A multi-stage stochastic ordering method for wildfire preparedness and response

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• McCleneghan, Megan Rose, author.

Advisor dc:contributor.advisor

• Georgia Perakis and Saurabh Amin.

• Sloan School of Management,
• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150463

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150463