2026-09-21T13:10:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1504632026-06-05T15:42:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Georgia Perakis and Saurabh Amin.
McCleneghan, Megan Rose,
author.
Sloan School of Management,
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Operations Research Center
Massachusetts Institute of Technology. Department of Mechanical Engineering
2023-04-07T16:54:35Z
2023-04-07T16:54:35Z
2019
2019
https://hdl.handle.net/1721.1/150463
1373629276
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2019
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis. "The pagination in this thesis reflects how it was delivered to the Institute Archives and Special Collections. The Table of Contents does not accurately represent the page numbering"--Disclaimer page.
Includes bibliographical references (page 83).
Following an unprecedented wildfire season in 2017, management of Sierra Gas & Electric (SG&E), an undisclosed utility company, issued a new standard directing that all new and replacement electric transmission line (T-Line) poles be made from steel wherever possible to mitigate liability. This new standard necessitates that some inventory be held locally in anticipation of emergencies and quality issues as steel poles have significantly longer lead times than wood. The variability of poles makes ordering for an emergency inventory difficult, as steel poles come in more than 60 common strength/length combinations. This thesis focuses on assessing the risk wildfire poses to SG&E's wood T-Line poles, and simulating an estimated yearly demand to determine order quantities that optimize pole replacement preparedness. In general, this work presents a two-stage process for determining necessary inventory levels for non-perishable products when the products needed change with the location of an event. A Markov Chain Monte Carlo simulation was developed using empirical sampling of prior fire data over 2,000 iterations to create simulated wildfires throughout the state of California. Combining this with geospatial analysis allowed for modeling of approximate distributions of SG&E poles in the footprints of fires. Given the probabilistic demand for poles of different types, the two-stage process was defined as before an emergency has occurred and after, once the location of a fire is known. Optimization problems were set up based on both aggregate and location specific data to inform the service levels used for ordering poles at each stage. This model offers realistic insight into how the varied nature of SG&E's pole infrastructure across the state effects ordering decisions, as well as how the company can leverage its extensive geospatial data and forecasting abilities to make ordering decisions.
Megan Rose McCleneghan.
S.M. in Management Research
S.M.
S.M. in Management Research Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
128 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management,
Mechanical Engineering.
A multi-stage stochastic ordering method for wildfire preparedness and response
Thesis
application/pdf
2023-04-07T16:54:35Z
Publication
Master
Sloan
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b9505da5-b466-45c0-ac36-c4d5a64c4de5">
<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>A multi-stage stochastic ordering method for wildfire preparedness and response</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2019</PublicationDate>
<Authors>
<Author>
<DisplayName>McCleneghan, Megan Rose,
author.</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>Sloan School of Management,</Keyword>
<Keyword>Mechanical Engineering.</Keyword>
<Abstract>Following an unprecedented wildfire season in 2017, management of Sierra Gas & Electric (SG&E), an undisclosed utility company, issued a new standard directing that all new and replacement electric transmission line (T-Line) poles be made from steel wherever possible to mitigate liability. This new standard necessitates that some inventory be held locally in anticipation of emergencies and quality issues as steel poles have significantly longer lead times than wood. The variability of poles makes ordering for an emergency inventory difficult, as steel poles come in more than 60 common strength/length combinations. This thesis focuses on assessing the risk wildfire poses to SG&E's wood T-Line poles, and simulating an estimated yearly demand to determine order quantities that optimize pole replacement preparedness. In general, this work presents a two-stage process for determining necessary inventory levels for non-perishable products when the products needed change with the location of an event. A Markov Chain Monte Carlo simulation was developed using empirical sampling of prior fire data over 2,000 iterations to create simulated wildfires throughout the state of California. Combining this with geospatial analysis allowed for modeling of approximate distributions of SG&E poles in the footprints of fires. Given the probabilistic demand for poles of different types, the two-stage process was defined as before an emergency has occurred and after, once the location of a fire is known. Optimization problems were set up based on both aggregate and location specific data to inform the service levels used for ordering poles at each stage. This model offers realistic insight into how the varied nature of SG&E's pole infrastructure across the state effects ordering decisions, as well as how the company can leverage its extensive geospatial data and forecasting abilities to make ordering decisions.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>