Stochastic time–cost optimization using non-dominated archiving ant colony approach

This article employs a non-dominated archiving ant colony approach to solve the stochastic time–cost trade-off optimization problem. The model searches for non-dominated solutions considering total duration and total cost of the project as two objectives. In order to expect more realistic outcomes for the time–cost trade-off problem, uncertainties in time and cost of the project should be taken into account. Fuzzy sets theory is used to answer for uncertainties in time and cost of the project. The model embeds the α-cut approach to account for accepted risk level of the project manager. Left and right dominance ranking method is used for finding non-dominated solutions. The ranking method employs decision maker's optimism using β concept. The performance of the model is tested according to performance metrics for multi-objective evolutionary algorithms proposed in the literature. The results show that the algorithm is adequately reliable. A case study is solved to show the application of the proposed model for the uncertain time–cost trade-off problem.

► In this paper, a multi-objective optimization of time and cost of the project is considered.
► A non-dominated sorting version of ACO (NAACO) is applied to find Pareto-optimal solutions of the problem.
► Efficiency of the proposed NAACO is tested and verified.
► This paper considers time-cost optimization in an uncertain environment.
► Fuzzy mathematics is fully applied to address non-statistical uncertainties involved in the problem.