With the explosion of autonomous systems under development, complex simulation models are being tested and relied on far more than in the recent past. This uptick in autonomous systems being modeled then tested magnifies both the advantages and disadvantages of simulation experimentation. An inherent problem in autonomous systems development is when small changes in factor settings result in large changes in a response’s performance. These occurrences look like cliffs in a metamodel’s response surface and are referred to as performance mode boundary regions. These regions represent areas of interest in the autonomous system’s decision-making process. Therefore, performance mode boundary regions are areas of interest for autonomous systems developers.Traditional augmentation methods aid experimenters seeking different objectives, often by improving a certain design property of the factor space (such as variance) or a design’s modeling capabilities. While useful, these augmentation techniques do not target areas of interest that need attention in autonomous systems testing focused on the response. Boundary Explorer Adaptive Sampling Technique, or BEAST, is a set of design augmentation algorithms. The adaptive sampling algorithm targets performance mode boundaries with additional samples. The gap filling augmentation algorithm targets sparsely sampled areas in the factor space. BEAST allows for sampling to adapt to information obtained from pervious iterations of experimentation and target these regions of interest. Exploiting the advantages of simulation model experimentation, BEAST can be used to provide additional iterations of experimentation, providing clarity and high-fidelity in areas of interest along potentially steep gradient regions. The objective of this thesis is to research and present BEAST, then compare BEAST’s algorithms to other design augmentation techniques. Comparisons are made towards traditional methods that are already implemented in SAS Institute’s JMP software, or emerging adaptive sampling techniques, such as Range Adversarial Planning Tool (RAPT). The goal of this objective is to gain a deeper understanding of how BEAST works and where it stands in the design augmentation space for practical applications. With a gained understanding of how BEAST operates and how well BEAST performs, future research recommendations will be presented to improve BEAST’s capabilities.

This thesis is developed in the context of biomanufacturing of modern products that have the following properties: require short design to manufacturing time, they have high variability due to a high desired level of patient personalization, and, as a result, may be manufactured in low volumes. This area at the intersection of therapeutics and biomanufacturing has become increasingly important: (i) a huge push toward the design of new RNA nanoparticles has revolutionized the science of vaccines due to the COVID-19 pandemic; (ii) while the technology to produce personalized cancer medications is available, efficient design and operation of manufacturing systems is not yet agreed upon. In this work, the focus is on operations research methodologies that can support faster design of novel products, specifically RNA; and methods for the enabling of personalization in biomanufacturing, and will specifically look at the problem of cancer therapy manufacturing. Across both areas, methods are presented attempting to embed pre-existing knowledge (e.g., constraints characterizing good molecules, comparison between structures) as well as learn problem structure (e.g., the landscape of the rewards function while synthesizing the control for a single use bioreactor). This thesis produced three key outcomes: (i) ExpertRNA for the prediction of the structure of an RNA molecule given a sequence. RNA structure is fundamental in determining its function. Therefore, having efficient tools for such prediction can make all the difference for a scientist trying to understand optimal molecule configuration. For the first time, the algorithm allows expert evaluation in the loop to judge the partial predictions that the tool produces; (ii) BioMAN, a discrete event simulation tool for the study of single-use biomanufacturing of personalized cancer therapies. The discrete event simulation engine was designed tailored to handle the efficient scheduling of many parallel events which is cause by the presence of single use resources. This is the first simulator of this type for individual therapies; (iii) Part-MCTS, a novel sequential decision-making algorithm to support the control of single use systems. This tool integrates for the first-time simulation, monte-carlo tree search and optimal computing budget allocation for managing the computational effort.

Reliability growth is not a new topic in either engineering or statistics and has been a major focus for the past few decades. The increasing level of high-tech complex systems and interconnected components and systems implies that reliability problems will continue to exist and may require more complex solutions. The most heavily used experimental designs in assessing and predicting a systems reliability are the "classical designs", such as full factorial designs, fractional factorial designs, and Latin square designs. They are so heavily used because they are optimal in their own right and have served superbly well in providing efficient insight into the underlying structure of industrial processes. However, cases do arise when the classical designs do not cover a particular practical situation. Repairable systems are such a case in that they usually have limitations on the maximum number of runs or too many varying levels for factors. This research explores the D-optimal design criteria as it applies to the Poisson Regression model on repairable systems, with a number of independent variables and under varying assumptions, to include the total time tested at a specific design point with fixed parameters, the use of a Bayesian approach with unknown parameters, and how the design region affects the optimal design. In applying experimental design to these complex repairable systems, one may discover interactions between stressors and provide better failure data. Our novel approach of accounting for time and the design space in the early stages of testing of repairable systems should, theoretically, in the final engineering design improve the system's reliability, maintainability and availability.

