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Aggregate production planning generally deals with configuration of an aggregate plan in advance of 6 to 18 periods (e.g. months) to give the organizations an idea about the amount of invested money, utilized capacity, required inventory and any other procurement activities need to be done before the actual times arrives. Inherent uncertainties faced by the planners (caused by unreliable estimates of demand, cost or production processes) could make the production planning a challenging task. That is, the production planners not only have to deal with the available parameters’ uncertainties (Demand, cost, etc.), but also, new information which become available with the pass of time, sometimes requires several re-planning activities for the future periods. Stochastic and Fuzzy planning are among the popular techniques to deal with the uncertainties in optimization models. While the stochastic/fuzzy programming techniques provide a more realistic representation of future estimations, the production plans need to be also revised from one planning period to another as time rolls and new information become available (a.k.a. rolling horizon planning). However, frequent re-planning activities and changes in the production plans could result in a state of plan instability causing plan related “nervousness” in manufacturing firms, which could undermine manager’s confidence in the system, depriving it of the support needed for successful operations. It could also result in disruptions in the production and delivery systems, which could result in inaccurate personnel scheduling, machine loading, and unnecessary supplier orders.
Frozen horizon along with other solution approaches attempt to provide insights on how to mitigate nervousness, however, most of the existing approaches do not consider the flexibility aspect in production plans. Flexible Requirements Profile (FRP) and bi-objective optimization are alternative stabilizing approaches which are the focus of this research. In FRP, flexible bounds are enforced on production plans to maintain the desired degree of flexibility. Instead of 0% flexibility in the case of a frozen period or 100% flexibility in the case of plan to order, FRP model considers different flexibility levels. For the bi-objective optimization approach, the production planning problem can also be formulated with two objectives, where one trades-off between the traditional cost objective and the plan stability objective.
Our main research objectives are:
1) to compare FRP-APP with Stochastic and Fuzzy APP in terms of both plan cost and stability,
2) to develop and compare new “hybrid” Stochastic and Fuzzy FRP-APP models to combine the strengths of stochastic and fuzzy models, which represent input uncertainties more realistically, and FRP models that have better control over plan variability,
3) to develop and compare new Stochastic and Fuzzy Bi-objective APP models as alternate techniques to trade off the traditional cost objective with the stability objective formally following a multi-objective decision making framework, and
4) to conduct extensive testing of the proposed FRP-based and Bi-objective models under various industry scenarios.
Ultra-precision manufacturing is a deterministic method of producing optical-grade components. Continuous and interruptive machine operations are the focus of this research with the goal of improving the manufacturing community’s knowledge. The original contributions of this research are: (a) a comprehensive analysis of the cutting mechanics of single-crystal germanium, specifically studying the effects of major crystal orientation in germanium and cutting speed; (b) methodology for producing flat, damage-free test samples in single-crystal germanium; and (c) machine learning model for estimating surface finish parameters Sa, Sq, and Sz for SPDT of single-crystal germanium and oxygen-free high-conductivity copper. As a final product of this research, a pair of collimating lenses were produced. AFRL funded the research for development of these lenses.
Ultra-precision manufacturing is a deterministic method of producing optical-grade components. Continuous and interruptive machine operations are the main focus of this research with the goal of improving the manufacturing communities knowledge. The original contributions of this research are: (a) a comprehensive analysis of the cutting mechanics of single-crystal germanium, specifically studying the effects of major crystal orientation in germanium and cutting speed; (b) methodology for producing
flat, damage-free test samples in single-crystal germanium; and (c) machine learning model for estimating surface finish parameters Sa, Sq, and Sz for SPDT of single-crystal germanium and oxygen-free high-conductivity copper. As a final product of this research, a pair of collimating lenses were produced. AFRL funded the research for development of these lenses.
In this study, we used scattering-scanning near-field optical microscopy (s-SNOM) to experimentally characterize several structures relevant to infrared (IR) antenna technology, by measuring the electric near-field strength as a function of position on metallic antennas and transmission lines. Having spatially resolved measurements of electric field amplitude allows assessment of the wave impedance at any location on a standing-wave structure. We improved the usual s-SNOM data-processing method using a principal-components decomposition to allow unambiguous phase retrieval. We demonstrated the efficacy of this technique on IR bow-tie antennas of continuous and discrete designs, allowing comparison of their polarization dependence and spatial response distribution. This phase-retrieval procedure was used throughout our investigations. An IR sensor of particular interest is the antenna-coupled metal-oxide-metal (MOM) diode, which rectifies IR-frequency current waves collected by the antenna to produce an output voltage proportional to the incident irradiance. These sensors are appealing because they have a fast response and do not require cryogenic cooling. IR antennas have a typical impedance in the range of tens of Ohms at resonance, while MOM diodes have impedance in the range of thousands of Ohms. This impedance mismatch is a limiting factor in the detection sensitivity that can be achieved with antenna-coupled MOM diodes. To address this issue, we studied two impedance-matching techniques. The first is based on the fact that a MOM diode under DC bias exhibits a change in its dynamic resistance. We obtained measurements that demonstrate modification of IR-frequency current waves using diodes contained in the antenna structure. The ability to tune the operating point of a MOM diode and thereby modify antenna or transmission-line impedance at IR frequencies offers the possibility of active impedance-matching networks. The second technique we investigated involved tailoring of the feed-point geometry to obtain an antenna with higher impedance that offers better matching. We designed, fabricated and demonstrated several new IR-antenna designs that have impedance in the range of 1000 Ohms. This new class of antennas stands to improve signal-transfer efficiency to high-impedance IR sensors such as MOM diodes.
