Supply Chain and Revenue Management for Online Retailing

This dissertation focuses on optimizing inventory and pricing decisions in the online retail industry. Motivated by the importance of great customer service quality in the online retail marketplace, we investigate service-level-constrained inventory control problems in both static and dynamic settings. The first essay studies multi-period production planning problems (with or without pricing options) under stochastic demand. A joint service-level constraint is enforced to restrict the joint probability of having backorders in any period. We use the Sample Average Approximation (SAA) approach to reformulate both chance-constrained models as mixed-integer linear programs (MILPs). Via computations of diverse instances, we demonstrate the effectiveness of the SAA approach, analyze the solution feasibility and objective bounds, and conduct sensitivity analysis. The approaches can be generalized to a wide variety of production planning problems. The second essay investigates the dynamic versions of the service-level-constrained inventory control problems, in which retailers have the flexibility to adjust their inventory policies in each period. We formulate two periodic-review stochastic inventory models (backlogging model and remanufacturing model) via Dynamic Programs (DP), and establish the optimality of generalized base-stock policies. We also propose 2-approximation algorithms for both models, which is computationally more efficient than the brute-force DP. The core concept developed in our algorithms is called the delayed marginal cost, which is proven effective in dealing with service-level-constrained inventory systems. The third essay is motivated by the exploding use of sales rank information in today's internet-based e-commerce marketplace. The sales rank affects consumers' shopping preference and therefore, is critical for retailers to utilize when making pricing decisions. We study periodic-review dynamic pricing problems in presence of sales rank, in which customers' demand is a function of both prices and sales rank. We propose rank-based pricing models and characterize the structure and monotonicity of optimal pricing policies. Our numerical experiments illustrate the potential of revenue increases when strategic cyclic policy is used.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144159/1/ycjiang_1.pd

Xi Sigma Pi, Gamma Chapter

Xi Sigma Pi is a national honor society for students of forestry. Once a person has been initiated into the fraternity, they are a lifelong member of the national organization. The Iowa State University chapter of Xi Sigma Pi is Alpha Gamma, and includes faculty, staff, graduate students, and undergraduates within forestry and other departments

Symmetry breaking Paradigm In Typical Laminar-Turbulence Transition System

A stationary cylindrical vessel containing a rotating plate near the bottle surface is partially filled with liquid. With the bottom rotating, the shape of the liquid surface would become polygon-like. This polygon vortex phenomenon is an ideal system to demonstrate the Laminar-Turbulent transition process. Within the framework of equilibrium statistical mechanics, a profound comparison with Landau's phase transition theory was applied in the symmetry-breaking aspect to derive the evolution equation of this system phenomenologically. A comparison between theoretical prediction and experimental data is carried out. We concluded a considerably highly matched result, while some exceptions are viewed as the natural result that the experiment breaks through the up-limit of using equilibrium mechanics as an effective theory, namely breaking through the Arnold Tongue. Some extremely complex Non-equilibrium approaches were desired to solve this problem thoroughly in the future. So our method could be viewed as a linear approximation of this theoretical framework.Comment: 7 pages, 2 figures. This is the first work of our academic career, and we dedicate it to our parents and all our loved one

Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations

Low rank matrix approximation is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns and provides concise representations for the data. Research on low rank approximation usually focus on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrix. Here we are given a $d \times n$ binary matrix $A$ and a small integer $k$. The goal is to find two binary matrices $U$ and $V$ of sizes $d \times k$ and $k \times n$ respectively, so that the Frobenius norm of $A - U V$ is minimized. There are two models of this problem, depending on the definition of the dot product of binary vectors: The $\mathrm{GF}(2)$ model and the Boolean semiring model. Unlike low rank approximation of real matrix which can be efficiently solved by Singular Value Decomposition, approximation of binary matrix is $NP$-hard even for $k=1$. In this paper, we consider the problem of Column Subset Selection (CSS), in which one low rank matrix must be formed by $k$ columns of the data matrix. We characterize the approximation ratio of CSS for binary matrices. For $GF(2)$ model, we show the approximation ratio of CSS is bounded by $\frac{k}{2}+1+\frac{k}{2(2^k-1)}$ and this bound is asymptotically tight. For Boolean model, it turns out that CSS is no longer sufficient to obtain a bound. We then develop a Generalized CSS (GCSS) procedure in which the columns of one low rank matrix are generated from Boolean formulas operating bitwise on columns of the data matrix. We show the approximation ratio of GCSS is bounded by $2^{k-1}+1$, and the exponential dependency on $k$ is inherent.Comment: 38 page

CLIPUNetr: Assisting Human-robot Interface for Uncalibrated Visual Servoing Control with CLIP-driven Referring Expression Segmentation

The classical human-robot interface in uncalibrated image-based visual servoing (UIBVS) relies on either human annotations or semantic segmentation with categorical labels. Both methods fail to match natural human communication and convey rich semantics in manipulation tasks as effectively as natural language expressions. In this paper, we tackle this problem by using referring expression segmentation, which is a prompt-based approach, to provide more in-depth information for robot perception. To generate high-quality segmentation predictions from referring expressions, we propose CLIPUNetr - a new CLIP-driven referring expression segmentation network. CLIPUNetr leverages CLIP's strong vision-language representations to segment regions from referring expressions, while utilizing its ``U-shaped'' encoder-decoder architecture to generate predictions with sharper boundaries and finer structures. Furthermore, we propose a new pipeline to integrate CLIPUNetr into UIBVS and apply it to control robots in real-world environments. In experiments, our method improves boundary and structure measurements by an average of 120% and can successfully assist real-world UIBVS control in an unstructured manipulation environment

