Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

Abstract

In a decision‐making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade‐offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near‐optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. We propose a new design selection strategy that identifies several top‐ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.

Ignorance‐aware safety and reliability analysis: A heuristic approach

“…Recently, many scholars have noticed this situation and introduced varieties of MCDM methods. For example, Burke et al proposed a novel design selection strategy using multiple criteria with enhanced robustness of user priorities . Sun et al introduced a novel expert investigation with consideration of small probability intervals in order to assess the risk of tunnel collapse .…”

“…For example, Burke et al proposed a novel design selection strategy using multiple criteria with enhanced robustness of user priorities. 22 Sun et al introduced a novel expert investigation with consideration of small probability intervals in order to assess the risk of tunnel collapse. 23 A risk matrix is developed using an extension TOPSIS method and an intuitionistic fuzzy number by Yazdi to enhance the performance of a safety system in a refinery under construction.…”