Decades of discriminatory housing policies have resulted in geographic segregation, forcing low-income minorities into areas of concentrated poverty (Massey & Kanaiaupuni, 1993; Stoloff, 2004). Areas of concentrated poverty are typically marked by poor housing quality, under performing schools, high crime rates, and limited access to resources such as healthcare and grocery stores, lack of social cohesion, and poor health outcomes (Crump, 2002; Dutko, Ver Ploeg, & Farrigan, 2012; Kawachi & Berkman, 2000; Massey, 1990). To combat the challenges associated with concentrated poverty and build healthy communities, place-based interventions have become increasingly popular (Arias, Escobedo, Kennedy, Fu, & Cisewski, 2018; Diez-Roux, 2017; Jutte, Miller, & Erickson, 2015). Several place-based models (e.g., Harlem Children’s Zone, Purpose Built Communities) have shown positive outcomes (Bridgespan 2004; 2011), however evaluation to guide replication and best practices have lagged.
This study examined data from a nonprofit replicating the Purpose Built Communities model in the southeastern U.S. Renaissance West Community Initiative (RWCI) is a place-based nonprofit that coordinates activities and services for residents living in a newly redeveloped mixed-income community and an adjacent low-income community. Activities coordinated by RWCI include college and career readiness programs, health education programs, health resources, community engagement activities, and children’s programs. Data from program participation and community surveys were assessed to understand the characteristics of adult residents, such as their education level, employment status, income, health, social networks, perceptions of their neighbors, participation in the nonprofit’s activities, and the degree to which each of these variables are related. Additionally, longitudinal analyses examined changes in these variables over a twelve to eighteen-month period.
Findings show that residents’ socioeconomic status (SES) and social network size were the primary predictors of the types of RWCI activities in which they participated and the frequency of participation. Participation in RWCI’s activities was not related to changes in SES, health, or neighborhood perceptions, but participation in activities was related to increased social network size. Social networks also played a role in neighborhood perceptions, such that residents with stronger neighborhood social networks had more positive perceptions of their neighbors overall. Residents with a disability had the lowest perceptions of their neighbors and reported worse health status.
The present study provides an example of how even limited quantitative data can be used by place-based nonprofits to understand the characteristics and experiences of adults living in their service area, to monitor implementation and outcomes, and provide guidance for improvements in use of resources to improve the community. The findings have implications for RWCI and their ongoing efforts to revitalize this low-income neighborhood into a healthy mixed-income community. Recommendations for ongoing data collection and analyses, targeting of services, and community building strategies are provided.
Nucleic acid biopolymers are essential to all living organisms. The chemical makeup of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) consist of sequences of only four monomers. However, they transmit and express genetic information for various biological functions based on stored blueprints. Along with communicating the flow of genetic information (DNA → RNA→ protein), nucleic acids have also become a preferred material for fabrication of objects and scaffolds at the nanoscale. Nucleic acid-based assemblies that interact with each other and communicate with the cellular machinery represent a new class of reconfigurable nanostructures that enable precise control over their formulation and physicochemical properties and activation of multiple biological functionalities. Therefore, the use of nucleic acids offers a unique platform for development of nanoparticles which consist of nanoscale-size oligonucleotides designed to fold into predicted three-dimentional structures. They can serve as scaffolds capable of carrying numerous functional moieties such as: RNA interference activators for gene silencing, aptamers for specific targeting of selected molecules, immunostimulatory sequences for modulating immune responses, and fluorescent entities for bioimaging. Programmable, controllable, biocompatible, rationally designed, and self-assembled nucleic acid nanoparticles have become attractive candidates for diverse therapeutic options. Despite profound advances in the field of therapeutic nucleic acids, their negative charges decrease membrane permeation capacity, thus hindering their translation from experimental research to clinical application. This dissertation focuses on the development of nucleic acid nanoparticles as therapeutics with defined immunostimulatory properties accompanied by rationally designed systems able to regulate the duration of therapeutic activity. Furthermore, a safe, efficient and stable intracellular delivery system for multi-functional nucleic acid nanoparticle platforms is investigated.