Photolithography is among the key phases in chip manufacturing. It is also among the most expensive with manufacturing equipment valued at the hundreds of millions of dollars. It is paramount that the process is run efficiently, guaranteeing high resource utilization and low product cycle times. A key element in the operation of a photolithography system is the effective management of the reticles that are responsible for the imprinting of the circuit path on the wafers. Managing reticles means determining which are appropriate to mount on the very expensive scanners as a function of the product types being released to the system. Given the importance of the problem, several heuristic policies have been developed in the industry practice in an attempt to guarantee that the expensive tools are never idle. However, such policies have difficulties reacting to unforeseen events (e.g., unplanned failures, unavailability of reticles). On the other hand, the technological advance of the semiconductor industry in sensing at system and process level should be harnessed to improve on these “expert policies”. In this thesis, a system for the real time reticle management is developed that not only is able to retrieve information from the real system, but also can embed commonly used policies to improve upon them. A new digital twin for the photolithography process is developed that efficiently and accurately predicts the system performance thus enabling predictions for the future behaviors as a function of possible decisions. The results demonstrate the validity of the developed model, and the feasibility of the overall approach demonstrating a statistically significant improvement of performance as compared to the current policy.

Longitudinal data involving multiple subjects is quite popular in medical and social science areas. I consider generalized linear mixed models (GLMMs) applied to such longitudinal data, and the optimal design searching problem under such models. In this case, based on optimal design theory, the optimality criteria depend on the estimated parameters, which leads to local optimality. Moreover, the information matrix under a GLMM doesn't have a closed-form expression. My dissertation includes three topics related to this design problem. The first part is searching for locally optimal designs under GLMMs with longitudinal data. I apply penalized quasi-likelihood (PQL) method to approximate the information matrix and compare several approximations to show the superiority of PQL over other approximations. Under different local parameters and design restrictions, locally D- and A- optimal designs are constructed based on the approximation. An interesting finding is that locally optimal designs sometimes apply different designs to different subjects. Finally, the robustness of these locally optimal designs is discussed. In the second part, an unknown observational covariate is added to the previous model. With an unknown observational variable in the experiment, expected optimality criteria are considered. Under different assumptions of the unknown variable and parameter settings, locally optimal designs are constructed and discussed. In the last part, Bayesian optimal designs are considered under logistic mixed models. Considering different priors of the local parameters, Bayesian optimal designs are generated. Bayesian design under such a model is usually expensive in time. The running time in this dissertation is optimized to an acceptable amount with accurate results. I also discuss the robustness of these Bayesian optimal designs, which is the motivation of applying such an approach.

Ultra-fast 2D/3D material microstructure reconstruction and quantitative structure-property mapping are crucial components of integrated computational material engineering (ICME). It is particularly challenging for modeling random heterogeneous materials such as alloys, composites, polymers, porous media, and granular matters, which exhibit strong randomness and variations of their material properties due to the hierarchical uncertainties associated with their complex microstructure at different length scales. Such uncertainties also exist in disordered hyperuniform systems that are statistically isotropic and possess no Bragg peaks like liquids and glasses, yet they suppress large-scale density fluctuations in a similar manner as in perfect crystals. The unique hyperuniform long-range order in these systems endow them with nearly optimal transport, electronic and mechanical properties. The concept of hyperuniformity was originally introduced for many-particle systems and has subsequently been generalized to heterogeneous materials such as porous media, composites, polymers, and biological tissues for unconventional property discovery. An explicit mixture random field (MRF) model is proposed to characterize and reconstruct multi-phase stochastic material property and microstructure simultaneously, where no additional tuning step nor iteration is needed compared with other stochastic optimization approaches such as the simulated annealing. The proposed method is shown to have ultra-high computational efficiency and only requires minimal imaging and property input data. Considering microscale uncertainties, the material reliability will face the challenge of high dimensionality. To deal with the so-called “curse of dimensionality”, efficient material reliability analysis methods are developed. Then, the explicit hierarchical uncertainty quantification model and efficient material reliability solvers are applied to reliability-based topology optimization to pursue the lightweight under reliability constraint defined based on structural mechanical responses. Efficient and accurate methods for high-resolution microstructure and hyperuniform microstructure reconstruction, high-dimensional material reliability analysis, and reliability-based topology optimization are developed. The proposed framework can be readily incorporated into ICME for probabilistic analysis, discovery of novel disordered hyperuniform materials, material design and optimization.

Monitoring a system for deviations from standard or reference behavior is essential for many data-driven tasks. Whether it is monitoring sensor data or the interactions between system elements, such as edges in a path or transactions in a network, the goal is to detect significant changes from a reference. As technological advancements allow for more data to be collected from systems, monitoring approaches should evolve to accommodate the greater collection of high-dimensional data and complex system settings. This dissertation introduces system-level models for monitoring tasks characterized by changes in a subset of system components, utilizing component-level information and relationships. A change may only affect a portion of the data or system (partial change). The first three parts of this dissertation present applications and methods for detecting partial changes. The first part introduces a methodology for partial change detection in a simple, univariate setting. Changes are detected with posterior probabilities and statistical mixture models which allow only a fraction of data to change. The second and third parts of this dissertation center around monitoring more complex multivariate systems modeled through networks. The goal is to detect partial changes in the underlying network attributes and topology. The contributions of the second and third parts are two non-parametric system-level monitoring techniques that consider relationships between network elements. The algorithm Supervised Network Monitoring (SNetM) leverages Graph Neural Networks and transforms the problem into supervised learning. The other algorithm Supervised Network Monitoring for Partial Temporal Inhomogeneity (SNetMP) generates a network embedding, and then transforms the problem to supervised learning. At the end, both SNetM and SNetMP construct measures and transform them to pseudo-probabilities to be monitored for changes. The last topic addresses predicting and monitoring system-level delays on paths in a transportation/delivery system. For each item, the risk of delay is quantified. Machine learning is used to build a system-level model for delay risk, given the information available (such as environmental conditions) on the edges of a path, which integrates edge models. The outputs can then be used in a system-wide monitoring framework, and items most at risk are identified for potential corrective actions.

