Matching Items (1,240)
Filtering by

Clear all filters

151286-Thumbnail Image.png
Description
Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities. Addressing continuous demand and multi-facilities represents major challenges. Given advances

Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities. Addressing continuous demand and multi-facilities represents major challenges. Given advances in geographic information systems (GIS), computational science and associated technologies, spatial optimization provides a possibility for improved problem solution. Essential here is how to represent facilities and demand in geographic space. In one respect, spatial abstraction as discrete points is generally assumed as it simplifies model formulation and reduces computational complexity. However, errors in derived solutions are likely not negligible, especially when demand varies continuously across a region. In another respect, although mathematical functions describing continuous distributions can be employed, such theoretical surfaces are generally approximated in practice using finite spatial samples due to a lack of complete information. To this end, the dissertation first investigates the implications of continuous surface approximation and explicitly shows errors in solutions obtained from fitted demand surfaces through empirical applications. The dissertation then presents a method to improve spatial representation of continuous demand. This is based on infill asymptotic theory, which indicates that errors in fitted surfaces tend to zero as the number of sample points increases to infinity. The implication for facility location modeling is that a solution to the discrete problem with greater demand point density will approach the theoretical optimum for the continuous counterpart. Therefore, in this research discrete points are used to represent continuous demand to explore this theoretical convergence, which is less restrictive and less problem altering compared to existing alternatives. The proposed continuous representation method is further extended to develop heuristics to solve the continuous Weber and multi-Weber problems, where one or more facilities can be sited anywhere in continuous space to best serve continuously distributed demand. Two spatial optimization approaches are proposed for the two extensions of the Weber problem, respectively. The special characteristics of those approaches are that they integrate optimization techniques and GIS functionality. Empirical results highlight the advantages of the developed approaches and the importance of solution integration within GIS.
ContributorsYao, Jing (Author) / Murray, Alan T. (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Kuby, Michael J (Committee member) / Arizona State University (Publisher)
Created2012
152456-Thumbnail Image.png
Description
Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of

Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of the vehicles have a range that is less than those powered by gasoline. These factors together create a "range anxiety" in drivers, which causes the drivers to worry about the utility of alternative-fuel and electric vehicles and makes them less likely to purchase these vehicles. For the new vehicle technologies to thrive it is critical that range anxiety is minimized and performance is increased as much as possible through proper routing and scheduling. In the case of long distance trips taken by individual vehicles, the routes must be chosen such that the vehicles take the shortest routes while not running out of fuel on the trip. When many vehicles are to be routed during the day, if the refueling stations have limited capacity then care must be taken to avoid having too many vehicles arrive at the stations at any time. If the vehicles that will need to be routed in the future are unknown then this problem is stochastic. For fleets of vehicles serving scheduled operations, switching to alternative-fuels requires ensuring the schedules do not cause the vehicles to run out of fuel. This is especially problematic since the locations where the vehicles may refuel are limited due to the technology being new. This dissertation covers three related optimization problems: routing a single electric or alternative-fuel vehicle on a long distance trip, routing many electric vehicles in a network where the stations have limited capacity and the arrivals into the system are stochastic, and scheduling fleets of electric or alternative-fuel vehicles with limited locations to refuel. Different algorithms are proposed to solve each of the three problems, of which some are exact and some are heuristic. The algorithms are tested on both random data and data relating to the State of Arizona.
ContributorsAdler, Jonathan D (Author) / Mirchandani, Pitu B. (Thesis advisor) / Askin, Ronald (Committee member) / Gel, Esma (Committee member) / Xue, Guoliang (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2014
155983-Thumbnail Image.png
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

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.
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
156498-Thumbnail Image.png
Description
Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are

Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.
ContributorsMahmoudi, Monirehalsadat (Author) / Zhou, Xuesong (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Miller, Harvey J. (Committee member) / Pendyala, Ram M. (Committee member) / Arizona State University (Publisher)
Created2018
155128-Thumbnail Image.png
Description
This dissertation carries out an inter-disciplinary research of operations research, statistics, power system engineering, and economics. Specifically, this dissertation focuses on a special power system scheduling problem, a unit commitment problem with uncertainty. This scheduling problem is a two-stage decision problem. In the first stage, system operator determines the binary

This dissertation carries out an inter-disciplinary research of operations research, statistics, power system engineering, and economics. Specifically, this dissertation focuses on a special power system scheduling problem, a unit commitment problem with uncertainty. This scheduling problem is a two-stage decision problem. In the first stage, system operator determines the binary commitment status (on or off) of generators in advance. In the second stage, after the realization of uncertainty, the system operator determines generation levels of the generators. The goal of this dissertation is to develop computationally-tractable methodologies and algorithms to solve large-scale unit commitment problems with uncertainty.

In the first part of this dissertation, two-stage models are studied to solve the problem. Two solution methods are studied and improved: stochastic programming and robust optimization. A scenario-based progressive hedging decomposition algorithm is applied. Several new hedging mechanisms and parameter selections rules are proposed and tested. A data-driven uncertainty set is proposed to improve the performance of robust optimization.

In the second part of this dissertation, a framework to reduce the two-stage stochastic program to a single-stage deterministic formulation is proposed. Most computation of the proposed approach can be done by offline studies. With the assistance of offline analysis, simulation, and data mining, the unit commitment problems with uncertainty can be solved efficiently.

