Capacity planning methodology for concurrent engineer -to -order operations

This research focuses on developing a capacity planning methodology for the emerging concurrent engineer-to-order (ETO) operations. The primary focus is placed on the capacity planning at sales stage. This study examines the characteristics of capacity planning in a concurrent ETO operation environment, models the problem analytically, and proposes a practical capacity planning methodology for concurrent ETO operations in the industry. A computer program that mimics a concurrent ETO operation environment was written to validate the proposed methodology and test a set of rules that affect the performance of a concurrent ETO operation. ^ This study takes a systems engineering approach to the problem and employs systems engineering concepts and tools for the modeling and analysis of the problem, as well as for developing a practical solution to this problem. This study depicts a concurrent ETO environment in which capacity is planned. The capacity planning problem is modeled into a mixed integer program and then solved for smaller-sized applications to evaluate its validity and solution complexity. The objective is to select the best set of available jobs to maximize the profit, while having sufficient capacity to meet each due date expectation. ^ The nature of capacity planning for concurrent ETO operations is different from other operation modes. The search for an effective solution to this problem has been an emerging research field. This study characterizes the problem of capacity planning and proposes a solution approach to the problem. This mathematical model relates work requirements to capacity over the planning horizon. The methodology is proposed for solving industry-scale problems. Along with the capacity planning methodology, a set of heuristic rules was evaluated for improving concurrent ETO planning. ^

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System

Thesis (Ph.D.)--University of Washington, 2016-08

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.

Asymmetries in Adversarial Settings

Even without formal guarantees of their effectiveness, adversarial attacks against Machine Learning models frequently fool new defenses. We identify six key asymmetries that contribute to this phenomenon and formulate four guidelines to build future-proof defenses by preventing such asymmetries. We also prove that attacking a classifier is NP-complete, while defending from such attacks is Sigma_2^P-complete. We then introduce Counter-Attack (CA), an asymmetry-free metadefense that determines whether a model is robust on a given input by estimating its distance from the decision boundary. Under specific assumptions CA can provide theoretical detection guarantees. Additionally, we prove that while CA is NP-complete, fooling CA is Sigma_2^P-complete. Even when using heuristic relaxations, we show that our method can reliably identify non-robust points. As part of our experimental evaluation, we introduce UG100, a new dataset obtained by applying a provably optimal attack to six limited-scale networks (three for MNIST and three for CIFAR10), each trained in three different manners.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil

The Cerrado and Amazon regions of Brazil are probably the largest agricultural frontier of the world, and Could be a sink or source for C depending on the net effect of land use change and subsequent management on soil organic C pools. We evaluated the effects of agricultural management systems on soil organic C (SOC) stocks in the Brazilian states of Rondonia and Mato Grosso, and derived regional specific factors for soil C stock change associated with different management systems. We used 50 observations (data points) in this study, including 42 dealing with annual cropping practices and 8 dealing with perennial cropping, and analyzed the data in linear mixed-effect models. No tillage (NT) systems in Cerrado areas increased SOC Storage by 1.08 +/- 0.06 relative to SOC stocks under native conditions, while SOC storage increased by a modest factor of 1.01 +/- 0.17 in Cerradao and Amazon Forest conditions. Full tillage (FT) had negative effect on SOC storage relative to NT, decreasing SOC stocks by a factor of 0.94 +/- 0.04. but did not significantly reduce SOC stocks relative to native levels when adopted in the Cerrado region. Perennial cropping had a minimal impact on SOC stocks, estimated at a factor Value of 0.98 +/- 0.14, suggesting these systems maintain about 98% of the SOC stock found under native vegetation. The results Suggest that NT adoption may be increasing SOC with land use change from native vegetation to cropland management in the Cerrado region of Brazil. (C) 2009 Elsevier B.V. All rights reserved.

Relationship between median nerve somatosensory evoked potentials and spinal cord injury levels in patients with quadriplegia

Study design: Cross-sectional study. Objectives: To observe if there is a relationship between the level of injury by the American Spinal Cord Injury Association (ASIA) and cortical somatosensory evoked potential (SSEP) recordings of the median nerve in patients with quadriplegia. Setting: Rehabilitation Outpatient Clinic at the university hospital in Brazil. Methods: Fourteen individuals with quadriplegia and 8 healthy individuals were evaluated. Electrophysiological assessment of the median nerve was performed by evoked potential equipment. The injury level was obtained by ASIA. N(9), N(13) and N(20) were analyzed based on the presence or absence of responses. The parameters used for analyzing these responses were the latency and the amplitude. Data were analyzed using mixed-effect models. Results: N(9) responses were found in all patients with quadriplegia with a similar latency and amplitude observed in healthy individuals; N(13) responses were not found in any patients with quadriplegia. N(20) responses were not found in C5 patients with quadriplegia but it was present in C6 and C7 patients. Their latencies were similar to healthy individuals (P > 0.05) but the amplitudes were decreased (P < 0.05). Conclusion: This study suggests that the SSEP responses depend on the injury level, considering that the individuals with C6 and C7 injury levels, both complete and incomplete, presented SSEP recordings in the cortical area. It also showed a relationship between the level of spinal cord injury assessed by ASIA and the median nerve SSEP responses, through the latency and amplitude recordings. Spinal Cord (2009) 47, 372-378; doi:10.1038/sc.2008.147; published online 20 January 2009

