Genetically optimized infrastructure design strategies in degradable transport networks

Dimitriou, L; Tsekeris, T; Stathopoulos, A

dc.contributor.author	Dimitriou, L	en
dc.contributor.author	Tsekeris, T	en
dc.contributor.author	Stathopoulos, A	en
dc.date.accessioned	2014-03-01T01:57:09Z
dc.date.available	2014-03-01T01:57:09Z
dc.date.issued	2008	en
dc.identifier.issn	1860949X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28361
dc.subject	Degradable transport networks	en
dc.subject	Discrete and continuous system structures	en
dc.subject	Game theory	en
dc.subject	Genetic algorithm	en
dc.subject	Latin hypercube sampling	en
dc.subject	Stochastic optimum network design	en
dc.subject	Systems reliability	en
dc.title	Genetically optimized infrastructure design strategies in degradable transport networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-540-69390-1_2	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-69390-1_2	en
heal.publicationDate	2008	en
heal.abstract	This chapter examines the problem of the resource allocation in degradable road transport networks within a stochastic evolutionary optimization framework. This framework expresses the stochastic equilibrium Network Design Problem (NDP) as a game-theoretic, combinatorial bi-level program. Both the discrete and continuous versions of the reliable NDP are considered in order to address different strategies of network infrastructure investment. The estimation procedure employs a Latin Hypercube sampling method for simulating degradation-inducing variations in users' attributes and system characteristics, and hence evaluates the network travel time reliability which constrains the solution. This simulation-based risk assessment technique is combined with a genetic algorithm to handle the complex, non-convex nature of the NDP adequately. The test implementation of the proposed framework demonstrates the significant role of incorporating the stochasticity and reliability requirements in the design process to facilitate the selection of the optimal investment strategies in degradable road networks. © 2008 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Studies in Computational Intelligence	en
dc.identifier.doi	10.1007/978-3-540-69390-1_2	en
dc.identifier.volume	144	en
dc.identifier.spage	23	en
dc.identifier.epage	43	en