Dynamic pricing under customer choice behavior for revenue management in passenger railway networks

Revenue management (RM) for passenger railway is a small but active research field with an increasing attention during the past years. However, a detailed look into existing research shows that most of the current models in theory rely on traditional RM techniques and that advanced models are rare. This thesis aims to close the gap by proposing a state-of-the-art passenger railway pricing model that covers the most important properties from practice, with a special focus on the German railway network and long-distance rail company Deutsche Bahn Fernverkehr (DB). The new model has multiple advantages over DB’s current RM system. Particularly, it uses a choice-based demand function rather than a traditional independent demand model, is formulated as a network model instead of the current leg-based approach and finally optimizes prices on a continuous level instead of controlling booking classes. Since each itinerary in the network is considered by multiple heterogeneous customer segments (e.g., differentiated by travel purpose, desired departure time) a discrete mixed multinomial logit model (MMNL) is applied to represent demand. Compared to alternative choice models such as the multinomial logit model (MNL) or the nested logit model (NL), the MMNL is significantly less considered in pricing research. Furthermore, since the resulting deterministic multi-product multi-resource dynamic pricing model under the MMNL turns out to be non- linear non-convex, an open question is still how to obtain a globally optimal solution. To narrow this gap, this thesis provides multiple approaches that make it able to derive a solution close to the global optimum. For medium-sized networks, a mixed-integer programming approach is proposed that determines an upper bound close to the global optimum of the original model (gap < 1.5%). For large-scale networks, a heuristic approach is presented that significantly decreases the solution time (by factor up to 56) and derives a good solution for an application in practice. Based on these findings, the model and heuristic are extended to fit further price constraints from railway practice and are tested in an extensive simulation study. The results show that the new pricing approach outperforms both benchmark RM policies (i.e., DB’s existing model and EMSR-b) with a revenue improvement of approx. +13-15% over DB’s existing approach under a realistic demand scenario. Finally, to prepare data for large-scale railway networks, an algorithm is presented that automatically derives a large proportion of necessary data to solve choice-based network RM models. This includes, e.g., the set of all meaningful itineraries (incl. transfers) and resources in a network, the corresponding resource consumption and product attribute values such as travel time or number of transfers. All taken together, the goal of this thesis is to give a broad picture about choice-based dynamic pricing for passenger railway networks

25 Hz adaptation: Influence on recovery time in glaucoma

INTRODUCTION. Normal temporal contrast sensitivity is maximally influenced by pre-adaptation with 25-Hz temporal contrast flicker. The aim of this study was to investigate the effects of 25-Hz contrast adaptation on recovery of contrast sensitivity in normals, patients with ocular hypertension, preperimetric, perimetric and advanced perimetric open-angle glaucoma. MATERIALS AND METHODS. Temporal contrast sensitivity was examined after pre-adaptation with 25 Hz in the following: 43 normals, 14 ocular hypertension, 10 preperimetric primary open-angle glaucoma, and 33 perimetric open-an­gle glaucoma patients. After pre-adaptation (the time after which a test stimulus could be detected again), recovery time (RT) was measured at 3% and 5% test contrast. Additionally, 25 patients with advanced perimetric open-angle glaucoma were measured at 12%, 25%, and 35% contrast and compared to a normal group consisting of 15 subjects. RESULTS. 1. Measurements of RT are reliable (Cronbach’s a &gt; 0.8). 2. RT was age-dependent requiring an age-correction in further analyses. 3. RT3% and RT5% were significantly prolonged in perimetric primary open-angle glau­coma compared to normals (3% test contrast: p = 0.007; 5% test contrast: p = 0.035). 4. Within each group, RT3% and RT5% were significantly different at both test contrasts (normals, perimetric open-angle glaucoma: p &lt; 0.001; ocular hypertension: p = 0.007; preperimetric open-angle glaucoma: p = 0.035). 5. RT3% and RT5% were significantly correlated with mean defect (p &lt; 0.001) and retinal nerve fibre layer thickness (p = 0.018). RT5% was correlated with loss variance (p = 0.048). 6. RT12%, RT25% and RT35% were significantly prolonged in advanced perimetric glaucoma (p &lt; 0.001), and correlated with mean defect (p &lt; 0.001, p = 0.002, p = 0.013) and retinal nerve fibre layer thickness (p &lt; 0.001, p = 0.003, p = 0.013). RT12% was also correlated with loss variance (p = 0.016). CONCLUSIONS. Measurements of RT after 25-Hz pre-adaptation can be used in glaucoma diagnosis and follow-up examination, especially in monitoring glaucoma progress in advanced perimetric primary open-angle glaucoma

Understanding, diagnosing, and treating Myalgic encephalomyelitis/chronic fatigue syndrome - State of the art: Report of the 2nd international meeting at the Charité fatigue center.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a devastating disease affecting millions of people worldwide. Due to the 2019 pandemic of coronavirus disease (COVID-19), we are facing a significant increase of ME/CFS prevalence. On May 11th to 12th, 2023, the second international ME/CFS conference of the Charité Fatigue Center was held in Berlin, Germany, focusing on pathomechanisms, diagnosis, and treatment. During the two-day conference, more than 100 researchers from various research fields met on-site and over 700 attendees participated online to discuss the state of the art and novel findings in this field. Key topics from the conference included: the role of the immune system, dysfunction of endothelial and autonomic nervous system, and viral reactivation. Furthermore, there were presentations on innovative diagnostic measures and assessments for this complex disease, cutting-edge treatment approaches, and clinical studies. Despite the increased public attention due to the COVID-19 pandemic, the subsequent rise of Long COVID-19 cases, and the rise of funding opportunities to unravel the pathomechanisms underlying ME/CFS, this severe disease remains highly underresearched. Future adequately funded research efforts are needed to further explore the disease etiology and to identify diagnostic markers and targeted therapies

Patients with COVID-19: in the dark-NETs of neutrophils.

SARS-CoV-2 infection poses a major threat to the lungs and multiple other organs, occasionally causing death. Until effective vaccines are developed to curb the pandemic, it is paramount to define the mechanisms and develop protective therapies to prevent organ dysfunction in patients with COVID-19. Individuals that develop severe manifestations have signs of dysregulated innate and adaptive immune responses. Emerging evidence implicates neutrophils and the disbalance between neutrophil extracellular trap (NET) formation and degradation plays a central role in the pathophysiology of inflammation, coagulopathy, organ damage, and immunothrombosis that characterize severe cases of COVID-19. Here, we discuss the evidence supporting a role for NETs in COVID-19 manifestations and present putative mechanisms, by which NETs promote tissue injury and immunothrombosis. We present therapeutic strategies, which have been successful in the treatment of immunο-inflammatory disorders and which target dysregulated NET formation or degradation, as potential approaches that may benefit patients with severe COVID-19

Dynamic pricing under customer choice behavior for revenue management in passenger railway networks

Revenue management (RM) for passenger railway is a small but active research field with an increasing attention during the past years. However, a detailed look into existing research shows that most of the current models in theory rely on traditional RM techniques and that advanced models are rare. This thesis aims to close the gap by proposing a state-of-the-art passenger railway pricing model that covers the most important properties from practice, with a special focus on the German railway network and long-distance rail company Deutsche Bahn Fernverkehr (DB). The new model has multiple advantages over DB’s current RM system. Particularly, it uses a choice-based demand function rather than a traditional independent demand model, is formulated as a network model instead of the current leg-based approach and finally optimizes prices on a continuous level instead of controlling booking classes. Since each itinerary in the network is considered by multiple heterogeneous customer segments (e.g., differentiated by travel purpose, desired departure time) a discrete mixed multinomial logit model (MMNL) is applied to represent demand. Compared to alternative choice models such as the multinomial logit model (MNL) or the nested logit model (NL), the MMNL is significantly less considered in pricing research. Furthermore, since the resulting deterministic multi-product multi-resource dynamic pricing model under the MMNL turns out to be non- linear non-convex, an open question is still how to obtain a globally optimal solution. To narrow this gap, this thesis provides multiple approaches that make it able to derive a solution close to the global optimum. For medium-sized networks, a mixed-integer programming approach is proposed that determines an upper bound close to the global optimum of the original model (gap < 1.5%). For large-scale networks, a heuristic approach is presented that significantly decreases the solution time (by factor up to 56) and derives a good solution for an application in practice. Based on these findings, the model and heuristic are extended to fit further price constraints from railway practice and are tested in an extensive simulation study. The results show that the new pricing approach outperforms both benchmark RM policies (i.e., DB’s existing model and EMSR-b) with a revenue improvement of approx. +13-15% over DB’s existing approach under a realistic demand scenario. Finally, to prepare data for large-scale railway networks, an algorithm is presented that automatically derives a large proportion of necessary data to solve choice-based network RM models. This includes, e.g., the set of all meaningful itineraries (incl. transfers) and resources in a network, the corresponding resource consumption and product attribute values such as travel time or number of transfers. All taken together, the goal of this thesis is to give a broad picture about choice-based dynamic pricing for passenger railway networks

