Engineering Commons^™

Meal Duration: Implications For Restaurant Revenue Management, Dale F. Billings

UNLV Theses, Dissertations, Professional Papers, and Capstones

A comprehensive revenue management strategy addressing space, product, price and time, has been shown to increase profits within the hospitality sector. While the literature shows that the restaurant industry has frequently addressed space, product, and price when looking at financial strategy, the effect of the variable of time on revenue generation has not been adequately studied. Restaurants shy away from the practice of using time as a commodity, particularly regarding meal duration, due to fears of reducing customer satisfaction.

This project explores the use of time as a commodity in restaurant revenue management. In particular, it examines consumers’ feelings about …

Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian

Electronic Thesis and Dissertation Repository

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …

Mazak Laser Optimization, Maylon Ellington, Terrilian E. Agbor Oji, William N. Palm

Senior Design Project For Engineers

Starflex Fabrication is a manufacturing company that thrives to provide custom, precision fabricated parts, and assemblies to their customers.The Mazak laser is Starflex Fabrication’s primary cutting machine and is critical to the overall process for each order. Team Flex will look to optimize the laser’s process and improve overall shop throughput.

Space Optimization And Process Improvement, Harith Alshareef

Senior Design Project For Engineers

Siemens Energy warehouse located in Suwanee, GA runs out of space in the Precision Lab due to rapid growth, seasonal peaks, and poor space utilization. The objective of the project is to redesign the Precision Lab layout and improve the tool assessment process to increase efficiency, productivity, and profitability.

The first solution is to add shelves in the Precision lab as well as the Torque lab and reduce the honeycombing effect on the shelves, which results in a 120%-150% increase in the storage space. We propose adding Tablets that include barcode scanner features to reduce the time spent on the …

Process Improvement And Lift Design For The Installation Of A Metrology Machine Assembly, Dalt J. Lasell, James Mitchell O'Meara, Matthew Steensma

Industrial and Manufacturing Engineering

The objective is to work with Onto Innovations, a leading provider of semiconductor metrology and manufacturing solutions, to develop a system that safely installs a 250lb optics plate into the Atlas III: one of their metrology machines. Based in Milpitas, CA, Onto Innovations utilizes a variety of operations to create different products and assembles each product on site. The 250lb optics plates are WIP that are transferred between fixtures and the Atlas III using a combination of lifts and human workers. The WIP have long lead times, high tolerances, and large costs associated with their manufacturing process.

Onto Innovations identified …

Automation Of Data Analysis In Formula 1, Adam Joseph Mourad, Prescott Jeanne Delzell, Patrick Conner Mccabe

Industrial and Manufacturing Engineering

This paper explores economic solutions for Formula 1 racing companies who are interested in data visualization tools. The research was conducted on the current development of data gathering, data visualization, and data interpretation in Formula 1 racing. It was found that a large chunk of racing companies within the league needs an affordable, effective, and automated visualization tool for data interpretation. As data collection in Formula 1 arises, the need for faster and more powerful software increases. Racing companies profit off-brand exposure and the more a racing team wins, the more exposure they receive. The goal of the paper focuses …

A Game Modeling Of A Closed-Loop Supply Chain In A Water-Energy Nexus: Technology Advancement, Market Competition And Capacity Limit, Nabeel Hamoud

Theses and Dissertations

Water and energy are two scarce and concerning resources interconnected in the water-energy nexus. In the nexus, production of energy needs water, and production of water needs energy. For better management of these resources in the nexus, this research considers a supply chain that consists of water suppliers, power suppliers, and consumers of these commodities. In the chain, water suppliers purchase power from power suppliers, and power suppliers purchase water from water suppliers. Other consumers can also buy these resources at the water and power markets. Each firm tries to maximize its own profit. The suppliers of water and power …

A Systematic Approach For Automatically Answering General-Purpose Objective And Subjective Questions, Lok Prasad Acharya

Theses and Dissertations

In this era of information explosion, people generally rely on the Internet, and more precisely, the search engines to get answers to their questions. However, what a search engine can do is just retrieve documents. Given some keywords, it only returns the relevant ranked documents that contain the keywords. Although users often want a precise answer to a question, they are left to extract answers from the documents themselves. This is where Automatic Question Answering (AQA) systems come into play. An AQA system takes questions in natural language as input and searches related answers in the set of documents and …

