Matching Items (37)
Description
Project portfolio selection (PPS) is a significant problem faced by most organizations. How to best select the many innovative ideas that a company has developed to deploy in a proper and sustained manner with a balanced allocation of its resources over multiple time periods is one of vital importance to a company's goals. This dissertation details the steps involved in deploying a more intuitive portfolio selection framework that facilitates bringing analysts and management to a consensus on ongoing company efforts and buy into final decisions. A binary integer programming selection model that constructs an efficient frontier allows the evaluation of portfolios on many different criteria and allows decision makers (DM) to bring their experience and insight to the table when making a decision is discussed. A binary fractional integer program provides additional choices by optimizing portfolios on cost-benefit ratios over multiple time periods is also presented. By combining this framework with an `elimination by aspects' model of decision making, DMs evaluate portfolios on various objectives and ensure the selection of a portfolio most in line with their goals. By presenting a modeling framework to easily model a large number of project inter-dependencies and an evolutionary algorithm that is intelligently guided in the search for attractive portfolios by a beam search heuristic, practitioners are given a ready recipe to solve big problem instances to generate attractive project portfolios for their organizations. Finally, this dissertation attempts to address the problem of risk and uncertainty in project portfolio selection. After exploring the selection of portfolios based on trade-offs between a primary benefit and a primary cost, the third important dimension of uncertainty of outcome and the risk a decision maker is willing to take on in their quest to select the best portfolio for their organization is examined.
ContributorsSampath, Siddhartha (Author) / Gel, Esma (Thesis advisor) / Fowler, Jown W (Thesis advisor) / Kempf, Karl G. (Committee member) / Pan, Rong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
ContributorsPeng, Dening (Author) / Mirchandani, Pitu B. (Thesis advisor) / Sefair, Jorge (Committee member) / Wu, Teresa (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install Global Positioning System(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments).
ContributorsFu, Zishan (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2019
Description
The shift in focus of manufacturing systems to high-mix and low-volume production poses a challenge to both efficient scheduling of manufacturing operations and effective assessment of production capacity. This thesis considers the problem of scheduling a set of jobs that require machine and worker resources to complete their manufacturing operations. Although planners in manufacturing contexts typically focus solely on machines, schedules that only consider machining requirements may be problematic during implementation because machines need skilled workers and cannot run unsupervised. The model used in this research will be beneficial to these environments as planners would be able to determine more realistic assignments and operation sequences to minimize the total time required to complete all jobs. This thesis presents a mathematical formulation for concurrent scheduling of machines and workers that can optimally schedule a set of jobs while accounting for changeover times between operations. The mathematical formulation is based on disjunctive constraints that capture the conflict between operations when trying to schedule them to be performed by the same machine or worker. An additional formulation extends the previous one to consider how cross-training may impact the production capacity and, for a given budget, provide training recommendations for specific workers and operations to reduce the makespan. If training a worker is advantageous to increase production capacity, the model recommends the best time window to complete it such that overlaps with work assignments are avoided. It is assumed that workers can perform tasks involving the recently acquired skills as soon as training is complete. As an alternative to the mixed-integer programming formulations, this thesis provides a math-heuristic approach that fixes the order of some operations based on Largest Processing Time (LPT) and Shortest Processing Time (SPT) procedures, while allowing the exact formulation to find the optimal schedule for the remaining operations. Computational experiments include the use of the solution for the no-training problem as a starting feasible solution to the training problem. Although the models provided are general, the manufacturing of Printed Circuit Boards are used as a case study.