Nonregular designs are a preferable alternative to regular resolution four designs because they avoid confounding two-factor interactions. As a result nonregular designs can estimate and identify a few active two-factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard screening strategies can fail to identify all active effects. In this research, two-level nonregular screening designs with orthogonal main effects will be discussed. By utilizing knowledge of the alias structure, a design based model selection process for analyzing nonregular designs is proposed.

The Aliased Informed Model Selection (AIMS) strategy is a design specific approach that is compared to three generic model selection methods; stepwise regression, least absolute shrinkage and selection operator (LASSO), and the Dantzig selector. The AIMS approach substantially increases the power to detect active main effects and two-factor interactions versus the aforementioned generic methodologies. This research identifies design specific model spaces; sets of models with strong heredity, all estimable, and exhibit no model confounding. These spaces are then used in the AIMS method along with design specific aliasing rules for model selection decisions. Model spaces and alias rules are identified for three designs; 16-run no-confounding 6, 7, and 8-factor designs. The designs are demonstrated with several examples as well as simulations to show the AIMS superiority in model selection.

A final piece of the research provides a method for augmenting no-confounding designs based on a model spaces and maximum average D-efficiency. Several augmented designs are provided for different situations. A final simulation with the augmented designs shows strong results for augmenting four additional runs if time and resources permit.

In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible cost. Designing an optimal supply chain by optimizing supply chain operations and decisions is key to achieving these goals.

In this research, a capacity planning and production scheduling mathematical model for a multi-facility and multiple product supply chain network with significant capital and labor costs is first proposed. This model considers the key levers of capacity configuration at production plants namely, shifts, run rate, down periods, finished goods inventory management and overtime. It suggests a minimum cost plan for meeting medium range demand forecasts that indicates production and inventory levels at plants by time period, the associated manpower plan and outbound shipments over the planning horizon. This dissertation then investigates two model extensions: production flexibility and pricing. In the first extension, the cost and benefits of investing in production flexibility is studied. In the second extension, product pricing decisions are added to the model for demand shaping taking into account price elasticity of demand.





The research develops methodologies to optimize supply chain operations by determining the optimal capacity plan and optimal flows of products among facilities based on a nonlinear mixed integer programming formulation. For large size real life cases the problem is intractable. An alternate formulation and an iterative heuristic algorithm are proposed and tested. The performance and bounds for the heuristic are evaluated. A real life case study in the automotive industry is considered for the implementation of the proposed models. The implementation results illustrate that the proposed method provides valuable insights for assisting the decision making process in the supply chain and provides significant improvement over current practice.

The main objective of this research is to develop reliability assessment methodologies to quantify the effect of various environmental factors on photovoltaic (PV) module performance degradation. The manufacturers of these photovoltaic modules typically provide a warranty level of about 25 years for 20% power degradation from the initial specified power rating. To quantify the reliability of such PV modules, the Accelerated Life Testing (ALT) plays an important role. But there are several obstacles that needs to be tackled to conduct such experiments, since there has not been enough historical field data available. Even if some time-series performance data of maximum output power (Pmax) is available, it may not be useful to develop failure/degradation mode-specific accelerated tests. This is because, to study the specific failure modes, it is essential to use failure mode-specific performance variable (like short circuit current, open circuit voltage or fill factor) that is directly affected by the failure mode, instead of overall power which would be affected by one or more of the performance variables. Hence, to address several of the above-mentioned issues, this research is divided into three phases. The first phase deals with developing models to study climate specific failure modes using failure mode specific parameters instead of power degradation. The limited field data collected after a long time (say 18-21 years), is utilized to model the degradation rate and the developed model is then calibrated to account for several unknown environmental effects using the available qualification testing data. The second phase discusses the cumulative damage modeling method to quantify the effects of various environmental variables on the overall power production of the photovoltaic module. Mainly, this cumulative degradation modeling approach is used to model the power degradation path and quantify the effects of high frequency multiple environmental input data (like temperature, humidity measured every minute or hour) with very sparse response data (power measurements taken quarterly or annually). The third phase deals with optimal planning and inference framework using Iterative-Accelerated Life Testing (I-ALT) methodology. All the proposed methodologies are demonstrated and validated using appropriate case studies.