We develop a tractable and flexible data-driven approach for incorporating side information into multi-stage stochastic programming. The proposed framework uses predictive machine learning methods (such as k-nearest neighbors, kernel regression, random forests) to weight the relative importance of various uncertainty sets in robust optimization formulation. Through novel measure concentration r...

This paper deals with the determination of machine numbers and production schedules in manufacturing environments. In this line, a two-stage fuzzy stochastic programming model is discussed with fuzzy processing times where both deterioration and learning effects are evaluated simultaneously. The first stage focuses on the type and number of machines in order to minimize the total costs associat...

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As demand and yield own different uncertain natures, they are modeled separately and then integrated. Demand uncertainty is con...

We consider the optimal management of a hydro-thermal power system in the mid and long terms. From the optimization point of view, this amounts to a large-scale multistage stochastic linear program, often solved by combining sampling with decomposition algorithms, like stochastic dual dynamic programming. Such methodologies, however, may entail prohibitive computational time, especially when ap...

A power generation system comprising thermal and pumped storage hy dro plants is considered Two kinds of models for the cost optimal generation of electric power under uncertain load are introduced i a dynamic model for the short term operation and ii a power production planning model In both cases the presence of stochastic data in the optimization model leads to multi stage and two stage stoc...

Computational studies on two-stage stochastic programming problems indicate that aggregate models have better scale-up properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we attempt to explain this phenomenon, and to lower this threshold. We present the on-demand accuracy approach of Oliveira and Sagastizábal in a form which shows that this approa...

The increasing complexity of the enterprise has motivated the utilization of mathematical programming methodologies to deal with many of the decisions faced by companies. However, in many cases the combinatorial complexity of the decision problems is exacerbated by uncertainty in many of the model parameters, requiring the use of scenarios or stochastic programming strategies. Unfortunately, th...

In this paper we analyze the electricity portfolio problem of a big consumer in a multi-stage stochastic programming framework. Stochasticity enters the model via the uncertain spot price process and is represented by a scenario tree. The decision that has to be taken is how much energy should be bought in advance, and how large the exposition to the uncertain spot market, as well as the relati...

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because its high operational flexibility. In MES, hybrid storage (HESS) composed battery thermal tank plays an important role in enhancing reliability, economics, Hence, determining optimal size HESS MES is critical problem but has not received enough attention. l...