Effects Of Resampled Data On Time Series Forecasting Accuracy, Jennifer Garland

Theses and Dissertations

This thesis will look at time series forecasting of the air pollutants in Beijing, China and the power consumption of an individual household located in Sceaux, France. The forecast will be taken from two classical methods, the Holt-Winters’ methods and the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The Holt-Winters’ method will be looked at from the additive, multiplicative and damped methods. The SARIMA model will be looked at as a uni-variate model. In this thesis, it will be shown that less complex algorithms, such as the Holt-Winters’ methods can process larger data sets without the need of resampling, and …

Improving Grapheme-To-Phoneme Translation With The Use Of Nalu Attention Mechanisms, Brian Sheldon Smith

Theses and Dissertations

In recent years, Natural Language Processing in the field of machine learning has seen some major improvements. Data scientists have shown that neural networks are capable of breaking down semantics of sentences, translating languages, and answering complex questions with fast recall. While impressive, these feats all hinge on having access to a massive amount of clean text, or data sets with almost perfect grammar and spelling. Without this, neural networks will usually fail to converge on a meaningful result. To partially this dependency, Grapheme-to-Phoneme conversion can be employed. This is the conversion of words from their spellings to a form …

Deep Learning Models For Visibility Forecasting, Luz Carolina Ortega Gomez

Theses and Dissertations

This dissertation addresses the task of visibility forecasting via deep learning models using data from weather stations. Visibility is one of the most critical weather impacts on transportation systems. Low visibility conditions can seriously impact safety and traffic operations, leading to adverse scenarios, causing accidents, and jeopardizing transportation systems. Accurate visibility forecasting plays a key role in decision-making and management of transportation systems. However, due to the complexity and variability of weather variables, visibility forecasting remains a highly challenging task and a matter of significant interest for transportation agencies nationwide. This dissertation explores the use of deep learning models for …

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …

Simulation And Optimization Of A Multi-Agent System On Physical Internet Enabled Interconnected Urban Logistics., Long Zheng

Electronic Theses and Dissertations

An urban logistics system is composed of multiple agents, e.g., shippers, carriers, and distribution centers, etc., and multi-modal networks. The structure of Physical Internet (PI) transportation network is different from current logistics practices, and simulation can effectively model a series of PI-approach scenarios. In addition to the baseline model, three more scenarios are enacted based on different characteristics: shared trucks, shared hubs, and shared flows with other less-than-truckload shipments passing through the urban area. Five performance measures, i.e., truck distance per container, mean truck time per container, lead time, CO₂ emissions, and transport mean fill rate, are included in …

Optimizing Block-Stacking Operations With Relocation, Hueon Lee

Graduate Theses and Dissertations

The focus of the dissertation is developing the optimization problem of finding the minimum-cost operational plan of block stacking with relocation as well as devising a solution procedure to solve practical-sized instances of the problem. Assuming changeable row depth instead of permanent row depth, this research is distinguished from conventional block stacking studies.

The first contribution of the dissertation is the development of the optimization problem under the assumption of deterministic demand. The problem is modeled using integer programming as a variation of the unsplittable multi-commodity flow problem. To find a good feasible solution of practical-sized instances in reasonable time, …

Resource Allocation And Task Scheduling Optimization In Cloud-Based Content Delivery Networks With Edge Computing, Yang Peng

Operations Research and Engineering Management Theses and Dissertations

The extensive growth in adoption of mobile devices pushes global Internet protocol (IP) traffic to grow and content delivery network (CDN) will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. In this praxis, Interconnected Cache Edge (ICE) based on different public cloud infrastructures with multiple edge computing sites is considered to help CDN service providers (SPs) to maximize their operational profit. The problem of resource allocation and performance optimization is studied in order to maximize the cache hit ratio with available CDN capacity.

