In this article, we compare Markov Decision Process (MDP) to expected marginal seat revenue b (EMSRb) under realistic demand behaviour by implementing both in a stochastic simulation model. First come first served as well as an upper bound, serves for reference. We give a short explanation of...

In this article, we compare Markov Decision Process (MDP) to expected marginal seat revenue b (EMSRb) under realistic demand behaviour by implementing both in a stochastic simulation model. First come first served as well as an upper bound, serves for reference. We give a short explanation of...

Primary care systems are a cornerstone of universally accessible health care. The planning, analysis, and adaptation of primary care systems is a highly non-trivial problem due to the systems' inherent complexity, unforeseen future events, and scarcity of data. To support the search for...

State-of-the-art revenue management systems combine forecasting and optimization algorithms with human decision-making. However, only a few existing contributions consider the behavioral aspects of revenue management. To extend the related research, we examine the impact of nonstationary demand...

Revenue management is a complex operational planning process involving predictive and prescriptive analytics. As real-world implementations strongly rely on the joint outcomes from both algorithms and analysts, we consider the revenue management system as an example of symbiotic analytics...

Analyzing historical sales data to draw conclusions on the underlying demand structure is a central foundation for sales planning, e.g. in assortment and revenue optimization. This contribution focuses on estimating the choice behavior of demand segments as well as their distribution from panel...