Business Commons^™

Mathematical Modeling For Platform-Based Product Configuration Considering Total Life-Cycle Sustainability, Tian Lan

Theses and Dissertations--Mechanical Engineering

Many companies are using platform-based product designs to fulfill the requirements of customers while maintaining low cost. However, research that integrates sustainability into platform-based product design is still limited. Considering sustainability during platform-based design process is a challenge because the total life-cycle from pre-manufacturing, manufacturing and use to post-use stages as well as economic, environmental and societal performance in these stages must be considered. In this research, an approach for quantifying sustainability is introduced and a mathematical model is developed for identifying a more sustainable platform. Data from life-cycle assessment is used to quantify environmental factors; criteria from the Product …

A Framework For Sustainable Material Selection For Multi-Generational Components, Ryan T. Bradley

Theses and Dissertations--Mechanical Engineering

The early stages of a product’s design are a critical time for decisions that impact the entire life-cycle cost. Product designers have mastered the first generation; however, they currently do not have the ability to know the impact of their decisions on the multi-generational view. This thesis aims at closing the gap between total life-cycle information and the traditional design process in order to harbor sustainable value creation among all stakeholders involved. A framework is presented that uses a combination of a life-cycle costing methodology and an evolutionary algorithm in order to achieve a sustainability assessment for a true multi-generational …

Data Privacy Preservation In Collaborative Filtering Based Recommender Systems, Xiwei Wang

Theses and Dissertations--Computer Science

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives.

The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy.

It is apparent that with more customers' preference data, recommender systems …