in the real world, risk and uncertainty are two natural properties in the implementation of mega projects. most projects fail to achieve the pre-determined objectives due to uncertainty. a linear integer programming optimization model was used in this work to solve a problem in order to choose the most appropriate risk responses for the project risks. a mathematical model, in which work structu...

Optimization problems due to noisy data are usually solved using stochastic programming or robust optimization approaches. Both requiring the explicit characterization of an uncertainty set that models the nature of the noise. Such approaches tightly depend on the modeling of the uncertainty set. In this paper, we introduce a framework that implicitly models the uncertain data. We define the ge...

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...

this paper proposes a family of robust counterpart for uncertain linear programs (lp) which is obtained for a general definition of the uncertainty region. the relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. those properties lead us to characterize primal and dual robust counterparts. the researchers show t...

This paper proposes a stochastic framework for demand response (DR) aggregator to procure DR from customers and sell it to purchasers in the wholesale electricity market. The aggregator assigns fixed DR contracts with customers based on three different load reduction strategies. In the presented problem the uncertainty of market price is considered and the risk of aggregator participation is ma...