ALM model for pension funds : numerical results for a prototype model

A multistage mixed-integer stochastic programming model is formulated for an Asset Liability Management problem for pension funds. Since these models are too difficult to solve for realistically sized problems, a heuristic is described. Numerical results for several instances of a prototype model are presented and discussed.

An ALM model for pension funds using integrated chance constraints

We discuss integrated chance constraints in their role of short-term risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for modeling short-term risk prompted by the development of new guidelines by the regulating authority for Dutch pension funds. The paper concludes with a numerical illustration of the importance of such short-term risk constraints

Betere diagnostiek voor luchtweginfecties bij kalveren

Adriaan Antonis werkt dertien jaar bij het Centraal Veterinair Instituut onderdeel van Wageningen UR in Lelystad. Daar wordt onderzoek gedaan naar de bedrijfsgebonden dierziekten, onder andere naar luchtweginfecties op kalverbedrijven. Adriaan werkt mee aan de ontwikkeling van nieuwe diagnostische tests. "Je moet weten wat er op een bedrijf aan de hand is, dan kun je gerichter behandelen"

Terrestrial Implications of Cosmological Gamma-Ray Burst Models

The observation by the BATSE instrument on the Compton Gamma Ray Observatory that gamma-ray bursts (GRBs) are distributed isotropically around the Earth but nonuniformly in distance has led to the widespread conclusion that GRBs are most likely to be at cosmological distances, making them the most luminous sources known in the Universe. If bursts arise from events that occur in normal galaxies, such as neutron star binary inspirals, then they will also occur in our Galaxy about every hundred thousand to million years. The gamma-ray flux at the Earth due to a Galactic GRB would far exceed that from even the largest solar flares. The absorption of this radiation in the atmosphere would substantially increase the stratospheric nitric oxide concentration through photodissociation of N$_2$, greatly reducing the ozone concentration for several years through NO$_x$ catalysis, with important biospheric effects due to increased solar ultraviolet flux. A nearby GRB may also leave traces in anomalous radionuclide abundances.Comment: uuencoded, gzip-ed postscript; 6 pages; submitted to ApJ Letter

Processing second-order stochastic dominance models using cutting-plane representations

This is the post-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2011 Springer-VerlagSecond-order stochastic dominance (SSD) is widely recognised as an important decision criterion in portfolio selection. Unfortunately, stochastic dominance models are known to be very demanding from a computational point of view. In this paper we consider two classes of models which use SSD as a choice criterion. The first, proposed by Dentcheva and Ruszczyński (J Bank Finance 30:433–451, 2006), uses a SSD constraint, which can be expressed as integrated chance constraints (ICCs). The second, proposed by Roman et al. (Math Program, Ser B 108:541–569, 2006) uses SSD through a multi-objective formulation with CVaR objectives. Cutting plane representations and algorithms were proposed by Klein Haneveld and Van der Vlerk (Comput Manage Sci 3:245–269, 2006) for ICCs, and by Künzi-Bay and Mayer (Comput Manage Sci 3:3–27, 2006) for CVaR minimization. These concepts are taken into consideration to propose representations and solution methods for the above class of SSD based models. We describe a cutting plane based solution algorithm and outline implementation details. A computational study is presented, which demonstrates the effectiveness and the scale-up properties of the solution algorithm, as applied to the SSD model of Roman et al. (Math Program, Ser B 108:541–569, 2006).This study was funded by OTKA, Hungarian National Fund for Scientific Research, project 47340; by Mobile Innovation Centre, Budapest University of Technology, project 2.2; Optirisk Systems, Uxbridge, UK and by BRIEF (Brunel University Research Innovation and Enterprise Fund)

Initiating pancreatic neuroendocrine tumour (pNET) screening in young MEN1 patients:results from the DutchMEN Study Group

Context: Nonfunctioning pancreatic neuroendocrine tumors (NF-pNETs) are highly prevalent and constitute an important cause of mortality in patients with multiple endocrine neoplasia type 1 (MEN1). Still, the optimal age to initiate screening for pNETs is under debate. Objective: The aim of this work is to assess the age of occurrence of clinically relevant NF-pNETs in young MEN1 patients. Methods: Pancreatic imaging data of MEN1 patients were retrieved from the DutchMEN Study Group database. Interval-censored survival methods were used to describe age-related penetrance, compare survival curves, and develop a parametric model for estimating the risk of having clinically relevant NF-pNET at various ages. The primary objective was to assess age at occurrence of clinically relevant NF-pNET (size ≥†20 mm or rapid growth); secondary objectives were the age at occurrence of NF-pNET of any size and pNET-associated metastasized disease. Results: Five of 350 patients developed clinically relevant NF-pNETs before age 18 years, 2 of whom subsequently developed lymph node metastases. No differences in clinically relevant NF-pNET-free survival were found for sex, time frame, and type of MEN1 diagnosis or genotype. The estimated ages (median, 95% CI) at a 1%, 2.5%, and 5% risk of having developed a clinically relevant tumor are 9.5 (6.5-12.7), 13.5 (10.2-16.9), and 17.8 years (14.3-21.4), respectively. Conclusion: Analyses from this population-based cohort indicate that start of surveillance for NF-pNETs with pancreatic imaging at age 13 to 14 years is justified. The psychological and medical burden of screening at a young age should be considered

Application of stochastic programming to reduce uncertainties in quality-based supply planning of slaughterhouses

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses