Operational Research Commons^™

An Enhanced Adaptive Learning System Based On Microservice Architecture, Abdelsalam Helmy Ibrahim, Mohamed Eliemy, Aliaa Abdelhalim Youssif

Future Computing and Informatics Journal

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian …

Visual Question Answering: A Survey, Gehad Assem El-Naggar

Future Computing and Informatics Journal

Visual Question Answering (VQA) has been an emerging field in computer vision and natural language processing that aims to enable machines to understand the content of images and answer natural language questions about them. Recently, there has been increasing interest in integrating Semantic Web technologies into VQA systems to enhance their performance and scalability. In this context, knowledge graphs, which represent structured knowledge in the form of entities and their relationships, have shown great potential in providing rich semantic information for VQA. This paper provides an abstract overview of the state-of-the-art research on VQA using Semantic Web technologies, including knowledge …

Resources Based Planning Framework For Infrastructure Maintenance And Rehabilitation Projects, Heba Gad

Theses and Dissertations

Infrastructure maintenance and rehabilitation projects involve activities scattered over a large geographical area (e.g., scattered road segments maintenance, telecom towers maintenance program, etc.). Planning such projects require a resource-based approach that accounts for the implications of resource mobility between activities’ locations in terms of time & cost. Existing scheduling techniques fall short of addressing the unique challenges of the scattered nature of these projects in combination with organization's limited resources availability. To address this need, this research presents a resources-based planning framework for infrastructure maintenance and rehabilitation scattered projects with the objective of enhancing resources utilization achieving time and cost …

Autonomous Cyber Warfare Agents: Dynamic Reinforcement Learning For Defensive Cyber Operations, David A. Bierbrauer, Rob Schabinger, Caleb Carlin, Jonathan Mullin, John Pavlik, Nathaniel D. Bastian

ACI Journal Articles

In this work, we aim to develop novel cybersecurity playbooks by exploiting dynamic reinforcement learning (RL) methods to close holes in the attack surface left open by the traditional signature-based approach to Defensive Cyber Operations (DCO). A useful first proof-of-concept is provided by the problem of training a scanning defense agent using RL; as a first line of defense, it is important to protect sensitive networks from network mapping tools. To address this challenge, we developed a hierarchical, Monte Carlo-based RL framework for the training of an autonomous agent which detects and reports the presence of Nmap scans in near …

Improving Inventory For A Large Food Service Supplier, John Krolick, Chrsitian Ingersoll, Joseph Fuentes, Harmoni Blackstock, John Miller, Alexander Contarino

Mathematica Militaris

Golden State Foods (GSF) is an international food service supplier. Their Georgia manufacturing plant currently produces thousands of condiments for restaurants across the United States. This project analyzed the inventory policies of seasonal ingredients, in order to decrease GSF’s working capital and inventory holding costs. By inputting product recipes, ingredient usage, and weekly inventory data into a dynamic lot-sizing model, we predict optimal order quantities and reorder points for GSF’s seasonal and expensive ingredients. This will potentially decrease the facility’s $1.2 million holding cost and increase its long-term profits compared to the status quo.

Prioritizing Ports And Waterways Safety Assessments (Pawsas), Matalynn Clark, Peyton Phillips, Claire Portigue, Justin Canovas, Eric Johnson, Andrew Zuckerman

Mathematica Militaris

A Ports and Waterways Safety Assessment (PAWSA) is a discussion forum facilitated by the United States Coast Guard Navigation Center (NAVCEN) to analyze the state of a port or waterway as it relates to navigation, vessel traffic, and physical attributes of the waterway. A successful workshop requires the collaboration between various stakeholders such as waterway users, environmental interest groups and local law enforcement. Without involving the relevant parties and encouraging their insight, the USCG risks creating an incomplete understanding of the port’s dynamic.