Finally, the impacts of uncertainty on energy market prices are studied. A new component of locational marginal price, a marginal security component, which is the weighted shadow prices of the proposed security constraints, is proposed to better represent energy prices.
ContributorsLi, Chao (Author) / Hedman, Kory W (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2016
Description

Human Papillomavirus, or HPV, is a viral pathogen that most commonly spreads through sexual contact. HPV strains 6 and 11 normally cause genital warts, while HPV strains 16 and 18 commonly cause cervical cancer, which causes cancerous cells to spread in the cervix. Physicians can detect those HPV strains, using

Human Papillomavirus, or HPV, is a viral pathogen that most commonly spreads through sexual contact. HPV strains 6 and 11 normally cause genital warts, while HPV strains 16 and 18 commonly cause cervical cancer, which causes cancerous cells to spread in the cervix. Physicians can detect those HPV strains, using a Pap smear, which is a diagnostic test that collects cells from the female cervix.

Created2021-04-06
Description

Johann Gregor Mendel studied patterns of trait inheritance in plants during the nineteenth century. Mendel, an Augustinian monk, conducted experiments on pea plants at St. Thomas’ Abbey in what is now Brno, Czech Republic. Twentieth century scientists used Mendel’s recorded observations to create theories about genetics.

Created2022-01-13
175283-Thumbnail Image.jpg
Description

In the 1930s, George Beadle and Boris Ephrussi discovered factors that affect eye colors in developing fruit flies. They did so while working at the California Institute of Technology in Pasadena, California. (1) They took optic discs (colored fuchsia in the image) from fruit fly larvae in the third instar

In the 1930s, George Beadle and Boris Ephrussi discovered factors that affect eye colors in developing fruit flies. They did so while working at the California Institute of Technology in Pasadena, California. (1) They took optic discs (colored fuchsia in the image) from fruit fly larvae in the third instar stage of development. Had the flies not been manipulated, they would have developed into adults with vermilion eyes. (2) Beadle and Ephrussi transplanted the donor optic discs into the bodies of several types of larvae, including those that would develop with normal colored eyes (brick red), and those that would develop eyes with other shades of red, such as claret, carmine, peach, and ruby (grouped together and colored black in the image). (3a) When implanted into normal hosts that would develop brick red eyes, the transplanted optic disc developed into an eye that also was brick red. (3b) When implanted into abnormal hosts that would develop eyes of some other shade of red, the transplanted optic discs developed into eyes that were vermilion. Beadle and Ephrussi concluded that there was a factor, such as an enzyme or some other protein, produced outside of the optic disc that influenced the color of the eye that developed from the disc.

Created2016-10-11
175286-Thumbnail Image.jpg
Description

This illustration shows George Beadle and Edward Tatum's experiments with Neurospora crassa that indicated that single genes produce single enzymes. The pair conducted the experiments at Stanford University in Palo Alto, California. Enzymes are types of proteins that can catalyze reactions inside cells, reactions that produce a number of things,

This illustration shows George Beadle and Edward Tatum's experiments with Neurospora crassa that indicated that single genes produce single enzymes. The pair conducted the experiments at Stanford University in Palo Alto, California. Enzymes are types of proteins that can catalyze reactions inside cells, reactions that produce a number of things, including nutrients that the cell needs. Neurospora crassa is a species of mold that grows on bread. In the early 1940s, Beadle and Tatum conducted an experiment to discover the abnormal genes in Neurospora mutants, which failed to produce specific nutrients needed to survive. (1) Beadle and Tatum used X-rays to cause mutations in the DNA of Neurospora, and then they grew the mutated Neurospora cells in glassware. (2) They grew several strains, represented in four groups of paired test tubes. For each group, Neurospora was grown in one of two types of growth media. One medium contained all the essential nutrients that the Neurospora needed to survive, which Beadle and Tatum called a complete medium. The second medium was a minimal medium and lacked nutrients that Neurospora needed to survive. If functioning normally and in the right conditions, however, Neurospora can produce these absent nutrients. (3) When Beadle and Tatum grew the mutated mold strains on both the complete and on the minimal media, all of the molds survived on the complete media, but not all of the molds survived on the minimal media (strain highlighted in yellow). (4) For the next step, the researchers added nutrients to the minimal media such that some glassware received an amino acid mixture (represented as colored squares) and other glassware received a vitamin mixture (represented as colored triangles) in an attempt to figure out which kind of nutrients the mutated molds needed. The researchers then took mold from the mutant mold strain that had survived on a complete medium and added that mold to the supplemented minimal media. They found that in some cases the mutated mold grew on media supplemented only with vitamins but not on media supplemented only with amino acids. (5) To discover which vitamins the mutant molds needed, Beadle and Tatum used several tubes with the minimal media, supplementing each one with a different vitamin, and then they attempted to grow the mutant mold in each tube. They found that different mutant strains of the mold grew only on media supplemented with different kinds of vitamins, for instance vitamin B6 for one strain, and vitamin B1 for another. In experiments not pictured, Beadle and Tatum found in step (4) that other strains of mutant mold grew on minimal media supplemented only with amino acids but not on minimal media supplemented only with vitamins. When they repeated step (5) on those strains and with specific kinds of amino acids in the different test tubes, they found that the some mutated mold strains grew on minimal media supplemented solely with one kind of amino acid, and others strains grew only on minimal media supplemented with other kinds of amino acids. For both the vitamins and amino acid cases, Beadle and Tatum concluded that the X-rays had mutated different genes in Neurospora, resulting in different mutant strains of Neurospora cells. In a cell of a given strain, the X-rays had changed the gene normally responsible for producing an enzyme that catalyzed a vitamin or an amino acid. As a result, the Neurospora cell could no longer produce that enzyme, and thus couldn't catalyze a specific nutrient.

Created2016-10-12