The considered problem is formulated as a multi-stage stochastic linear …

Evaluating The Resiliency Of Industrial Internet Of Things Process Control Using Protocol Agnostic Attacks, Hector L. Roldan

Theses and Dissertations

Improving and defending our nation's critical infrastructure has been a challenge for quite some time. A malfunctioning or stoppage of any one of these systems could result in hazardous conditions on its supporting populace leading to widespread damage, injury, and even death. The protection of such systems has been mandated by the Office of the President of the United States of America in Presidential Policy Directive Order 21. Current research now focuses on securing and improving the management and efficiency of Industrial Control Systems (ICS). IIoT promises a solution in enhancement of efficiency in ICS. However, the presence of IIoT …

Contributions Of Role-Playing Games: Advantages Of Incorporating Social Media In Disaster Response, Erin Mullin

Graduate Theses and Dissertations

After a disaster, emergency managers need to know who needs help, what type of help they need, and how soon they need it. Traditionally, they have relied on 911 calls and ground assessments to collect this information. Because the needs of a population are not able to be identified in a timely manner by ground assessments and because individuals are often unable to get through to 911, many civilians in distress turn to social media outlets as a last-ditch effort to obtain the services they need. Due to the uncertainty concerning the accuracy of social media posts, responding to disaster …

Assessing The Repeatability Of Clinical Tests In People With And Without Flexion-Induced Neck Pain, Ashton Human

Graduate Theses and Dissertations

The accessibility of mobile technology has improved productivity but can contribute to musculoskeletal disorders due to the posture associated with using such devices. Neck flexion, a posture assumed by smartphone users, has been shown as a risk factor for the development of clinical neck pain if sustained for prolonged periods. Determining the individuals who are naturally more predisposed to develop flexion-induced neck pain can aid in the prevention and treatment of musculoskeletal disorders. The purpose of this thesis is to assess the repeatability of clinical tests between trials over the course of two days in young adults who do and …

Machine Learning Applications To Predict Road Crash And Soccer Game Outcomes, Lu Bai

Master's Theses

Machine learning has become a cutting-edge and widely studied data science field of study in recent years across many industries and disciplines. In this thesis, two problems (1- crash severity prediction, 2- soccer game outcome prediction.) were investigated by using a set of machine learning approaches, namely: Ridge regression, Lasso Regression, Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF).

The first study is focused on investigating the critical factors affecting crash severity on a comprehensive time-series state-wide traffic crash data. The dataset covers crashes occurred in the state of Connecticut between 1995 and 2014. Traffic crashes are an …

A Data-Driven Predictive Model Of Reliability Estimation Using State-Space Stochastic Degradation Model, Farhad Balali

Theses and Dissertations

The concept of the Industrial Internet of Things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to conduct a reliability …

Dynamic Vehicular Trajectory Optimization For Bottleneck Mitigation And Safety Improvement, Wenqing Chen

Theses and Dissertations

Traffic bottleneck is defined as a disruption of traffic flow through a freeway or an arterial, which can be divided as two categories: stationary bottleneck and moving bottleneck. The stationary bottleneck is mainly formed by the lane drops in the multi-lane roadways, while the moving bottleneck are due to the very slowing moving vehicles which disrupt the traffic flow. Traffic bottlenecks not only impact the mobility, but also potentially cause safety issues.

Traditional strategies for eliminating bottlenecks mainly focus on expanding supply including road widening, green interval lengthening and optimization of intersection channelization. In addition, a few macroscopic methods are …

Aisle Design For Order Picking Operations With Unit-Load Replenishment., Dominic Ian Sleigh

Electronic Theses and Dissertations

The warehouse design problem has been addressed in the last decade with fewer constraints than ever before. Between optimization for unit load and order picking warehouses, the former has had extensively more research performed. Unit load optimization efforts have promised labor savings of 20-22%. Of the existing research for order-picking warehouses, only small improvements of less than 4% have been claimed. We propose the design optimization for order-picking warehouses requires consideration for inherently incurred unit load operations from pallet inbound activities, as well as forward-pick area replenishment. We perform experiments comparing optimization of aisle design using order picking data with …

Effects Of Musical Ability On Flight Planning, Situational Awareness, And Flight Path Deviations, Andrew P. Henry

