Active Robust Optimization - Optimizing for Robustness of Changeable Products

To succeed in a demanding and competitive market, great attention needs to be given to the process of product design. Incorporating optimization into the process enables the designer to find high-quality products according to their simulated performance. However, the actual performance may differ from the simulation results due to a variety of uncertainty factors. Robust optimization is commonly used to search for products that are less affected by the anticipated uncertainties. Changeability can improve the robustness of a product, as it allows the product to be adapted to a new configuration whenever the uncertain conditions change. This ability provides the changeable product with an active form of robustness. Several methodologies exist for engineering design of changeable products, none of which includes optimization. This study presents the Active Robust Optimization (ARO) framework that offers the missing tools for optimizing changeable products. A new optimization problem is formulated, named Active Robust Optimization Problem (AROP). The benefit in designing solutions by solving an AROP lies in the realistic manner adaptation is considered when assessing the solutions' performance. The novel methodology can be applied to optimize any product that can be classified as a changeable product, i.e., it can be adjusted by its user during normal operation. This definition applies to a huge variety of applications, ranging from simple products such as fans and heaters, to complex systems such as production halls and transportation systems. The ARO framework is described in this dissertation and its unique features are studied. Its ability to find robust changeable solutions is examined for different sources of uncertainty, robustness criteria and sampling conditions. Additionally, a framework for Active Robust Multi-objective Optimization is developed. This generalisation of ARO itself presents many challenges, not encountered in previous studies. Novel approaches for evaluating and comparing changeable designs comprising multiple objectives are proposed along with algorithms for solving multi-objective AROPs. The framework and associated methodologies are demonstrated on two applications from different fields in engineering design. The first is an adjustable optical table, and the second is the selection of gears in a gearbox

Gearbox design for uncertain load requirements using active robust optimization

Design and optimization of gear transmissions have been intensively studied, but surprisingly the robustness of the resulting optimal design to uncertain loads has never been considered. Active Robust (AR) optimization is a methodology to design products that attain robustness to uncertain or changing environmental conditions through adaptation. In this study the AR methodology is utilized to optimize the number of transmissions, as well as their gearing ratios, for an uncertain load demand. The problem is formulated as a bi-objective optimization problem where the objectives are to satisfy the load demand in the most energy efficient manner and to minimize production cost. The results show that this approach can find a set of robust designs, revealing a trade-off between energy efficiency and production cost. This can serve as a useful decision-making tool for the gearbox design process, as well as for other applications

Evaluation of Modified PEG-Anilinoquinazoline Derivatives as Potential Agents for EGFR Imaging in Cancer by Small Animal PET

Purpose: The in vivo evaluation of three modified polyethylene glycol (PEG)-anilinoquinazoline derivatives labeled with 124 I, 18 F, and 11 C as potential positron emission tomography (PET) bioprobes for visualizing epidermal growth factor receptor (EGFR) in cancer using small animal PET. Procedures: Xenograft mice with the human glioblastoma cell lines U138MG (lacking EGFR expression) and U87MG.wtEGFR (transfected with an overexpressing human wild-type EGFR gene) were used. Static and dynamic PET imaging was conducted for all three PEGylated compounds. Tumor necrosis, microvessel density, and EGFR levels were evaluated by histopathology and enzyme-linked immunosorbent assay. Results: Nineteen animal models were generated (two U138MG, three U87MG, 14 with both U138MG and U87MG bilateral masses). In static images, a slight increase in tracer uptake was observed in tumors, but in general, there was no retention of tracer uptake over time and no difference in uptake between U138MG and U87MG masses. In addition, no significant uptake was demonstrated in dynamic scans of the 18 F-PEG tracer. No necrosis was present except in four animals. MVD was 9.6 and 48 microvessels/×400 field in the U138GM and U87GM masses