ContributorsAdams, Katherine Bahia (Author) / Sefair, Jorge (Thesis advisor) / Askin, Ronald (Thesis advisor) / Webster, Scott (Committee member) / Arizona State University (Publisher)
Created2019
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
Ultimate Frisbee or "Ultimate," is a fast growing field sport that is being played competitively at universities across the country. Many mid-tier college teams have the goal of winning as many games as possible, however they also need to grow their program by training and retaining new players. The purpose of this project was to create a prototype statistical tool that maximizes a player line-up's probability of scoring the next point, while having as equal playing time across all experienced and novice players as possible. Game, player, and team data was collected for 25 different games played over the course of 4 tournaments during Fall 2017 and early Spring 2018 using the UltiAnalytics iPad application. "Amount of Top 1/3 Players" was the measure of equal playing time, and "Line Efficiency" and "Line Interaction" represented a line's probability of scoring. After running a logistic regression, Line Efficiency was found to be the more accurate predictor of scoring outcome than Line Interaction. An "Equal PT Measure vs. Line Efficiency" graph was then created and the plot showed what the optimal lines were depending on what the user's preferences were at that point in time. Possible next steps include testing the model and refining it as needed.
ContributorsSpence, Andrea Nicole (Author) / McCarville, Daniel R. (Thesis director) / Pavlic, Theodore (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Effective communication and engineering are not a natural pairing. The incongruence is because engineering students are focused on making, designing and analyzing. Since these are the core functions of the field there is not a direct focus on developing communication skills. This honors thesis explores the role and expectations for student engineers within the undergraduate engineering education experience to present and communicate ideas. The researchers interviewed faculty about their perspective on students' abilities with respect to their presentation skills to inform the design of a workshop series of interventions intended to make engineering students better communicators.
ContributorsAlbin, Joshua Alexander (Co-author) / Brancati, Sara (Co-author) / Lande, Micah (Thesis director) / Martin, Thomas (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Revenue management (RM) attempts to understand and shape consumer behavior to maximize revenue from a perishable resource. Various algorithms can be used to control bid-prices, and subsequently, perform differently with respect to the total network revenue that they generate. There is currently a need for some method to compare RM engines; a simulation can fulfill this need.
The first module of this thesis will create a statistically accurate representation of customers arriving at ticket purchasing channels. Each customer's attributes are: arrival time, origin and destination, number of destined tickets, and willingness to pay. Each attribute can be generated using a specific distribution.
The created customers will then be used to simulate the purchase of tickets and overall revenue for a flight network. With a valid simulation, airlines will be able to compare the performance of different RM engines under various circumstances.
The first module of this thesis will create a statistically accurate representation of customers arriving at ticket purchasing channels. Each customer's attributes are: arrival time, origin and destination, number of destined tickets, and willingness to pay. Each attribute can be generated using a specific distribution.
The created customers will then be used to simulate the purchase of tickets and overall revenue for a flight network. With a valid simulation, airlines will be able to compare the performance of different RM engines under various circumstances.
ContributorsFischer, Amanda (Author) / Gel, Esma (Thesis director) / Jacobs, Tim (Thesis director) / Purnomo, Hadi (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2012-05
Description
This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.
ContributorsDean, Arlen (Co-author) / Ozcan, Ozkan (Co-author) / Travis, Daniel (Co-author) / Gel, Esma (Thesis director) / Armbruster, Dieter (Committee member) / Parry, Sam (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the park could mean the difference between life and death. In an effort to provide the utmost safety for the guests of a park, it is important to make the best decision when selecting the location for emergency response crews. A theme park is different from a regular residential or commercial area because the crowds and shows block certain routes, and they change throughout the day. We propose an optimization model that selects staging locations for emergency medical responders in a theme park to maximize the number of responses that can occur within a pre-specified time. The staging areas are selected from a candidate set of restricted access locations where the responders can store their equipment. Our solution approach considers all routes to access any park location, including areas that are unavailable to a regular guest. Theme parks are a highly dynamic environment. Because special events occurring in the park at certain hours (e.g., parades) might impact the responders' travel times, our model's decisions also include the time dimension in the location and re-location of the responders. Our solution provides the optimal location of the responders for each time partition, including backup responders. When an optimal solution is found, the model is also designed to consider alternate optimal solutions that provide a more balanced workload for the crews.
ContributorsLivingston, Noah Russell (Author) / Sefair, Jorge (Thesis director) / Askin, Ronald (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12