This article presents a framework for simulation-based design optimization of computationally expensive problems, where economizing the generation of sample designs is highly desirable. One popular approach for such problems is efficient global optimization (EGO), where an initial set of design samples is used to construct a kriging model, which is then used to generate new ‘infill’ sample designs at regions of the search space where there is high expectancy of improvement. This article attempts to address one of the limitations of EGO, where generation of infill samples can become a difficult optimization problem in its own right, as well as allow the generation of multiple samples at a time in order to take advantage of parallel computing in the evaluation of the new samples. The proposed approach is tested on analytical functions, and then applied to the vehicle crashworthiness design of a full Geo Metro model undergoing frontal crash conditions.

This paper considers multiobjective optimization under uncertainty (MOOUC) for the selection of optimal cutting conditions in advanced abrasive machining (AAM) processes. Processes considered are water jet machining (WJM), abrasive water jet machining (AWJM), and ultrasonic machining (USM). Decisions regarding the cutting conditions can involve optimization for multiple competing goals, such as surface finish, machining time, and power consumption. In practice, there is also an issue of variations in the ability to attain the performance goals. This can be due to limitations in machine accuracy or variations in material properties of the workpiece and/or abrasive particles. The approach adopted in this work relies on a strength Pareto evolutionary algorithm (SPEA2) framework, with specially tailored dominance operators to account for probabilistic aspects in the considered multiobjective problem. Deterministic benchmark problems in the literature for the considered machining processes are extended to include performance uncertainty and then used in testing the performance of the proposed approach. Results of the study show that accounting for process variations through a simple penalty term may be detrimental for the multiobjective optimization. On the other hand, a proposed fuzzy-tournament dominance operator appears to produce favorable results.

Plug-in vehicles (PEVs), which include battery-only electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have steadily grown in sales amidst various incentive programs, but much speculation exists on when PEVs would become cost-competitive without incentives. This research adopts a bottom-up approach for estimation of purchase cost, and total cost of ownership (TCO). Baseline predictions, as well as sensitivity analysis (with more favorable conditions for PEVs) are generated for 2030. Results show that the five-year TCO of some PEVs could be less than an equivalent-sized conventional internal combustion-engine (CICE) vehicle, but only in the more optimistic scenarios where the cost of batteries and motors decrease more rapidly than the baseline prediction, and when combined with either higher gasoline prices or longer annual distance travelled. However, without subsidies or incentives, purchase cost parity between PEVs and CICEs was not realized in any of the considered 2030 scenarios.