A predictive analysis of the Department of Defense distribution system utilizing random forests
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Authors
Coleman, Amber G.
Subjects
Department of Defense (DOD) distribution
linear regression
regression trees
random forests
machine learning
ocean shipments
predictive models
United States Transportation Command (USTRANSCOM)
Marine Corps Logistics Command (MARCORLOGCOM)
linear regression
regression trees
random forests
machine learning
ocean shipments
predictive models
United States Transportation Command (USTRANSCOM)
Marine Corps Logistics Command (MARCORLOGCOM)
Advisors
Buttrey, Samuel E.
Date of Issue
2016-06
Date
16-Jun
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
This thesis develops machine-learning models capable of predicting Department of Defense distribution system performance of United States Marine Corps ocean requisitions to the United States Pacific Command area of operations. We use historical data to develop a model for each sub-segment of the Transporter leg within the distribution pipeline and develop two different models to predict the ocean transit sub-segment based on Hawaii and non-Hawaii destinations. We develop a linear regression, regression tree and random forest model for each sub-segment and find that the weekday and month in which requisitions begin the Transporter segment are among the most significant drivers in variability. United States Transportation Command currently uses the average performance per sub-segment to estimate Transporter length, and our models, when applied to the test set, perform considerably better than the average. We conclude that the random forest models provide the best and most robust results for most sub-segments. However, we encounter several issues concerning missing values within our dataset, which we suspect artificially inflate the significance of some of our predictor variables. We recommend refining data collection processes in order to collect observations that are more accurate and applying the same methodologies in the future.
Type
Thesis
Description
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.