Operations Research, Systems Engineering and Industrial Engineering Commons^™

Smart Warehousing Implementaion And Education Of Supply Chain Management Leaders, David Quintanilla

Doctoral Dissertations

Paper #1 Overview

Businesses have to adapt to new challenges and technologies in the marketplace which influence warehousing. In order to support this growth, Industry 4.0 technologies have been implemented along the value chain to optimize their organizations and production processes; however, there are still gaps for warehousing research for Industry 4.0. We present four pillars¾location strategy, infrastructure/design, data management, and advanced planning and control¾ as a framework for businesses to use for their adaptation into smart warehousing. In particular, this framework will guide companies in their logistics journey into Industry 4.0. Industry experts and senior logistics professionals were interviewed …

Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang

Doctoral Dissertations

This study aims to investigate the spatiotemporal dynamic of global wildfires, their underlying climate-driving mechanisms, and their predictability by utilizing multiple data sources (both process-based model simulations and satellite-based observations) and multiple analytical methods including machine learning techniques (MLTs).

We first explored the global wildfire interannual variability (IAV) and its climate sensitivity across nine biomes from 1997 to 2018, leveraging the state-of-art U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) land component (ELM-v1) simulations with six sets of climate forcings. Results indicate that 1) ELM simulations could reproduce the IAV of wildfire in terms of magnitudes, distribution, bio-regional …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …