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Title page for ETD etd-05182004-014827


Type of Document Master's Thesis
Author Bansal, Amogh ,
Author's Email Address amogh_bansal@yahoo.com
URN etd-05182004-014827
Title Design of a Public Logistics Network.
Degree Master of Science
Graduate Program Operations Research
Advisory Committee
Advisor Name Title
Dr. Michael G. Kay Committee Chair
Dr. Negash Medhin Committee Member
Dr. Russell E. King Committee Member
Keywords
  • Public Logistics Network
  • GA
Date of Defense 2004-05-13
Availability unrestricted
Abstract
BANSAL, AMOGH. Design of a Public Logistics Network. (Under the direction of Dr. Michael G. Kay.)

This thesis presents a design for a public logistics network (PLN) covering the continental United States. A systematic approach is developed to determine the initial national road network using only the Interstate highways and part of the U.S. highways. Various heuristics are developed for generating the underlying road network. In two of the heuristics, every Interstate node is used and then U.S. highway nodes are added to the road network to supplement the Interstate nodes. Another heuristic generates the network by directly joining the roads (based on shortest time routes) between the cities of more than certain population. The results from testing show that the later heuristic performs better than the former ones. This road network is then used for developing the PLN by selecting some of its nodes as the locations for distribution centers (DCs). The PLN is then developed by removing and adding arcs in a reduced network obtained by Delaunay triangulation of the selected DC nodes in the underlying road network. Given the number and location of DCs on this underlying network, the minimum average transport time for a package is used as the criterion to compare alternative PLN designs. The package demand used to determine the minimum average transport time is proportional to the population at each five-digit zip code centroid surrounding each DC. Effects of different parameters on the design of the PLN are studied. Finally, a genetic algorithm is used to get the optimal public logistics network for the entire U.S.

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