Date of Graduation

7-2021

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering (MSIE)

Degree Level

Graduate

Department

Industrial Engineering

Advisor

Chase Rainwater

Committee Member

Xiao Liu

Second Committee Member

Khoa Luu

Keywords

Random Forest Regression Analysis, Polynomial Regression, data analysis, miles per stop, logistics growth, neural networks, industry data

Abstract

Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial Regression are promising for this problem. Additionally, data processing and cleansing is implemented on a model different than what is currently being used by the logistic provider. The results indicate that through the use of polynomial regression on pre-processed and cleaned data, a 75% improvement in performance can be achieve in comparison to the baseline established by the logistics provider.

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