This paper studies the following line balancing problem with uncertain operation execution times. Operations on the same product have to be assigned to the stations of a transfer line. The product moves along the stations in the same direction, and operations assigned to the same station are executed sequentially. Exclusion, inclusion and precedence relations are given on the set of operations....

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...

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization, one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to singlestage optimization. However, this ...

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to single-stage optimization. However, this ...

in this paper a new strategy is proposed to design a fixed-structure robust controller for a flexible beam.  robust controller designed by the conventional  loop­ shaping method is not appropriate for a beam because of its high order and complicated form. fixed-structure  loop shaping control in conjunction with particle swarm optimization (pso)algorithm  is used to overcome this drawback. the ...

In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used technique for computing smaller number scenarios to improve computational tractability and interpretability. However traditional approaches do not consider decision quality when these scenarios. “Optimization-Based Scenario Reduction Data-Driven Two-Stage Stochastic Optimization,” Bertsimas Mundru ...

Robust optimization (RO) is a distribution-freeworst-case solutionmethodology designed for uncertain maximization problems via a max-min approach considering a bounded uncertainty set. It yields a feasible solution over this set with a guaranteed worst-case value. As opposed to a previous conception that RO is conservative based on optimal value analysis, we argue that in practice the uncertain...

Many groundwater remediation designs for contaminant plume containment are developed using mathematically based groundwater now models. These mathematical models are most eiTective as predictive tools when the parameters that govern groundwater flow are known with a high degree of certainty. The hydraulic conductivity of an aquifer, however, is uncertain, and so remediation designs developed lI...