This project examines the characteristics of eight ports across the U.S. to determine which is in most …

Constrained Optimization Based Adversarial Example Generation For Transfer Attacks In Network Intrusion Detection Systems, Marc Chale, Bruce Cox, Jeffery Weir, Nathaniel D. Bastian

ACI Journal Articles

Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious network packets without requiring feature engineering. Adversarial machine learning methods have been used to evade classifiers in the computer vision domain; however, existing methods do not translate well into the constrained cyber domain as they tend to produce non-functional network packets. This research views the payload of network packets as code with many functional units. A meta-heuristic based generative model is developed to maximize classification loss of packet payloads with respect to a surrogate model by repeatedly substituting units of code with functionally equivalent counterparts. The …

Data-Driven Platform And Digital Operations, Bing Bai

Olin Business School Electronic Theses and Dissertations

The objective of this dissertation is to study the emerging operations issues on data-driven platforms and digital operations. With the increasing availability of data and the development of information technologies, platforms process a large amount of data in order to efficiently make daily operational decisions. Understanding human behaviors and the human-algorithm connection is instrumental to the success of this process. In my research, I implement field experiments and use structural models to study in-warehouse worker behavior and out-of-warehouse customer behavior in the last mile of logistics.

In Chapter 1, “The Impacts of Algorithmic Work Assignment on Fairness Perceptions and Productivity: …

Optimization And Information Problems In Operations, Puping Jiang

Olin Business School Electronic Theses and Dissertations

The main purpose of this dissertation is to study the optimization problems and the value of information in various commercial settings, especially in the emerging platform economy.

Chapter 1, “Data-Driven Asset Selling”. Motivated by online asset selling marketplace business (e.g., used cars and real estate), we formulate a data-driven asset selling dynamic pricing framework which utilizes platforms’ access to customers’ online behavioral data. With mild assumptions on the demand model, careful characterization of the problem structure shows that the model admits some ideal properties that facilitate our regret analysis under our dynamic programming setting. Instead of studying the policy performance …

Optimizing Wedding Venue Selection Process Using Integer Programming, Luis Rodriguez

Theses/Capstones/Creative Projects

Choosing the right wedding venue can be extremely difficult for the unsuspecting engaged couple. There is a myriad of variables that must be taken into account prior to the illustrious wedding date; these variables include the option for a reception, the location, and food requirements, to name a few. Consequently, the typical couple seems to spend multiple months researching and visiting many wedding spaces. However, even though months go into planning, it still is not a guarantee that all variables are accounted for. Furthermore, without a wedding planner, these couples may second-guess their chosen site due to seemingly arduous issues …

A Comparison Of Nonverbal And Paraverbal Behaviors In Simulated And Virtual Patient Encounters, Sarah Powers, Mark W. Scerbo, Matthew Pacailler, Macy Kisiel, Baillie Hirst, Ginger S. Watson, Lauren Hamel, Fred Kron

Modeling, Simulation and Visualization Student Capstone Conference

The present study assessed whether trainees display similar nonverbal and paraverbal behaviors when interacting with a simulated (SP) and virtual patient (VP). Sixty second slices of time following four interactions were rated for the presence and frequency of three nonverbal and paraverbal behaviors. Results revealed that students exhibited fewer behaviors in the VP interaction, possibly due to differences social inhibition or fidelity between the two formats.

Optimization Of Acrylonitrile Butadiene Styrene Filament 3d Printing Process Parameters Based On Mechanical Test, Raja S, Rajeswari N

International Journal of Mechanical and Industrial Engineering

This research paper's main goal is to improve the printing parameters that can be used in the 3D Printing Material Extrusion production method in order to get the best printing parameters for Acrylonitrile Butadiene Styrene (ABS) filament with the tensile test in the shortest possible time. The printing parameters that can be employed on 3D printing material extrusion machines include the extruder temperature, layer height, printing speed, and shell count. Also, tensile specimens in accordance with the ASTM (American Society for Testing and Materials) D638 standard were created utilizing ABS filament and the aforementioned adjusted printing settings. The most effective …

Factors Influencing Users’ Attitudes Towards Using Brain Computer Interface (Bci) For Non Medical Uses: An Application Of The Technology Acceptance Model (Tam), Yichin Wu, Leila Halawi

