Open-Pit Production Scheduling (OPPS) problem focuses on determining a block sequencing and scheduling to maximize Net Present Value (NPV) of the venture under constraints. The scheduling model is critically sensitive to the economic value volatility of block, block weight, and operational capacity. In order to deal with the OPPS uncertainties, various approaches can be recommended. Robust opti...

Abstract In the present paper we discussed the parameters optimization in medical decision making using maximum entropy weight. Currently, most medical decision models rely on point estimates for input parameters, although the uncertainty surrounding these values is well-recognized. However, it still left some challenge problems that are commonly involved in computational problems involving exp...

Random fields play a central role in the analysis of spatially correlated data and, as a result, have a significant impact on a broad array of scientific applications. This paper studies the cepstral random field model, providing recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the autocovarianc...

In this paper, we introduce a model  to optimization of milk run system that is one of VRP problem with time window and uncertainty in inventory. This approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .The problem is formulated as a robust optimization problem. Since the resulted problem illustrates that grows up ...

The paper introduces the worst-case portfolio optimization models within robust framework for maximizing return through either mean or median metrics. risk in is quantified by Gini difference. We put forward under mixed and interval+polyhedral uncertainty sets. proposed turn out to be linear integer programs set, semidefinite program set. performance comparison of on listed stocks Euro Stoxx 50...

This study has been inspired by the paper "An efficient 3D topology optimization code written in MATLAB” written by Liu and Tovar (2014) demonstrating that SIMP-based three-dimensional (3D) topology optimization of continuum structures can be implemented in 169 lines of MATLAB code. Based on the above paper, we show here that, by simple and easy-to-understand modificati...

The last decade has seen an explosion in the availability of data. In this thesis, we propose new techniques to leverage these data to tractably model uncertainty and behavior. Specifically, this thesis consists of three parts: In the first part, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using hypothesis testing. The approach is flexible and...

Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this paper, we introduce a framework that considers the uncertain dat...

We consider maximum likelihood estimation of linear dynamical systems with generalized-linear observation models. Maximum likelihood is typically considered to be hard in this setting since latent states and transition parameters must be inferred jointly. Given that expectation-maximization does not scale and is prone to local minima, moment-matching approaches from the subspace identification ...

In Part II of this two-part paper, we analyze the marginal prices derived in Part I of this two-part paper within a robust optimization framework. The load and generation are priced at Locational Marginal Price (LMP) while the uncertainty and generation reserve are priced at Uncertainty Marginal Price (UMP). The Financial Transmission Right (FTR) underfunding is demonstrated when there is trans...