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- Creators: Haydn, Joseph, 1732-1809
Description
This thesis develops a low-investment marketing strategy that allows low-to-mid level farmers extend their commercialization reach by strategically sending containers of fresh produce items to secondary markets that present temporary arbitrage opportunities. The methodology aims at identifying time windows of opportunity in which the price differential between two markets create an arbitrage opportunity for a transaction; a transaction involves buying a fresh produce item at a base market, and then shipping and selling it at secondary market price. A decision-making tool is developed that gauges the individual arbitrage opportunities and determines the specific price differential (or threshold level) that is most beneficial to the farmer under particular market conditions. For this purpose, two approaches are developed; a pragmatic approach that uses historic price information of the products in order to find the optimal price differential that maximizes earnings, and a theoretical one, which optimizes an expected profit model of the shipments to identify this optimal threshold. This thesis also develops risk management strategies that further reduce profit variability during a particular two-market transaction. In this case, financial engineering concepts are used to determine a shipment configuration strategy that minimizes the overall variability of the profits. For this, a Markowitz model is developed to determine the weight assignation of each component for a particular shipment. Based on the results of the analysis, it is deemed possible to formulate a shipment policy that not only increases the farmer's commercialization reach, but also produces profitable operations. In general, the observed rates of return under a pragmatic and theoretical approach hovered between 0.072 and 0.616 within important two-market structures. Secondly, it is demonstrated that the level of return and risk can be manipulated by varying the strictness of the shipping policy to meet the overall objectives of the decision-maker. Finally, it was found that one can minimize the risk of a particular two-market transaction by strategically grouping the product shipments.
ContributorsFlores, Hector M (Author) / Villalobos, Rene (Thesis advisor) / Runger, George C. (Committee member) / Maltz, Arnold (Committee member) / Arizona State University (Publisher)
Created2011
Description
Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
ContributorsShinde, Amit (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsHaydn, Joseph, 1732-1809 (Composer)
ContributorsHaydn, Joseph, 1732-1809 (Composer)
Description
One of the greatest 21st century challenges is meeting the needs of a growing world population expected to increase 35% by 2050 given projected trends in diets, consumption and income. This in turn requires a 70-100% improvement on current production capability, even as the world is undergoing systemic climate pattern changes. This growth not only translates to higher demand for staple products, such as rice, wheat, and beans, but also creates demand for high-value products such as fresh fruits and vegetables (FVs), fueled by better economic conditions and a more health conscious consumer. In this case, it would seem that these trends would present opportunities for the economic development of environmentally well-suited regions to produce high-value products. Interestingly, many regions with production potential still exhibit a considerable gap between their current and ‘true’ maximum capability, especially in places where poverty is more common. Paradoxically, often high-value, horticultural products could be produced in these regions, if relatively small capital investments are made and proper marketing and distribution channels are created. The hypothesis is that small farmers within local agricultural systems are well positioned to take advantage of existing sustainable and profitable opportunities, specifically in high-value agricultural production. Unearthing these opportunities can entice investments in small farming development and help them enter the horticultural industry, thus expand the volume, variety and/or quality of products available for global consumption. In this dissertation, the objective is three-fold: (1) to demonstrate the hidden production potential that exist within local agricultural communities, (2) highlight the importance of supply chain modeling tools in the strategic design of local agricultural systems, and (3) demonstrate the application of optimization and machine learning techniques to strategize the implementation of protective agricultural technologies.
As part of this dissertation, a yield approximation method is developed and integrated with a mixed-integer program to estimate a region’s potential to produce non-perennial, vegetable items. This integration offers practical approximations that help decision-makers identify technologies needed to protect agricultural production, alter harvesting patterns to better match market behavior, and provide an analytical framework through which external investment entities can assess different production options.
As part of this dissertation, a yield approximation method is developed and integrated with a mixed-integer program to estimate a region’s potential to produce non-perennial, vegetable items. This integration offers practical approximations that help decision-makers identify technologies needed to protect agricultural production, alter harvesting patterns to better match market behavior, and provide an analytical framework through which external investment entities can assess different production options.
