Matching Items (13)
Description
Elections in the United States are highly decentralized with vast powers given to the states to control laws surrounding voter registration, primary procedures, and polling places even in elections of federal officials. There are many individual factors that predict a person's likelihood of voting including race, education, and age. Historically disenfranchised groups are still disproportionately affected by restrictive voter registration and ID laws which can suppress their turnout. Less understood is how election-day polling place accessibility affects turnout. Absentee and early voting increase accessibility for all voters, but 47 states still rely on election-day polling places. I study how the geographic allocation of polling places and the number of voters assigned to each (polling place load) in Maricopa County, Arizona has affected turnout in primary and general elections between 2006 and 2016 while controlling for the demographics of voting precincts. This represents a significant data problem; voting precincts changed three times during the time studied and polling places themselves can change every election. To aid in analysis, I created a visualization that allows for the exploration of polling place load, precinct demographics, and polling place accessibility metrics in a map view of the county. I find through a spatial regression model that increasing the load on a polling place can decrease the election-day turnout and prohibitively large distances to the polling place have a similar effect. The effect is more pronounced during general elections and is present at varying levels during each of the 12 elections studied. Finally, I discuss how early voting options appear to have little positive effect on overall turnout and may in fact decrease it.
ContributorsHansen, Brett Joseph (Author) / Maciejewski, Ross (Thesis director) / Grubesic, Anthony (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Environmental problems are more abundant because of the rapid increase in urbanization, climate change, and population growth leading to the depletion of natural resources and endangerment of some species. The availability of infrastructure as well as socio-economic factors facilitate the illicit trade of wildlife through supply chain networks, adding further threats to species. Ecosystem conservation and protection of wildlife from illegal trade and poaching is fundamental to guarantee the survival of endangered species. Conservation efforts require a landscape approach that incorporates spatial features for the effective functionality of the selected reserve. This dissertation studies combinatorial optimization problems with application to two classes of societal problems: landscape conservation and disruption of illicit supply chains. The first and second chapter propose a mixed-integer formulation to model the reserve design problem with budget and ecological constraints. The first uses the radius of the smallest circle enclosing the selected areas as a metric of compactness. An extension of the model is proposed to solve the multi reserve design problem and the reserve expansion problem. The solution approach includes warm start heuristic, separation problem and cuts to improve model performance. The enhanced model outperforms the linearized and the equivalent nonlinear model. The second chapter uses the Reock’s metric as a metric of compactness. The solution approach includes warm start heuristic, knapsack based separation problem to inject solutions, and cuts to improve model performance. The enhanced model outperforms the default model. The third chapter proposes an integer programming model to solve the wildlife corridor design problem with minimum width requirement and a budget constraint. A separation algorithm is proposed to identify boundary patches and violations in the corridor width. A branch-and-cut approach is proposed to induce the corridor width and is tested on real-life landscape. The fourth chapter proposes an integer programming formulation to model the disruption of illicit supply chain problem. The proposed model enforces that at least x paths must be disrupted for an Origin-Destination pair to be disrupted and at least y arcs must be disrupted for a path to be disrupted. The proposed model is tested on real-life road networks.
ContributorsRavishankar, Shreyas (Author) / Sefair, Jorge A (Thesis advisor) / Escobedo, Adolfo R (Committee member) / Grubesic, Anthony (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2023
Description
Workforce planning in service systems is essential for customer satisfaction and profitability. Typical decisions include hiring levels, shift design, break scheduling, movement of workers around facilities, and matching worker skills to the requirements of tasks. The complexity of these decisions grows when realistic factors such as spatiotemporal demand dynamics, system performance assessment, or skill learning are incorporated into planning. As a result, optimal (or near-optimal) workforce plans should utilize resources efficiently and achieve satisfactory levels of service. This dissertation provides models and solution algorithms for three problems in service system optimization, which include realistic workforce management planning, nonlinear dynamics, and scheduling of tasks. The second chapter studies an airport security screening process and prescribes a daily operational plan, including service rate (i.e., number of servers), scheduling, and resource allocation decisions. The non-stationary arrivals are predicted and known in advance. A discrete-time queuing model that relies on a simple approximation and flow conservation equations embedded in a mixed-integer programming formulation, where consecutive time periods are connected. A multi-step solution method is proposed to optimize various metrics, such as maximum allowed queue lengths and wait times.
