Springer Science and Business Media LLC

In Lagerhaltungsproblemen mit stochastischen Lieferfristschwankungen kann der Bestellpunkt grundsätzlich nach zwei unterschiedlichen Verfahren berechnet werden; entweder durch explizites Bilden von Lieferfristprognosen oder durch die Erfassung nicht befriedigter Nachfrage. Diese grundsätzlich unterschiedlichen Vorgehensweisen stellen Feedforeward- bzw. Feedbackverfahren dar. Gegenstand der Arbeit ist ein Vergleich beider Verfahren in unterschiedlichen Problem-situationen hinsichtlich verschiedener Kriterien.

A variety of fundamental modelling approaches exist using different competition concepts with and without strategic behaviour to derive electricity prices. To investigate the quality and practicability of these different approaches in energy economics, a perfect competition model, a Cournot model and a Bilevel model are introduced and applied to different situations in the German electricity market. The three electricity market approaches are analysed with respect to their ability to represent electricity prices and the possibility of market power abuse. Market prices are taken as a benchmark for model validity. As a result, the perfect competition model fits best to today’s market situation in most hours of the year. The Bilevel approach explains prices in high load hours sometimes better than the competition model. But complexity and calculation time increase disproportionately. In addition to the analysis of model quality, we use three scenarios to quantify how a high renewable feed-in influences the ability to abuse market power. Results show that the ability to address market power strongly depends on the amount of installed capacities.

This paper studies a multi-period investment–consumption optimization problem with a stochastic discount rate and a time-varying utility function, which are governed by a Markov-modulated regime switching model. The investment is dynamically reallocated between one risk-free asset and one risky asset. The problem is time inconsistent due to the stochastic discount rate. An analytical equilibrium solution is established by resorting to a game theoretical framework. Numerous sensitivity analyses and numerical examples are provided to demonstrate the effects of the stochastic discount rate and time-varying utility coefficients on the decision-maker’s investment–consumption behavior. Our results show that many properties which are satisfied in the classical models do not hold any more due to either the stochastic discount rate or the time-varying utility function.

In this paper we describe the concept and design of a meta-heuristic based decision support system generator (DSS-generator) for portfolio optimization. We report extensively on experience with the application of a specific DSS that has been customized for controlling and optimizing passively managed stock funds. Here, the constraints from the law on investment trust companies as well as several fund specific guidelines prohibit that the benchmark can be identically reproduced. For measuring the performance of the portfolio a tracking error model with data stemming from a factor model is applied. Our results show that the system provides proposals for the fund manager in acceptable time which are feasible with respect to the guidelines and excellent in quality.