Railway network dynamic pricing under discrete mixed logit demand

Revenue Management is an already widespread method in the passenger rail business. In a survey that we conducted in 2018 with European rail companies we observed that leg-based approaches are still dominant in practice. However, due to the distinct network structure of railway companies, i.e., a resource is used by many different itineraries, an approach that accounts for network effects seems to offer significant additional revenue potential. In our talk, we present a choice-based network dynamic pricing model that is developed in cooperation with Deutsche Bahn Fernverkehr (DB), a leading provider for long-distance passenger transportation in Germany, with over 140 million passengers each year and several millions of pricing decisions daily. In addition to the traditional capacity constraints, the model includes railway specific constraints, such as price consistency constraints that ensure that longer itineraries are more expensive than shorter ones. Further, we use a discrete mixed multinomial logit demand model to represent the choice among different itineraries for different customer segments. A challenge that comes along with this model is that the original formulation is nonlinear, nonconvex and thus not directly solvable to global optimality. In addition, it turns out that solving the original problem formulation is very slow so that large-scale instances cannot be solved within a reasonable time limit specified by practice. While under the classical MNL model a convex representation is already well studied, the convexity does not hold under the assumption of multiple discrete customer segments. Therefore, a different approach is needed in practice that performs well with regard to solution quality and time. We present a heuristic that determines a good solution in reasonable time and show that it leads to increased revenue compared to the current approach used in practice and a classical leg-based EMSRb heuristic. To quantify the revenue improvements, we conducted an extensive simulation study that compares the three models under different scenarios (i.e., different rail network and demand cases). Furthermore, we evaluate the approaches for robustness by randomly adjusting the original choice parameters in the simulation. The results provide an insight into how inaccuracies in demand forecasting influence the performance of the approaches and give a realistic picture of the revenue potential in practice. We analyze the results in two ways: Firstly, we present statistics about the solution time and quality of large-scale networks. Secondly, we show the results in terms of revenue, i.e., compare the revenue of the new model with the two above mentioned benchmark approaches. We show that the O&D based model significantly outperforms both benchmark heuristics in terms of total revenue and that the revenue gap increases in high demand scenarios. Furthermore, even in case of incorrect choice model parameter estimates the revenue gap is still significantly positive, though smaller than in the case of correct forecasts - showing that the O&D based approach is less robust to forecasting errors

Railway network dynamic pricing under discrete mixed logit demand

Revenue Management is an already widespread method in the passenger rail business. In a survey that we conducted in 2018 with European rail companies we observed that leg-based approaches are still dominant in practice. However, due to the distinct network structure of railway companies, i.e., a resource is used by many different itineraries, an approach that accounts for network effects seems to offer significant additional revenue potential. In our talk, we present a choice-based network dynamic pricing model that is developed in cooperation with Deutsche Bahn Fernverkehr (DB), a leading provider for long-distance passenger transportation in Germany, with over 140 million passengers each year and several millions of pricing decisions daily. In addition to the traditional capacity constraints, the model includes railway specific constraints, such as price consistency constraints that ensure that longer itineraries are more expensive than shorter ones. Further, we use a discrete mixed multinomial logit demand model to represent the choice among different itineraries for different customer segments. A challenge that comes along with this model is that the original formulation is nonlinear, nonconvex and thus not directly solvable to global optimality. In addition, it turns out that solving the original problem formulation is very slow so that large-scale instances cannot be solved within a reasonable time limit specified by practice. While under the classical MNL model a convex representation is already well studied, the convexity does not hold under the assumption of multiple discrete customer segments. Therefore, a different approach is needed in practice that performs well with regard to solution quality and time. We present a heuristic that determines a good solution in reasonable time and show that it leads to increased revenue compared to the current approach used in practice and a classical leg-based EMSRb heuristic. To quantify the revenue improvements, we conducted an extensive simulation study that compares the three models under different scenarios (i.e., different rail network and demand cases). Furthermore, we evaluate the approaches for robustness by randomly adjusting the original choice parameters in the simulation. The results provide an insight into how inaccuracies in demand forecasting influence the performance of the approaches and give a realistic picture of the revenue potential in practice. We analyze the results in two ways: Firstly, we present statistics about the solution time and quality of large-scale networks. Secondly, we show the results in terms of revenue, i.e., compare the revenue of the new model with the two above mentioned benchmark approaches. We show that the O&D based model significantly outperforms both benchmark heuristics in terms of total revenue and that the revenue gap increases in high demand scenarios. Furthermore, even in case of incorrect choice model parameter estimates the revenue gap is still significantly positive, though smaller than in the case of correct forecasts - showing that the O&D based approach is less robust to forecasting errors

