Show simple item record

dc.contributor.advisorBanerjee, Amarnath
dc.creatorDasgupta, Sumantra
dc.date.accessioned2010-01-15T00:12:27Z
dc.date.accessioned2010-01-16T00:54:33Z
dc.date.available2010-01-15T00:12:27Z
dc.date.available2010-01-16T00:54:33Z
dc.date.created2008-05
dc.date.issued2009-05-15
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-2755
dc.description.abstractThe present research formulates the path planning as an optimization problem with multiple objectives and stochastic edge parameters. The first section introduces different variants of the PP problem and discusses existing solutions to the problem. The next section introduces and solves various versions of the PP model within the scope of this research. The first three versions describe a single entity traveling from a single source to a single destination node. In the first version, the entity has a single objective and abides by multiple constraints. The second version deals with an entity traveling with multiple objectives and multiple constraints. The third version is a modification of the second version where the actual probability distributions of travel times along edges are known. The fourth and final version deals with multiple heterogeneous entities routed from multiple sources (supply nodes) to multiple destinations (demand nodes) along capacitated edges. Each of these formulations is solved by using either exact algorithms or heuristics developed in this research. The performance of each algorithm/heuristic is discussed in the final section. The main contributions of this research are: 1. Provide a framework for analyzing PP in presence of multiple objectives and stochastic edge parameters. 2. Identify candidate constraints where clustering based multi-level programming can be applied to eliminate infeasible edges. 3. Provide an exact O (V.E) algorithm for building redundant shortest paths. 4. Provide an O (V.E+C2) heuristic for generating Pareto optimal shortest paths in presence of multiple objectives where C is the upper bound for path length. The complexity can be further reduced to O (V.E) by using graphical read-out of the Pareto frontier. 5. Provide a cost structure which can capture multiple key probability distribution parameters of edge variables. This is in contrast with usual techniques which just capture single parameters like the mean or the variance of distributions. 6. Provide a MIP formulation to a multi-commodity transportation problem with multiple decision variables, stochastic demands and uncertain edge/route capacities. 7. Provide an alternate formulation to the classic binary facility selection problem.en
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectMultiobjectiveen
dc.subjectStochasticen
dc.titleMulti-objective stochastic path planningen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentIndustrial and Systems Engineeringen
thesis.degree.disciplineIndustrial Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberCurry, Guy
dc.contributor.committeeMemberLiang, Faming
dc.contributor.committeeMemberNtaimo, Lewis
dc.type.genreElectronic Thesisen
dc.type.materialtexten
dc.format.digitalOriginborn digitalen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record