Active Robust Optimization: Optimizing for Robustness of Changeable Products

Abstract

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.iii Statement of OriginalityUnless otherwise stated in the text, the work described in this thesis was carried out solely by the candidate. None of this work has already been

Evaluation of steel turning by means of probability-based multi-objective optimization with appropriate numbers of attributes

“…The advantages of PMOO are its probabilistic foundation in view of the system theory, rationality and certainty of its solution without any artificial factors, and a simple and convenient algorithm in mathematical treatment, which are obviously superior to other methods of multiobjective optimization such as the Analytic Hierarchy Process (AHP), the Vlšekriterijumsko KOmpromisno Rangiranje (VIKOR), the Technique of Ranking Preferences by Similarity to the Ideal Solution (TOPSIS), Multi-Objective Optimization (MOO) on the basis of the Ratio Analysis (MOORA) , the Pareto solution, the Grey Relational Analysis (GRA), etc. (Zheng et al, 2024;Salomon, 2019). Besides, this approach is superior regarding simplicity in data processing to other metaheuristics.…”