Model-Based Optimization of a Hydraulic Backhoe using Multi-Attribute Utility Theory

Modeling and simulation are commonly used in all stages of the design process. This is particularly vital to the success of systems engineering projects where the system under consideration is complex and involves interactions between many interdisciplinary subsystems. In the refining stages of the design process (after concept selection), models and simulations can be used to refine and optimize a system with respect to the decision maker’s objectives. In this paper, a dynamic model of a hydraulic backhoe serves as a test-bed for a large-scale sensitivity analysis and subsequent optimization of the most significant design parameters. The model is optimized under uncertainty with respect to a multi-attribute utility function that includes fuel consumption, cost of the key components, and machine performance. Since such an optimization can be costly in terms of time and computational resources, the objective of this paper is to provide a useful, cost-effective methodology for this type of problem. To perform the optimization, a parallel computing cluster is used in conjunction with a kriging surrogate model to reduce computation time.