The capital budgeting process for a transportation system is usually complicated by the interdependencies of projects and the uncertainty of project costs. The existence of project interdependence in transportation systems makes it difficult to evaluate the project effects with analytical methods. Furthermore, when the construction costs of candidate projects are uncertain, the budget constraints that bind project selection become chance constraints, and this may render most existing approaches inapplicable. This paper formulates the project selection problem as a nonlinear integer optimization problem whose objective function is implicit but can be evaluated with network simulation. The Lagrangian method is applied to relax the complex project constraints that are nonlinear under cost uncertainty. An efficient genetic algorithm is developed to solve the Lagrangian subproblems. This paper applies an equilibrium traffic assignment model to evaluate the project impacts and the objective values of the Lagrangian subproblems. Experiments are designed to test the performance of the developed approach on a fairly generic highway system. The experiment results show that the developed approach can effectively solve the problem of selecting interdependent projects under cost uncertainty.
This paper formulates a bilevel compromise programming model for allocating resources between pavement and bridge deck maintenances. The first level of the model aims to solve the resource allocation problems for pavement management and bridge deck maintenance, without considering resource sharing between them. At the second level, the model uses the results from the first step as an input and generates the final solution to the resource-sharing problem. To solve the model, the paper applies genetic algorithms to search for the optimal solution. We use a combination of two digits to represent different maintenance types. Results of numerical examples show that the conditions of both pavements and bridge decks are improved significantly by applying compromise programming, rather than conventional methods. Resources are also utilized more efficiently when the proposed method is applied.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.