Genetic algorithm based multi-objective approach for fuzzy activity time-cost optimization with resources consideration

Abstract

The project managers’ major concern is to complete project cost effectively and timely. But, in real life, the project completion time and cost may vary significantly as an effect of uncertainty such as weather, political and social disturbances, inflation, site congestion, human factors, productivity level, etc. Minimization of project time requires maximum supply of resources. Moreover, any delay in project completion time adds extra cost. Since, the relationship between time and resources of the activity is no longer a monotonously decreasing or increasing curve, but rather the fuzzy nature. Therefore, it is needed to minimize both project time and cost under uncertainty. In this study, fuzzy set theory is applied to model the managers’ behavior in predicting time and resources of an activity and •-cut method, in fuzzy logic theory, is used as a measure of accepted risk level. Because of NP-hard nature of time-cost tradeoff problem, genetic algorithms are used as a searching mechanism to establish the Pareto optimal solutions under different risk levels incorporating multi-objective approach. In addition, the activities of project are crashed and penalty is induced for delay to make such problem realistic. The proposed model leads the managers to choose the optimal time-cost solutions under different risk levels as well as their associated degree of belief in a more flexible and realistic manner. Finally this proposed model is used to solve a real problem.