Processing of Constraints in Transportation Network Design Problem

Abstract

In the discrete transportation network design problem it is often the case that some projects are mutually exclusive projects: they cannot be selected simultaneously. Some projects are concurrent projects: they have to be selected together. Or, one project cannot be selected unless another one is also selected. All of these are called constraints. At the end of the design process, the final solutions must be the best feasible solutions, which satisfy all constraints. So, this paper presents a method for doing constraint processing within a cumulative genetic algorithm. This paper first briefly introduces the method of using the cumulative genetic algorithm and a neural network to design transportation networks. Then, some examples are given to show how constraints are proposed and how they reflect restrictions encountered in practice. Next, an analysis is made of alternative constraint processing methods and the optimization process is modified within the cumulative genetic algorithm. Furthermore, two groups of constraints for a test network are defined to test the method. It is found that the results from this search process are indeed the best feasible solutions. This study shows that the cumulative genetic algorithm, which employs a neural network, works very well in processing the constraints.