Ruud Brekelmans

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Dike height optimization is of major importance to the Netherlands as a large part of the country lies below sea level and high water levels in rivers can cause floods. A cost-benefit analysis is discussed in Eijgenraam et al. (2010), which is an improvement of the model Van Dantzig (1956) introduced after a devastating flood in the Netherlands in 1953. We(More)
This paper presents a new sequential method for constrained non-linear optimization problems. The principal characteristics of these problems are very time consuming function evaluations and the absence of derivative information. Such problems are common in design optimization, where time consuming function evaluations are carried out by simulation tools(More)
This paper determines the optimal timing of dike heightenings as well as the corresponding optimal dike heightenings to protect against floods. To derive the optimal policy we design an algorithm based on the Impulse Control Maximum Principle. In this way the paper presents one of the first real life applications of the Impulse Control Maximum Principle(More)
In this note we show that multiple solutions exist for the productioninventory example in the seminal paper on adjustable robust optimization in [2]. All these optimal robust solutions have the same worst-case objective value, but the mean objective values differ up to 21.9% and for individual realizations this difference can be up to 59.4%. We show via(More)
In this paper we consider an insurer who has incomplete information about the claim frequency of the risk process. He therefore calculates the premium on the basis of a prior distribution for the claim frequency. Future information might then reveal that it is no longer optimal for the insurer to continue to offer the insurance under the current conditions.(More)
Inventory models need some specification of the distribution of demand in order to find the optimal order-up-to level or reorder point. This distribution is unknown in real life and there are several solutions to overcome this problem. One approach is to assume a distribution, estimate its parameters and replace the unknown demand parameters by these(More)
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