Genetic algorithm for project time-cost optimization in fuzzy environment
Abstract: 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 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.
Findings: 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.
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 algorithm is developed under some assumptions.
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.
Author: Khan Md. Ariful Haque,
Md. Ahsan Akhtar Hasin
Journal Code: jptindustrigg120015