ContributorsFlores, Hector M. (Author) / Villalobos, Rene (Thesis advisor) / Pan, Rong (Committee member) / Wu, Teresa (Committee member) / Parker, Nathan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Mobile healthy food retailers are a novel alleviation technique to address disparities in access to urban produce stores in food desert communities. Such retailers, which tend to exclusively stock produce items, have become significantly more popular in the past decade, but many are unable to achieve economic sustainability. Therefore, when local and federal grants and scholarships are no longer available for a mobile food retailer, they must stop operating which poses serious health risks to consumers who rely on their services.
To address these issues, a framework was established in this dissertation to aid mobile food retailers with reaching economic sustainability by addressing two key operational decisions. The first decision was the stocked product mix of the mobile retailer. In this problem, it was assumed that mobile retailers want to balance the health, consumer cost, and retailer profitability of their product mix. The second investigated decision was the scheduling and routing plan of the mobile retailer. In this problem, it was assumed that mobile retailers operate similarly to traditional distribution vehicles with the exception that their customers are willing to travel between service locations so long as they are in close proximity.
For each of these problems, multiple formulations were developed which address many of the nuances for most existing mobile food retailers. For each problem, a combination of exact and heuristic solution procedures were developed with many utilizing software independent methodologies as it was assumed that mobile retailers would not have access to advanced computational software. Extensive computational tests were performed on these algorithm with the findings demonstrating the advantages of the developed procedures over other algorithms and commercial software.
The applicability of these techniques to mobile food retailers was demonstrated through a case study on a local Phoenix, AZ mobile retailer. Both the product mix and routing of the retailer were evaluated using the developed tools under a variety of conditions and assumptions. The results from this study clearly demonstrate that improved decision making can result in improved profits and longitudinal sustainability for the Phoenix mobile food retailer and similar entities.
To address these issues, a framework was established in this dissertation to aid mobile food retailers with reaching economic sustainability by addressing two key operational decisions. The first decision was the stocked product mix of the mobile retailer. In this problem, it was assumed that mobile retailers want to balance the health, consumer cost, and retailer profitability of their product mix. The second investigated decision was the scheduling and routing plan of the mobile retailer. In this problem, it was assumed that mobile retailers operate similarly to traditional distribution vehicles with the exception that their customers are willing to travel between service locations so long as they are in close proximity.
For each of these problems, multiple formulations were developed which address many of the nuances for most existing mobile food retailers. For each problem, a combination of exact and heuristic solution procedures were developed with many utilizing software independent methodologies as it was assumed that mobile retailers would not have access to advanced computational software. Extensive computational tests were performed on these algorithm with the findings demonstrating the advantages of the developed procedures over other algorithms and commercial software.
The applicability of these techniques to mobile food retailers was demonstrated through a case study on a local Phoenix, AZ mobile retailer. Both the product mix and routing of the retailer were evaluated using the developed tools under a variety of conditions and assumptions. The results from this study clearly demonstrate that improved decision making can result in improved profits and longitudinal sustainability for the Phoenix mobile food retailer and similar entities.
ContributorsWishon, Christopher John (Author) / Villalobos, Rene (Thesis advisor) / Fowler, John (Committee member) / Mirchandani, Pitu (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2016
Description
This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
ContributorsLujan Moreno, Gustavo A. (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert K (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Arizona State University (Publisher)
Created2017
ContributorsHaydn, Joseph, 1732-1809 (Composer)
ContributorsHaydn, Joseph, 1732-1809 (Composer)
Description
Increasing reliable produce farming and clean energy generation in the southwestern United States will be important for increasing the food supply for a growing population and reducing reliance on fossil fuels to generate energy. Combining greenhouses with photovoltaic (PV) films can allow both food and electric power to be produced simultaneously. This study tests if the combination of semi-transparent PV films and a transmission control layer can generate energy and spectrally control the transmission of light into a greenhouse. Testing the layer combinations in a variety of real-world conditions, it was shown that light can be spectrally controlled in a greenhouse. The transmission was overall able to be controlled by an average of 11.8% across the spectrum of sunlight, with each semi-transparent PV film able to spectrally select transmission of light in both the visible and near-infrared light wavelength. The combination of layers was also able to generate energy at an average efficiency of 8.71% across all panels and testing conditions. The most efficient PV film was the blue dyed, at 9.12%. This study also suggests additional improvements for this project, including the removal of the red PV film due to inefficiencies in spectral selection and additional tests with new materials to optimize plant growth and energy generation in a variety of light conditions.
ContributorsGunderson, Evan (Author) / Phelan, Patrick (Thesis director) / Villalobos, Rene (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05