The proliferation of connected devices creates various use cases and heterogeneous services, e.g., augmented/virtual reality (AR/VR), vehicle-to-everything (V2X), and mobile artificial intelligence. These services and use cases have diverse networking and performance requirements such as throughput and delay, which challenge the "one-fit-all" service architecture in current networks. In this research, an intelligent network management framework in mobile edge computing is explored. The primary challenges lie in the unique characteristics of heterogeneous services and complicated correlations between network management on multiple technical domains and high-dimension performance requirements in the complex mobile networks. This research addresses these challenges with two different management approaches. From the perspective of service providers, multiple mobile systems are designed to allow service adaptation under complex network dynamics, e.g., channel variation and traffic workload, which dynamically and adaptively adjust resource allocations and system configurations by exploiting the unique characteristics of individual services. From the perspective of infrastructure providers, multiple network systems are proposed to enable orchestration intelligence without accurate performance modelings of services, which automatically learn to orchestrate multiple domain network resources for supporting various services by exploiting advanced machine learning techniques.
Freeform optics, or optics with no axis of rotational invariance, provide optical designers more degrees of freedom, flexibility, and opportunity for innovation increasing optical performance and system integration while decreasing the form factor. Advancements in optical fabrication have enabled freeform surface manufacture with greater precision. Metrology instruments and techniques are needed to verify the performance of freeform optical surfaces and systems to keep up with design and manufacture. Freeform optics often have high slopes, no axis of symmetry, and a large departure from spherical, making traditional metrology techniques inadequate. This research was conducted to enable form measurements of freeform mirrors in the 250 mm class for a next generation three mirror anastigmatic (TMA) telescope complete with a statement of the measurement uncertainty to fill the gap in metrology of freeform optics. A flexible metrology instrument that could measure relatively large optics with customizable probe paths and sampling strategies was needed while maintaining the required precision.
In today’s world of modern metrology, it becomes increasingly common to find new technologies which employ non-traditional measuring mechanisms in attempts to provide new advantages to lever over competing measuring instruments. In addition to such new technologies emerging, parts being measured also tend to grow in complexity, presenting new and unique challenges in their measurement. As a result of such advancements in technology and increasing part complexities, it becomes necessary to develop and optimize data processing, data modeling methods and error analysis methods tailored to a given measurement technique or a given part. This dissertation explores these aspects of discrete part metrology and presents solutions for each case considered. A data processing method is developed to allow measurement of any complex part using a precision rotary table and a set of triangulation-based laser line sensors. Machine learning approaches are also explored for a similar measurement problem. The system described is tailored to measure desired discrete parts (gears in this case) and can be considered a custom coordinate measuring system (CMS). An essential step to pushing such custom machines or new measurement technologies into widespread use, is the development of standardized test procedures that enable users to compare different technologies by their performance against traceable standards. Development of such standards often involve designing performance evaluation test procedures that are sensitive to as many known error sources are possible. Therefore, such an approach is adopted for two measuring technologies, namely X-Ray Computed Tomography (XCT) and Stereo-vision Photogrammetry. In each case, several known error sources are characterized, and recommendations are made on ways to capture them. Further, from an automation perspective, it becomes essential to use a data modeling system that can support descriptions of complex parts and their measurement results, as well as accommodate new measurement technologies and tools. To this end, a promising candidate, the Quality Information Framework (QIF) has been identified.
Demand for clean and safe drinking water is a global challenge because of water scarcity, growth of human population, urbanization, and anthropogenic pollution. Purification of water involves removal of small molecules and ions from ground water addressed as “emerging contaminants” which are extremely mobile and toxic in nature, do not degrade or hydrolyze easily, and highly soluble in water resulting in bioaccumulation. Most of the current water treatment systems have complex deficiencies that affect their overall performance. We have synthesized carbon nanostructures assisted ion exchange resins in aqueous medium that help remove these emerging contaminants in a fast, easy, and high capacity manner while supporting less contact time and low transmembrane resistance primarily achieved using thin film assemblies. We have developed a novel sonochemistry assisted atom transfer radical polymerization (SONO-ATRP) process for synthesis of polyelectrolyte anion exchange resins in water without use of any external initiator or reducing agents while using only a few ppm of catalyst. We successfully performed high-density functionalization of polyelectrolyte anion exchange resin strands onto single walled carbon nanotubes sidewalls using the SONO-ATRP process while at low reaction temperatures thereby providing a less energy intensive alternative for green chemistry. We have developed green processes to defluorinate fluorographite in water and simultaneously perform covalent grafting of anionic short brushes of poly(vinyl benzyl trimethylammonium chloride) to its surface under mild reaction conditions without need of any external reactive reagents. Field Emission Scanning Electron microscopy of thin film of functionalized carbon nanotubes demonstrated pin-hole free mesoporous architecture illustrating scaffold robustness while thin films of functionalized fluorographite exhibited stacked arrangement of plate-like structures. Exfoliation and functionalization of fluorographite was revealed through Transmission Electron Microscopy. Both the resins demonstrated high water flux (>1500 L m^(-2) h^(-1) bar^(-1)) due to their intrinsic architecture and high percent removal (>90%) of contaminants due to the tortuous path length during molecular transport through the membrane. These properties enable adsorption of impurities at environmentally relevant concentrations. These materials exhibited facile regeneration and reusage of the thin films, thus supporting sustainability. In conclusion, these processes abide by the principles of green chemistry and their processability opens new avenues for smart point-of-use water purification systems.
