A novel data-driven approach for optimization under uncertainty based on multistage adaptive robust optimization (ARO) and nonparametric kernel density M-estimation is proposed. Different from conventional robust optimization methods, the proposed framework incorporates distributional information to avoid over-conservatism. Robust kernel density estimation with Hampel loss function is employed ...

Robust counterpart optimization techniques are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geome...

Metamaterials have recently emerged in the search for lightweight noise and vibration solutions. One of their appealing properties control engineering is ability to create stop bands, which are frequency ranges without free wave propagation. These bands arise from sub-wavelength addition identically tuned resonators or on a host structure. However, when manufacturing metamaterials, variability ...

Robust optimization considers optimization problems with uncertainty in the data. The common data model assumes that the uncertainty can be represented by an uncertainty set. Classic robust optimization considers the solution under the worst case scenario. The resulting solutions are often too conservative, e.g., they have high costs compared to non-robust solutions. This is a reason for the de...

Spatial decision support systems (SDSS) are designed to make complex resource allocation problems more transparent and to support the design and evaluation of allocation plans. Recent developments in this field focus on the design of allocation plans using optimization techniques. In this paper we analyze how uncertainty in spatial (input) data propagates through, and affects the results of, an...

Multidisciplinary design optimization (MDO) is a useful technique on complex product design in recent years. Collaborative optimization (CO) is an effective MDO methods based decomposition which is for deterministic optimization. However, many uncertainties exist in product design such as model error and design variables error. And the propagation of uncertainties in multidisciplinary is more c...

Chaotic systems are characterized by long-term unpredictability. Existing methods designed to estimate and forecast such systems, such as Extended Kalman filtering (a “sequential” or “incremental” matrix-based approach) and 4Dvar (a “variational” or “batch” vector-based approach), are essentially based on the assumption that Gaussian uncertainty in the initial state, state disturbances, and mea...

Chaotic systems are characterized by long-term unpredictability. Existing methods designed to estimate and forecast such systems, such as Extended Kalman filtering (a “sequential” or “incremental” matrix-based approach) and 4DVar (a “variational” or “batch” vector-based approach), are essentially based on the assumption that Gaussian uncertainty in the initial state, state disturbances, and mea...