The third chapter extends the model from the second chapter. In this case, resources can move between different server locations, and their transit time is unproductive. Movement dynamics are captured via a multi-commodity flow model on a time-expanded network. A new discrete-time queue approximation scheme addresses the inaccuracies in existing methods stemming from server overload and underload fluctuations. Problem-specific valid inequalities are derived to improve the solution time, and a temporal decomposition algorithm is proposed to find initial feasible solutions.
The last chapter focuses on a medium to long-term scheduling problem with embedded workforce management decisions. The classic formulation for multi-mode multi-skill resource-constrained project scheduling problem is extended to incorporate worker task learning and training dynamics simultaneously. A discretization scheme to model the nonlinear learning process resulting from job assignments is developed. Training of workers is enabled to acquire new skills. A mixed-integer programming formulation is introduced, and a sequential solution scheme is proposed. The trade-offs between project duration and workforce skill composition objectives are investigated.
ContributorsArici, Berkin Tan (Author) / Sefair, Jorge A (Thesis advisor) / Askin, Ronald G (Committee member) / Escobedo, Adolfo R (Committee member) / Teksan, Zehra M (Committee member) / Arizona State University (Publisher)
Created2023
Description
The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.
ContributorsSkolfield, Joshua Kyle (Author) / Escobedo, Adolfo R (Thesis advisor) / Sefair, Jorge (Committee member) / Mirchandani, Pitu (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study aims to combine the wisdom of crowds with ML to make more accurate stock price predictions for a select set of stocks. Different from prior works, this study uses different input elicitation techniques to improve crowd performance. In addition, machine learning is used to support the crowd. The influence of ML on the crowd is tested by priming participants with suggestions from an ML model. Lastly, the market conditions and stock popularity is observed to better understand crowd behavior.
ContributorsBhogaraju, Harika (Author) / Escobedo, Adolfo R (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Computational social choice theory is an emerging research area that studies the computational aspects of decision-making. It continues to be relevant in modern society because many people often work as a group and make decisions in a group setting. Among multiple research topics, rank aggregation is a central problem in computational social choice theory. Oftentimes, rankings may involve a large number of alternatives, contain ties, and/or be incomplete, all of which complicate the use of robust aggregation methods. To address these challenges, firstly, this work introduces a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non-strict (i.e., with-ties) and incomplete (i.e., unknown) preferences. The new measure, which can be regarded as a generalization of the seminal Kendall tau correlation coefficient, is proven to satisfy a set of metric-like axioms and to be equivalent to a recently developed ranking distance function associated with Kemeny aggregation. Secondly, this work derives an exact binary programming formulation for the generalized Kemeny rank aggregation problem---whose ranking inputs may be complete and incomplete, with and without ties. It leverages the equivalence of minimizing the Kemeny-Snell distance and maximizing the Kendall-tau correlation, to compare the newly introduced binary programming formulation to a modified version of an existing integer programming formulation associated with the Kendall-tau distance. Thirdly, this work introduces a new social choice property for decomposing large-size problems into smaller subproblems, which allows solving the problem in a distributed fashion. The new property is adequate for handling complete rankings with ties. The property is leveraged to develop a structural decomposition algorithm, through which certain large instances of the NP-hard Kemeny rank aggregation problem can be solved exactly in a practical amount of time. Lastly, this work applies these rank aggregation mechanisms to novel contexts for extracting collective wisdom in crowdsourcing tasks. Through this crowdsourcing experiment, we assess the capability of aggregation frameworks to recover underlying ground truth and the usefulness of multimodal information in overcoming anchoring effects, which shows its ability to enhance the wisdom of crowds and its practicability to the real-world problem.
