performed under different combinations of GA parameters and after result analysis best values of those parameters have been found for the near optimal or sustainable solution. Research limitations/implications: For demonstration of the application of the developed algorithm, a project on new product (Pre-paid electric meter, a project under government finance) launching has been chosen as a real case. The formulation of the model of the problem is developed under some assumptions that have been mentioned in section 5. Practical implications: The proposed model leads decision makers to choose the desired solution under different risk levels. Originality/value: Reports reveal that project optimization problems have never been solved under multiple uncertainty conditions. Here, the function has been optimized using Genetic Algorithm search technique, with varied level of risks and fuzzy time periods.
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  • 2
364-381
  • English

REFERENCES

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