An Interactive Refinement Methodology for Early-Stage Exploration of Design Space during System Design

摘要: Abstract The design of large engineering systems requires the quick exploration space. Though simulation models have been used to support optimal individual subsystems, similar approaches cannot be for system design, because difficulties in using disparate different subsystems and perspectives together, addition ensuing excessive computational requirements. This paper presents a methodology called Hierarchical Interactive Decision Refinement (HIDER), which combines machine learning based adaptive modeling, multiple-objective optimization, interactive refinement techniques provide decision quickly exploring also results from application HIDER wheel loader. In this example, complex cycle time analysis stability are loader simultaneously these two perspectives.