Essays in Applied Microeconomics

This dissertation consists of three essays in applied microeconomics.The first chapter investigates the effect of coroner partisanship on COVID-19 death reporting. The politicization of the COVID-19 pandemic in the United States has raised questions about the integrity and accuracy of death reporting, particularly in jurisdictions with elected, partisan coroners. Using mortality data from the CDC and manually collected data on county-level death certification systems and coroner party affiliation where applicable, I examine the parallel systems of appointed medical examiners and elected coroners and analyze the effect of partisanship on reported COVID-19 deaths. Cross-sectional comparisons do not seem to suggest counties with coroners report fewer deaths than those with medical examiners, and difference-in-differences specifications reveal limited evidence of a statistically significant but not economically meaningful effect of partisanship on reported COVID death counts. The second chapter examines the effect of new information on lead water pipes on housing prices. In 2016, the Water and Sewer Authority of Washington, DC released an online map that contains information on lead service lines (LSLs) for all properties in the district. Using the release as a natural experiment, I estimate the effect of the new information on prices of properties with and without LSLs. Recent literature has found that housing lead reduction policies such as remediation mandates have significant price effects. In DC, while the map’s release was followed by a marked increase in requests for water lead tests, neither a difference-in-differences model nor a repeat sales model captures a significant divergence between housing prices of the two types of properties after the release, implying the housing market response to the information was limited. The second chapter considers the effect of the marriage tax subsidy on the marriage decision of same-sex couples. The U.S. Supreme Court’s ruling on United States v. Windsor in June 2013 compelled the federal government to recognize state-sanctioned same-sex marriages, including for tax purposes. The switch in the income tax filing status for same-sex couples meant that the marriage penalty or subsidy as a result of joint filing became a relevant factor that may enter couples’ marriage decisions. I construct a sample of married and cohabiting same-sex couples in 2012 and 2014 from public-use data of the American Community Survey. Using a difference-in-differences methodology, I do not find evidence that same-sex couples who would earn a higher marriage subsidy became more likely to marry after the Supreme Court ruling

Analysing retinal images using (extended) persistent homology

Biometrics are data used for the automatic verification and identification of individuals, two important routines commonly performed to enhance the level of security within a system. Therefore, improvements to the analysis of biometrics are crucial. Common examples of biometrics include fingerprints and facial features. In this thesis, we consider retinal fundus images, which are scans of a person's retina blood vessels at the back of the eyeballs. They have become a popular choice for these tasks due to their uniqueness and stability over time. Traditional methods mainly utilise specific biological features observed in the scans. These methods generally rely on highly accurate automated extractions of these traits, which are challenging to produce especially when abnormalities appear in diseased individuals. In this paper, we instead propose a novel approach, which is more tolerant of the errors from the feature extraction process, to analyse retina biometrics. In particular, we compute the \emph{(extended) persistent homology} of the blood vessel structure (viewed as a manifold with boundary embedded in $\R^2$) in a retinal image with respect to some filtration and produce a summary statistic called a \emph{persistence diagram}. This then allows us to perform further statistical tests. We test our method on a publicly available database using different filtrations choices to capture the vessels' shapes. Some of these choices achieve a high level of accuracy compared with tests done on the same database. Our method also takes significantly less time compared to other proposed methods. In the future, we can explore more filtrations and/or use combinations of results obtained from different filtrations to see if we can further increase accuracy

The Impact of COVID-19 Pandemic on Undergraduate Students’ Interest in the STEM Field

The deadly consequences of COVID-19 have been well documented, as have the social, emotional, and cognitive effects. These sequelae extend to the educational system. Much less investigated have been the potential positive outcomes of the pandemic. Given that STEM education relies heavily on hands-on laboratory experiences, STEM students may have been especially impacted by pandemic-imposed remote instruction. We surveyed 392 students at one liberal arts college querying why they continue studying in STEM or leave the STEM disciplines. Because the literature indicates that people of color and those from lower socioeconomic groups were more negatively affected by COVID-19, we hypothesized that students from traditionally marginalized groups in STEM would report greater adverse educational consequences of the pandemic as well; however, this was not borne out by the findings. Across demographic groups, students reported negative impacts of COVID-19, although in a few areas we found that more traditionally “privileged” groups complained of more negative outcomes than traditionally marginalized students did. What was most novel and dramatic in our results were the positive outcomes of the “lockdown” reported by students. These beneficial results were in the areas of enhanced resilience, improved social relationships, greater opportunities, academic improvement, and better mental health. Our paper concludes with recommendations for addressing the negative outcomes of COVID-19 and remote instruction, and for taking advantage of the unexpected positive effects

Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the domain of geoscience and remote sensing. However, advanced progresses on this topic are mainly focused on the designment of backbone networks or header networks, but surprisingly ignored the neck ones. In this letter, we first analyse the importance of the neck network in object detection frameworks from the theory of information bottleneck. Then, to alleviate the information loss problem in the current neck network, we propose a global semantic network, which acts as a bridge from the backbone to the head network in a bidirectional global convolution manner. Compared to the existing neck networks, our method has advantages of capturing rich detailed information and less computational costs. Moreover, we further propose a fusion refinement module, which is used for feature fusion with rich details from different scales. To demonstrate the effectiveness and efficiency of our method, experiments are carried out on two challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure