Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
| Hi-index | 0.00 |
This paper deals with the problems of optimal capacity and pricing decisions in private road networks. These problems are described as a class of design and pricing Stackelberg games and formulated as nonconvex, bilevel nonlinear programs. Such games capture interactions among the decisions of system designer/operator, government regulations and reactions of multi-class users on optimal toll-capacity combinations. The present class of games applies to a realistic urban highway with untolled alternative arterial links. In contrast with the mostly used continuous representations, the highway capacity is more intuitively expressed as a discrete variable, which further complicates the solution procedure. Hence, an evolutionary computing approach is employed to provide a stochastic global search of the optimal toll and capacity choices. The results offer valuable insights into how investment and pricing strategies can be deployed in regulated private road networks.