National Training Aircraft Symposium (NTAS)

While brain-computer interfaces (BCI) are gaining popularity in assisting people with illnesses, there is also increased technical research on incorporating BCI into healthy people’s lives. So far, not much research has focused on user attitudes, although some research has pointed out privacy and trust issues. Understanding potential users’ attitudes, expectations, and concerns early in the technology development stage is crucial for the novelty's success. For this reason, this study aims to understand the general publics’ attitude towards BCI for nonmedical uses using the technology acceptance model (TAM). The study will offer insights into how external factors including technology optimism, familiarity, …

The Sensitivity Of A Laplacian Family Of Ranking Methods, Claire S. Chang

HMC Senior Theses

Ranking from pairwise comparisons is a particularly rich subset of ranking problems. In this work, we focus on a family of ranking methods for pairwise comparisons which encompasses the well-known Massey, Colley, and Markov methods. We will accomplish two objectives to deepen our understanding of this family. First, we will consider its network diffusion interpretation. Second, we will analyze its sensitivity by studying the "maximal upset" where the direction of an arc between the highest and lowest ranked alternatives is flipped. Through these analyses, we will build intuition to answer the question "What are the characteristics of robust ranking methods?" …

A Holistic Work System Approach To Creating Flow During Transactional Work, Steven Clapp

Electronic Theses and Dissertations, 2020-

Psychological flow is a positive mental state where one is so fully concentrated in a challenging task that self-consciousness falls away, time seems to stand still, and the reward is the experience of meeting the challenge. Previous research on flow in the workplace has been performed on how to create conditions to promote its occurrence in workers, to describe its attendant individual and organizational benefits, and to measure it through self-reported means and physiologically. Such research has been focused on creative endeavors (such as the arts, sports, medicine, teaching), where individuals have high agency over the execution of activities needed …

Short-Term Prediction Of Icu Admission For Covid-19 Inpatients, Yoon Sang Lee, Riyaz T. Sikora

Journal of International Technology and Information Management

Since the COVID-19 outbreak, many hospitals suffered from a surge of some high-risk inpatients needing to be admitted to the ICU. In this study, we propose a method

predicting the likelihood of COVID-19 inpatients’ admission to the ICU within a time frame of 12 hours. Four steps, the Bayesian Ridge Regression-based missing value imputation, the synthesis of training samples by the combination of two rows (the first and another row) of each patient, customized oversampling, and XGBoost classifier, are used for the proposed method. In the experiment, the AUC-ROC and F-score of our method is compared with those of other …

The Need For International Ai Activities Monitoring, Parviz Partow-Navid, Ludwig Slusky

Journal of International Technology and Information Management

This paper focuses primarily on the need to monitor the risks arising from the dual-use of Artificial Intelligence (AI). Dual-use AI technology capability makes it applicable for defense systems and consequently may pose significant security risks, both intentional and unintentional, with the national and international scope of effects. While domestic use of AI remains the prerogative of individual countries, the unregulated and nonmonitored use of AI with international implications presents a specific concern. An international organization tasked with monitoring potential threats of AI activities could help defuse AI-associated risks and promote global cooperation in developing and deploying AI technology. The …

Mitigating Supply Chain Disruptions In Retail Discount Department Stores, Anthony T. Patton

Walden Dissertations and Doctoral Studies

Supply chain disruptions can have adverse effects on business outcomes. Retail industry supply chain leaders are concerned with supply chain disruptions because supply chain disruptions can lead to dissatisfied customers and loss of profits. Grounded in game theory, the purpose of this multiple case study was to explore strategies four retail industry supply chain leaders in Northern Illinois and Northwest Indiana implemented to mitigate the effects of supply chain disruptions. Data were collected using semistructured virtual interviews with four retail industry supply chain leaders and a review of company documents. Through thematic analysis, four themes emerged: (a) choosing appropriate inventory …

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

ACI Journal Articles

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …