Demand Box Uncertainty Evaluation in Discrete and Continuous Transportation Network Design with Genetic and Ant Colony Algorithms

Uncertain demand is one of the most important sources of uncertainties in transportation networks. It is almost based on various sources such as difficulty in forecasting of socio economic variables and the model’s deficiency. This demand is one of the main operative elements of transportation network design problems. The aim of this paper is to evaluate the effect of demand variability in discrete and continuous transportation network design problem. Thus, the uncertainty of demand has been assumed to have a maximum and a minimum rate, and the level of uncertainty in demand is assumed to vary between these extreme values as in a box. In this paper two Meta heuristic methods; Ant Colony Optimization (ACO) and Genetic Algorithm (GA), has been used to solve the aforementioned problem. It has been shown in this paper that relative to designing a network with maximum demand, the proposed method has found demands with 4 to 10 percent longer travel time. This could considerable effect on results of the network design problem.