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dc.contributor.authorLucic, Pantaen
dc.date.accessioned2014-03-14T20:08:05Zen
dc.date.available2014-03-14T20:08:05Zen
dc.date.issued2002-02-06en
dc.identifier.otheretd-03092002-231724en
dc.identifier.urihttp://hdl.handle.net/10919/26396en
dc.description.abstractMany real-world problems could be formulated in a way to fit the necessary form for discrete optimization. Discrete optimization problems can be solved by numerous different techniques that have developed over time. Some of the techniques provide optimal solution(s) to the problem and some of them give â good enoughâ solution(s). The fundamental reason for developing techniques capable of producing solutions that are not necessarily optimal is the fact that many discrete optimization problems are NP-complete. Metaheuristic algorithms are a common name for a set of general-purpose techniques developed to provide solution(s) to the problems associated with discrete optimization. Mostly the techniques are based on natural metaphors. Discrete optimization could be applied to countless problems in transportation engineering. Recently, researchers started studying the behavior of social insects (ants) in an attempt to use the swarm intelligence concept to develop artificial systems with the ability to search a problemâ s solution space in a way that is similar to the foraging search by a colony of social insects. The development of artificial systems does not entail the complete imitation of natural systems, but explores them in search of ideas for modeling. This research is partially devoted to the development of a new system based on the foraging behavior of bee colonies â Bee System. The Bee System was tested through many instances of the Traveling Salesman Problem. Many transportation-engineering problems, besides being of combinatorial nature, are characterized by uncertainty. In order to address these problems, the second part of the research is devoted to development of the algorithms that combine the existing results in the area of swarm intelligence (The Ant System) and approximate reasoning. The proposed approach â Fuzzy Ant System is tested on the following two examples: Stochastic Vehicle Routing Problem and Schedule Synchronization in Public Transit.en
dc.publisherVirginia Techen
dc.relation.haspartPanta_Lucic_ED.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectFuzzy Sets Theoryen
dc.subjectTransportation Engineeringen
dc.subjectSwarm Intelligenceen
dc.titleModeling Transportation Problems Using Concepts of Swarm Intelligence and Soft Computingen
dc.typeDissertationen
dc.contributor.departmentCivil Engineeringen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineCivil Engineeringen
dc.contributor.committeechairTeodorovic, Dusanen
dc.contributor.committeememberTrani, Antoino A.en
dc.contributor.committeememberVanLandingham, Hugh F.en
dc.contributor.committeememberSchonfeld, Paulen
dc.contributor.committeememberTriantis, Konstantinos P.en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-03092002-231724/en
dc.date.sdate2002-03-09en
dc.date.rdate2003-03-26en
dc.date.adate2002-03-26en


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