3 resultados para Multicultural and Diversity

em Duke University


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An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

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Studies of adaptive divergence have traditionally focused on the ecological causes of trait diversification, while the ecological consequences of phenotypic divergence remain relatively unexplored. Divergence in predator foraging traits, in particular, has the potential to impact the structure and dynamics of ecological communities. To examine the effects of predator trait divergence on prey communities, we exposed zooplankton communities in lake mesocosms to predation from either anadromous or landlocked (freshwater resident) alewives, which have undergone recent and rapid phenotypic differentiation in foraging traits (gape width, gill raker spacing, and prey size-selectivity). Anadromous alewives, which exploit large prey items, significantly reduced the mean body size, total biomass, species richness, and diversity of crustacean zooplankton relative to landlocked alewives, which exploit smaller prey. The zooplankton responses observed in this experiment are consistent with patterns observed in lakes. This study provides direct evidence that phenotypic divergence in predators, even in its early stages, can play a critical role in determining prey community structure.

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Advances in technology, communication, and transportation over the past thirty years have led to tighter linkages and enhanced collaboration across traditional borders between nations, institutions, and cultures. This thesis uses the furniture industry as a lens to examine the impacts of globalization on individual countries and companies as they interact on an international scale. Using global value chain analysis and international trade data, I break down the furniture production process and explore how countries have specialized in particular stages of production to differentiate themselves from competitors and maximize the benefits of global involvement. Through interviews with company representatives and evaluation of branding strategies such as advertisements, webpages, and partnerships, I investigate across four country cases how furniture companies construct strong brands in an effort to stand out as unique to consumers with access to products made around the globe. Branding often serves to highlight distinctiveness and associate companies with national identities, thus revealing that in today’s globalized and interconnected society, local differences and diversity are more significant than ever.