2026-09-19T14:42:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/688242026-06-06T01:03:48Zcom_1721.1_7582com_1721.1_7581com_1721.1_101402col_1721.1_131023col_1721.1_101610
Jarrod Goentzel.
Jagatheesan, Jay
Kilcullen, Ryan
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Engineering Systems Division
2012-01-30T16:52:22Z
2012-01-30T16:52:22Z
2011
2011
http://hdl.handle.net/1721.1/68824
772179692
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 78-79).
This thesis involves railcar fleet sizing strategies with a specific company in the chemical industry. We note that the identity of the company in this report has been disguised, and some portions of the fleets have been omitted to mask their actual sizes. However, all analysis in this thesis was conducted on actual data. In our research, we evaluate the appropriateness of both deterministic and stochastic fleet sizing models for this company. In addition, we propose an economic model that is adapted from a basic inventory management policy that can be applied to fleet sizing in order to arrive at a cost-driven solution. Through our research, we demonstrate that the fleet sizing strategy of this company can be improved by incorporating transit time variability into the fleet sizing model. Additionally, we show that fleet sizes can be reduced by accurately characterizing the distributions of the underlying transit and customer holding time data. Finally, we show the potential value of considering economic factors to arrive at a fleet sizing decision that balances the cost of over-capacity with the cost of an insufficient supply of railcars.
by Jay Jagatheesan and Ryan Kilcullen.
M.Eng. in Logistics
79 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Incorporating cycle time uncertainty to improve railcar fleet sizing
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a4945e24-1638-4b38-87dd-38e4feba1c8e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Incorporating cycle time uncertainty to improve railcar fleet sizing</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2011</PublicationDate>
<Authors>
<Author>
<DisplayName>Jagatheesan, Jay</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
<Author>
<DisplayName>Kilcullen, Ryan</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Engineering Systems Division.</Keyword>
<Abstract>This thesis involves railcar fleet sizing strategies with a specific company in the chemical industry. We note that the identity of the company in this report has been disguised, and some portions of the fleets have been omitted to mask their actual sizes. However, all analysis in this thesis was conducted on actual data. In our research, we evaluate the appropriateness of both deterministic and stochastic fleet sizing models for this company. In addition, we propose an economic model that is adapted from a basic inventory management policy that can be applied to fleet sizing in order to arrive at a cost-driven solution. Through our research, we demonstrate that the fleet sizing strategy of this company can be improved by incorporating transit time variability into the fleet sizing model. Additionally, we show that fleet sizes can be reduced by accurately characterizing the distributions of the underlying transit and customer holding time data. Finally, we show the potential value of considering economic factors to arrive at a fleet sizing decision that balances the cost of over-capacity with the cost of an insufficient supply of railcars.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>