Two-stage stochastic linear programming is a classical model in operations research. The usual approach to this model requires detailed information on distribution of the random variables involved. In this paper, we only assume the availability of the first and second moments information of the...

The Equalization method for the Stochastic Generalized Transportation Problem is presented. The algorithm allows to find the optimal solution to the problem of minimizing the expected total cost in the Generalized Transportation Problem with random demand. It is shown that the method is...

In this paper, we present a multi-stage stochastic programming model that integrates financial hedging decisions into the planning of strategic production networks under uncertain exchange rates and product demands. This model considers the expenses of production plants and the revenues of...

Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy...

Multistage dynamic networks with random arc capacities (MDNRAC) have been successfully used for modeling various resource allocation problems in the transportation area. However, solving these problems is generally computationally intensive, and there is still a need to develop more efficient...

Pooling and exchange of random resources may offer the owners insurance and substitution. Greater efficiency and more stable revenues thereby obtain. These good properties derive from a sharing rule that complies with the core concept from cooperative production games. It is applied here to...

Decision making under uncertainty is a challenge faced by many decision makers. Stochastic programming is a major tool developed to deal with optimization with uncertainties that has found applications in, e.g. finance, such as asset-liability and bond-portfolio management. Computationally...