ContributorsYoo, Yeawon (Author) / Escobedo, Adolfo R (Thesis advisor) / Mirchandani, Pitu B (Committee member) / Pavlic, Ted P (Committee member) / Chiou, Erin K (Committee member) / Arizona State University (Publisher)
Created2021
Description
Networks are a versatile modeling tool for the cyber and physical infrastructure that characterize society. They can be used to describe system spatiotemporal dynamics, including distribution of commodities, movement of agents, and data transmission. This flexibility has resulted in the widespread use of network optimization techniques for decision-making in telecommunications, transportation, commerce, among other systems. However, realistic network problems are typically large-scale and require the use of integer variables to incorporate design or logical system constraints. This makes such problems hard to solve and precludes their wide applicability in the solution of applied problems. This dissertation studies four large-scale optimization problems with underlying network structure in different domain applications, including wireless sensor networks, wastewater monitoring, and scheduling. The problems of interest are formulated using mixed-integer optimization formulations. The proposed solution approaches in this dissertation include branch-and-cut and heuristic algorithms, which are enhanced with network-based valid inequalities and network reduction techniques. The first chapter studies a relay node placement problem in wireless sensor networks, with and without the presence of transmission obstacles in the deployment region. The proposed integer linear programming approach leverages the underlying network structure to produce valid inequalities and network reduction heuristics, which are incorporated in the branch-and-bound exploration. The solution approach outperforms the equivalent nonlinear model and solves instances with up to 1000 sensors within reasonable time. The second chapter studies the continuous version of the maximum capacity (widest) path interdiction problem and introduces the first known polynomial time algorithm to solve the problem using a combination of binary search and the discrete version of the Newton’s method. The third chapter explores the service agent transport interdiction problem in autonomous vehicle systems, where an agent schedules service tasks in the presence of an adversary. This chapter proposes a single stage branch-and-cut algorithm to solve the problem, along with several enhancement techniques to improve scalability. The last chapter studies the optimal placement of sensors in a wastewater network to minimize the maximum coverage (load) of placed sensors. This chapter proposes a branch-and-cut algorithm enhanced with network reduction techniques and strengthening constraints.
ContributorsMitra, Ankan (Author) / Sefair, Jorge A (Thesis advisor) / Mirchandani, Pitu (Committee member) / Grubesic, Anthony (Committee member) / Byeon, Geunyeong (Committee member) / Arizona State University (Publisher)
Created2023
Description
Coastal areas are susceptible to man-made disasters, such as oil spills, which not
only have a dreadful impact on the lives of coastal communities and businesses but also
have lasting and hazardous consequences. The United States coastal areas, especially
the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations
that resulted in major economic and ecological losses. These disasters affected the oil,
housing, forestry, tourism, and fishing industries with overall costs exceeding billions
of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been
done with respect to oil spill simulation techniques, spatial optimization models, and
innovative strategies to deal with spill response and planning efforts. However, most
of the research done in those areas is done independently of each other, leaving a
conceptual void between them.
In the following work, this thesis presents a Spatial Decision Support System
(SDSS), which efficiently integrates the independent facets of spill modeling techniques
and spatial optimization to enable officials to investigate and explore the various
options to clean up an offshore oil spill to make a more informed decision. This
thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim
et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill
Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to
spatially optimize the response efforts. The results of this combination are visualized
in the SDSS, featuring geographical maps, so the boat ramps from which the response
should be launched can be easily identified along with the amount of oil that hits the
shore thereby visualizing the intensity of the impact of the spill in the coastal areas
for various cleanup targets.
only have a dreadful impact on the lives of coastal communities and businesses but also
have lasting and hazardous consequences. The United States coastal areas, especially
the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations
that resulted in major economic and ecological losses. These disasters affected the oil,
housing, forestry, tourism, and fishing industries with overall costs exceeding billions
of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been
done with respect to oil spill simulation techniques, spatial optimization models, and
innovative strategies to deal with spill response and planning efforts. However, most
of the research done in those areas is done independently of each other, leaving a
conceptual void between them.
