Genetic algorithm for project time-cost optimization in fuzzy environment
<p class="Abstract"><strong><em>Purpose:</em></strong> 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.</p> <p class="Abstract&qu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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OmniaScience
2012-12-01
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| Series: | Journal of Industrial Engineering and Management |
| Subjects: | |
| Online Access: | http://www.jiem.org/index.php/jiem/article/view/410 |
| Summary: | <p class="Abstract"><strong><em>Purpose:</em></strong> 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.</p> <p class="Abstract"><strong><em>Design/methodology/approach:</em></strong> 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 a-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.</p> <p class="Abstract"><strong><em>Findings:</em></strong> The solution has been performed under different combinations of GA parameters and after result analysis optimum values of those parameters have been found for the best solution.</p> <p class="Abstract"><strong><em>Research limitations/implications:</em></strong> 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 algorithm is developed under some assumptions.</p> <p class="Abstract"><strong><em>Practical implications:</em></strong> The proposed model leads decision makers to choose the desired solution under different risk levels.</p> <p class="Abstract"><strong><em>Originality/value:</em></strong> 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.</p> |
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| ISSN: | 2013-8423 2013-0953 |