We consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new adaptive extensions of the Nelder and Mead simplex method designed to improve the accuracy and consistency of the observed best point are studied. We compare the performance of the extensions on a small microsimulation model, as well as on ve test functions. We found that gradua...

In this second portion of a two-part analysis of a computational approach to scalable stochastic unit commitment, we transition our focus from approximating accurate stochastic process models of load to solving the resulting stochastic optimization models in tractable run-times. Our solution technique is based on Rockafellar and Wets’ progressive hedging algorithm, a scenario-based decompositio...

In multidisciplinary analysis and optimization response surface approximations are frequently applied. An important reason is that response surface techniques provide a convenient representation of data from one discipline to other disciplines [1]. In each discipline usually one or more computationally expensive computer simulation models are involved. The response surface approximations are us...

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. This leads to a robust stochastic design framework where probabilistic mo...

Stochastic compositional optimization generalizes classic (non-compositional) stochastic to the minimization of compositions functions. Each composition may introduce an additional expectation. The series expectations be nested. is gaining popularity in applications such as reinforcement learning and meta learning. This paper presents a new Stochastically Corrected Compositional gradient method...

In this paper, we propose an extended relocation model for warehouses configuration in a supply chain network, in which uncertainty is associated to operational costs, production capacity and demands whereas, existing researches in this area are often restricted to deterministic environments. In real cases, we usually deal with stochastic parameters and this point justifies why the relocation m...

In this work we propose a stochastic model for the design and planning of closed-loop supply chains. Uncertainties in demand and return volumes are modelled together with uncertain transportation costs. A two-stage stochastic programming is developed and a sensitivity analysis to the worst-case probability is performed in order to test the solution robustness. Finally, in order to prove the goo...

This paper presents an asset liability management model based on robust optimization techniques. The model explicitly takes into consideration the time-varying aspect of investment opportunities. The emphasis of the proposed approach is on computational tractability and practical appeal. Computational studies with real market data study the performance of robust-optimization-based strategies, a...