Optimal synthesis of Fenton reactor networks for phenol degradation

There is an increasing need to treat effluents contaminated with phenol with advanced oxidation processes (AOPs) to minimize their impact on the environment as well as on bacteriological populations of other wastewater treatment systems. One of the most promising AOPs is the Fenton process that relies on the Fenton reaction. Nevertheless, there are no systematic studies on Fenton reactor networks. The objective of this paper is to develop a strategy for the optimal synthesis of Fenton reactor networks. The strategy is based on a superstructure optimization approach that is represented as a mixed integer non-linear programming (MINLP) model. Network superstructures with multiple Fenton reactors are optimized with the objective of minimizing the sum of capital, operation and depreciation costs of the effluent treatment system. The optimal solutions obtained provide the reactor volumes and network configuration, as well as the quantities of the reactants used in the Fenton process. Examples based on a case study show that multi-reactor networks yield decrease of up to 45% in overall costs for the treatment plant. (C) 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Model for facilities or vendors location in a global scale considering several echelons in the Chain

The facilities location problem for companies with global operations is very complex and not well explored in the literature. This work proposes a MILP model that solves the problem through minimization of the total logistic cost. Main contributions of the model are the pioneer carrying cost calculation, the treatment given to the take-or-pay costs and to the international tax benefits such as drawback and added value taxes in Brazil. The model was successfully applied to a real case of a chemical industry with industrial plants and sales all over the world. The model application recommended a totally new sourcing model for the company.

Optimized harvest scheduling in eucalyptus plantations under operational, spatial and climatic constraints

The representation of sustainability concerns in industrial forests management plans, in relation to environmental, social and economic aspects, involve a great amount of details when analyzing and understanding the interaction among these aspects to reduce possible future impacts. At the tactical and operational planning levels, methods based on generic assumptions usually provide non-realistic solutions, impairing the decision making process. This study is aimed at improving current operational harvesting planning techniques, through the development of a mixed integer goal programming model. This allows the evaluation of different scenarios, subject to environmental and supply constraints, increase of operational capacity, and the spatial consequences of dispatching harvest crews to certain distances over the evaluation period. As a result, a set of performance indicators was selected to evaluate all optimal solutions provided to different possible scenarios and combinations of these scenarios, and to compare these outcomes with the real results observed by the mill in the study case area. Results showed that it is possible to elaborate a linear programming model that adequately represents harvesting limitations, production aspects and environmental and supply constraints. The comparison involving the evaluated scenarios and the real observed results showed the advantage of using more holistic approaches and that it is possible to improve the quality of the planning recommendations using linear programming techniques.

Class attainment among British men - A multivariate extension of the CASMIN model of intergenerational class mobility

The CASMIN Project is arguably the most influential contemporary study of class mobility in the world. However, CASMIN results with respect to weak vertical status effects on class mobility have been extensively criticized. Drawing on arguments about how to model vertical mobility, Hout and Hauser (1992) show that class mobility is strongly determined by vertical socioeconomic differences. This paper extends these arguments by estimating the CASMIN model while explicitly controlling for individual determinants of socioeconomic attainment. Using the 1972 Oxford Mobility Data and the 1979 and 1983 British Election Studies, the paper employs mixed legit models to show how individual socioeconomic factors and categorical differences between classes shape intergenerational mobility. The findings highlight the multidimensionality of class mobility and its irreducibility to vertical movement up and down a stratification hierarchy.

The association between air pollution and blood pressure in traffic controllers in Santo Andre, Sao Paulo, Brazil

Background: Urban air pollutants are associated with cardiovascular events. Traffic controllers are at high risk for pollution exposure during outdoor work shifts. Objective: The purpose of this study was to evaluate the relationship between air pollution and systemic blood pressure in traffic controllers during their work shifts. Methods: This cross-sectional study enrolled 19 male traffic controllers from Santo Andre city (Sao Paulo, Brazil) who were 30-60 years old and exposed to ambient air during outdoor work shifts. Systolic and diastolic blood pressure readings were measured every 15 min by an Ambulatory Arterial Blood Pressure Monitoring device. Hourly measurements (lags of 0-5 h) and the moving averages (2-5 h) of particulate matter (PM(10)), ozone (O(3)) ambient concentrations and the acquired daily minimum temperature and humidity means from the Sao Paulo State Environmental Agency were correlated with both systolic and diastolic blood pressures. Statistical methods included descriptive analysis and linear mixed effect models adjusted for temperature, humidity, work periods and time of day. Results: Interquartile increases of PM(10) (33 mu g/m(3)) and O(3) (49 mu g/m(3)) levels were associated with increases in all arterial pressure parameters, ranging from 1.06 to 2.53 mmHg. PM(10) concentration was associated with early effects (lag 0), mainly on systolic blood pressure. However, O(3) was weakly associated most consistently with diastolic blood pressure and with late cumulative effects. Conclusions: Santo Andre traffic controllers presented higher blood pressure readings while working their outdoor shifts during periods of exposure to ambient pollutant fluctuations. However, PM(10) and O(3) induced cardiovascular effects demonstrated different time courses and end-point behaviors and probably acted through different mechanisms. (C) 2011 Elsevier Inc. All rights reserved.

Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs.

In population pharmacokinetic studies, the precision of parameter estimates is dependent on the population design. Methods based on the Fisher information matrix have been developed and extended to population studies to evaluate and optimize designs. In this paper we propose simple programming tools to evaluate population pharmacokinetic designs. This involved the development of an expression for the Fisher information matrix for nonlinear mixed-effects models, including estimation of the variance of the residual error. We implemented this expression as a generic function for two software applications: S-PLUS and MATLAB. The evaluation of population designs based on two pharmacokinetic examples from the literature is shown to illustrate the efficiency and the simplicity of this theoretic approach. Although no optimization method of the design is provided, these functions can be used to select and compare population designs among a large set of possible designs, avoiding a lot of simulations.