Doctoral Dissertations and Master's Theses

Numerous studies have been conducted on music education and the benefits that learning an instrument has on the brain. However, there is little research that connects a pilot’s ability to play an instrument to a pilot’s ability to fly an airplane. When learning an instrument, students learn non-musical abilities, such as executive functions, which may correspond with the skills necessary to be a good pilot. The purpose of this study was to find a relationship between learning a musical instrument and pilot performance, specifically related to flight planning, situational awareness, and flight path deviations. This study was a quasi-experimental design …

Uncertainty And Error In Combat Modeling, Simulation, And Analysis, Jason A. Blake

Theses and Dissertations

Due to the infrequent and competitive nature of combat, several challenges present themselves when developing a predictive simulation. First, there is limited data with which to validate such analysis tools. Secondly, there are many aspects of combat modeling that are highly uncertain and not knowable. This research develops a comprehensive set of techniques for the treatment of uncertainty and error in combat modeling and simulation analysis. First, Evidence Theory is demonstrated as a framework for representing epistemic uncertainty in combat modeling output. Next, a novel method for sensitivity analysis of uncertainty in Evidence Theory is developed. This sensitivity analysis method …

A Case Study: The First Coastal Nuclear Decommissioning Project In California, Willie Aaron Quiros

Construction Management

San Onofre Nuclear Generating System (SONGS) is one of two nuclear power plants in California. Since the shut down in 2012, there is only one actively remaining, Diablo Canyon Nuclear Power Plant, which is set to shut down in 2024. This paper will examine the decommissioning of SONGS thus far; the first coastal nuclear decommissioning project in California’s stringent permitting process. This project was awarded as a joint venture to AECOM and Energy Solutions, both having experiencing in the field of nuclear decommissioning. This paper will outline what nuclear decommissioning challenges have been in the past; general steps of decommissioning …

Resource-Constrained Project Scheduling With Autonomous Learning Effects, Jordan M. Ticktin

Master's Theses

It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. This thesis considers the idea that learning effects could allow selected activities to be completed within reduced time, if they're scheduled after activities where workers learn relevant skills. This paper computationally explores the effect of this autonomous, intra-project learning on optimal makespan and problem difficulty. A learning extension is proposed to the standard RCPSP scheduling problem. Multiple parameters are considered, including project size, learning frequency, and learning intensity. A test instance generator is developed to adapt the popular PSPLIB library of scheduling …

Rfid Item-Level Tagging In A Grocery Store Environment, Brian Truman

LSU Master's Theses

The purpose of this research was to investigate how effective item-level Radio Frequency Identification (RFID) tagging would be using current RFID technology as a replacement for barcodes in a supermarket/grocery store environment.

To accomplish this, an experiment was be performed that utilized commercially available RFID technology. Passive Ultra High Frequency (UHF) RFID Tags were affixed to various grocery store items of different material categories (Food, Metal, Plastic, Liquid, and Glass), and placed in a metal shopping cart. Eight (8) antenna arrangements were created, comprised of different combinations of four (4) antennas in different locations around the cart.

The experiment was …

Energy Supply Chain Of Co-Firing System To Generate Electricity, Abinash Panda

Major Papers

The problem of air pollution on the streets of the national capital of India, New Delhi is a major concern. Majority of the pollutants are generally originated from either the vehicles on the roads or the burning of crops from the neighbouring states of the National Capital Region (NCR). To improve the condition of the air quality by the vehicles, implementation of the Electric Vehicles (EVs) in an energy supply chain is a suitable plan. The objective of this paper is to plan a supply chain of generation of electricity by the process of co-firing that prompts benefits both in …

Algorithms For Multi-Objective Mixed Integer Programming Problems, Alvaro Miguel Sierra Altamiranda

USF Tampa Graduate Theses and Dissertations

This thesis presents a total of 3 groups of contributions related to multi-objective optimization. The first group includes the development of a new algorithm and an open-source user-friendly package for optimization over the efficient set for bi-objective mixed integer linear programs. The second group includes an application of a special case of optimization over the efficient on conservation planning problems modeled with modern portfolio theory. Finally, the third group presents a machine learning framework to enhance criterion space search algorithms for multi-objective binary linear programming.

In the first group of contributions, this thesis presents the first (criterion space search) algorithm …

Stakeholder Engagement In Marine Spatial Planning Of Bangladesh, Mohammad Samsul Mannan

World Maritime University Dissertations

No abstract provided.