A predictive analysis of the Department of Defense distribution system utilizing random forests

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dc.contributor.advisorButtrey, Samuel E.
dc.contributor.authorColeman, Amber G.
dc.contributor.departmentOperations Research
dc.contributor.secondreaderAlt, Jonathan K.
dc.date16-Jun
dc.date.accessioned2016-08-02T19:34:41Z
dc.date.available2016-08-02T19:34:41Z
dc.date.issued2016-06
dc.description.abstractThis 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.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.serviceMajor, United States Marine Corpsen_US
dc.description.urihttp://archive.org/details/apredictivenalys1094549436
dc.identifier.urihttps://hdl.handle.net/10945/49436
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis 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.en_US
dc.subject.authorDepartment of Defense (DOD) distributionen_US
dc.subject.authorlinear regressionen_US
dc.subject.authorregression treesen_US
dc.subject.authorrandom forestsen_US
dc.subject.authormachine learningen_US
dc.subject.authorocean shipmentsen_US
dc.subject.authorpredictive modelsen_US
dc.subject.authorUnited States Transportation Command (USTRANSCOM)en_US
dc.subject.authorMarine Corps Logistics Command (MARCORLOGCOM)en_US
dc.titleA predictive analysis of the Department of Defense distribution system utilizing random forestsen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
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