In the following work, this thesis presents a Spatial Decision Support System
(SDSS), which efficiently integrates the independent facets of spill modeling techniques
and spatial optimization to enable officials to investigate and explore the various
options to clean up an offshore oil spill to make a more informed decision. This
thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim
et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill
Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to
spatially optimize the response efforts. The results of this combination are visualized
in the SDSS, featuring geographical maps, so the boat ramps from which the response
should be launched can be easily identified along with the amount of oil that hits the
shore thereby visualizing the intensity of the impact of the spill in the coastal areas
for various cleanup targets.
ContributorsPydi Medini, Prannoy Chandra (Author) / Maciejewski, Ross (Thesis advisor) / Grubesic, Anthony (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
I study the problem of locating Relay nodes (RN) to improve the connectivity of a set
of already deployed sensor nodes (SN) in a Wireless Sensor Network (WSN). This is
known as the Relay Node Placement Problem (RNPP). In this problem, one or more
nodes called Base Stations (BS) serve as the collection point of all the information
captured by SNs. SNs have limited transmission range and hence signals are transmitted
from the SNs to the BS through multi-hop routing. As a result, the WSN
is said to be connected if there exists a path for from each SN to the BS through
which signals can be hopped. The communication range of each node is modeled
with a disk of known radius such that two nodes are said to communicate if their
communication disks overlap. The goal is to locate a given number of RNs anywhere
in the continuous space of the WSN to maximize the number of SNs connected (i.e.,
maximize the network connectivity). To solve this problem, I propose an integer
programming based approach that iteratively approximates the Euclidean distance
needed to enforce sensor communication. This is achieved through a cutting-plane
approach with a polynomial-time separation algorithm that identies distance violations.
I illustrate the use of my algorithm on large-scale instances of up to 75 nodes
which can be solved in less than 60 minutes. The proposed method shows solutions
times many times faster than an alternative nonlinear formulation.
of already deployed sensor nodes (SN) in a Wireless Sensor Network (WSN). This is
known as the Relay Node Placement Problem (RNPP). In this problem, one or more
nodes called Base Stations (BS) serve as the collection point of all the information
captured by SNs. SNs have limited transmission range and hence signals are transmitted
from the SNs to the BS through multi-hop routing. As a result, the WSN
is said to be connected if there exists a path for from each SN to the BS through
which signals can be hopped. The communication range of each node is modeled
with a disk of known radius such that two nodes are said to communicate if their
communication disks overlap. The goal is to locate a given number of RNs anywhere
in the continuous space of the WSN to maximize the number of SNs connected (i.e.,
maximize the network connectivity). To solve this problem, I propose an integer
programming based approach that iteratively approximates the Euclidean distance
needed to enforce sensor communication. This is achieved through a cutting-plane
approach with a polynomial-time separation algorithm that identies distance violations.
I illustrate the use of my algorithm on large-scale instances of up to 75 nodes
which can be solved in less than 60 minutes. The proposed method shows solutions
times many times faster than an alternative nonlinear formulation.
ContributorsSurendran, Vishal Sairam Jaitra (Author) / Sefair, Jorge (Thesis advisor) / Mirchandani, Pitu (Committee member) / Grubesic, Anthony (Committee member) / Arizona State University (Publisher)
Created2019
Description
The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.
Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.
Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.
ContributorsWang, Feng (Author) / Maciejewski, Ross (Thesis advisor) / Davulcu, Hasan (Committee member) / Grubesic, Anthony (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2017