In this study, a static single machine scheduling problem is investigated, where processing times are stochastic, due dates are deterministic and inserted idle time is allowed. Two objective functions are simultaneously taken into account, minimization of mean completion time and minimization of...

<Para ID="Par1">Data for optimization problems often comes from (deterministic) forecasts, but it is naïve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate...</para>

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...

<Para ID="Par1">The unit commitment problem, aims at computing the production schedule that satisfies the offer-demand equilibrium at minimal cost. Often such problems are considered in a deterministic framework. However uncertainty is present and non-negligible. Robustness of the production schedule is...</para>

We focus on rating of non-life insurance contracts. We employ multiplicative models with basic premium levels and specific surcharge coefficients for various levels of selected risk/rating factors. We use generalized linear models (GLM) to describe the probability distribution of total losses...

The purpose of this paper is to formally describe new optimization models for distributed telecommunication networks.Modern distributed networks put more focus on the processing of information and less on the actual transportation of datathan we are traditionally used to in telecommunications....