Continuous pricing in a capacitated network under mixed multinomial logit demand

In this paper, we consider the deterministic multi-product multi-resource dynamic pricing (DMMDP) problem with continuous prices under the mixed multinomial logit (MMNL) choice model. The DMMDP problem arises in many applications where pricing decisions for multiple products should be optimized jointly, in particular when products have cross-effects on demands of other products and/or different products share the same resources (Talluri and van Ryzin 2004, Chen and Chen 2015). Applications are manifold, such as revenue management for airlines, railways, and hotels, assortment pricing in retailing, or product line pricing in consumer goods industries. The corresponding problem instances in practice are typically of large-scale size such that efficient solution techniques are required. The MMNL model is considered to be a powerful choice model that captures heterogeneous cross-effects in demand and that can approximate any random utility choice model arbitrarily closely (McFadden and Train 2000). It has received increasing attention in the related field of product assortment (PA) optimization, involving a seller’s discrete decisions about the selection of products and their prices (see, e.g., Feldman and Topaloglu 2015, Kunnumkal 2015, Méndez-Díaz 2015). Since the PA problem is NP-hard under the MMNL choice model (Rusmevichientong et al. 2014, Désir et al. 2014), much work has been focused on deriving upper bounds and efficient approximations, with the recent exception of Sen et al. (2017) who propose an exact conic MIP approach. On the other hand, the MMNL model and its incorporation into the DMMDP problem has only received scant attention in the dynamic pricing literature (e.g., Keller et al. 2014), despite its theoretical and practical relevance. The more common approach so far has been to incorporate the standard single-segment MNL choice model into the DMMDP problem (see, e.g., Dong et al. 2009, Zhang and Lu 2013, Keller et al. 2014). The logit profit function is known to be concave with respect to demand (Hanson and Martin 1996, Song and Xue 2007, Dong et al. 2009, Li and Huh 2011). In case of a single customer segment, there is a linearizable one-to-one mapping between product prices and MNL choice probabilities such that the demand model satisfies some regularity conditions, and the resulting optimization problem DMMDP is a convex optimization (minimization) problem in demand. In case the logit choice model encompasses multiple customer segments with heterogeneous price sensitivity parameters, the convex problem structure can still be maintained if it is feasible to simultaneously quote individual prices for the same product to each segment according to a first- or third-degree price discrimination (Schön 2010a, b). However, this requires the capability to identify à priori which customer segment an incoming sales request belongs to. In the more common case considered here, where the same product is offered at a uniform price to all customer requests occurring at the same time, the convexity property is lost, since non- convex constraints need to be introduced to ensure price consistency across segments with overlapping consideration sets. Accordingly, the DMMDP problem under MMNL choice is non-linear non-convex and thus difficult to solve in general. How to efficiently solve the continuous pricing problem with multiple segments to optimality is still an open problem, and we want to contribute to narrow this gap. Our contributions are as follows: • First, we analyze the DMMDP problem with continuous prices and price consistency constraints under the MMNL choice model in detail with regard to its mathematical structure. • We present an approximate optimization problem to derive an upper bound on the optimal profit and to determine heuristic solutions. The approximate problem is convex, and can therefore be solved efficiently even for large problem instances. An experimental study shows that the approach is very promising with regard to run time performance and solution quality. • We present a convex mixed-integer programming approach that allows to tighten the upper bound arbitrarily close-to-optimum and to determine provably near-optimal solutions of the original problem; to our knowledge, this is the first approach to approximately tackle the problem under the MMNL choice model with a performance guarantee; in our experiments, we are able to approximately solve medium-sized problem instances in reasonable time. Furthermore, we discuss the potential benefits we gain by allowing prices to be continuous rather than restricting them to discrete values with regard to solution quality and run time performance. • The suggested dynamic pricing approach is applied to a real-world revenue management case study of the German long-distance railway network

Continuous pricing in a capacitated network under mixed multinomial logit demand

In this paper, we consider the deterministic multi-product multi-resource dynamic pricing (DMMDP) problem with continuous prices under the mixed multinomial logit (MMNL) choice model. The DMMDP problem arises in many applications where pricing decisions for multiple products should be optimized jointly, in particular when products have cross-effects on demands of other products and/or different products share the same resources (Talluri and van Ryzin 2004, Chen and Chen 2015). Applications are manifold, such as revenue management for airlines, railways, and hotels, assortment pricing in retailing, or product line pricing in consumer goods industries. The corresponding problem instances in practice are typically of large-scale size such that efficient solution techniques are required. The MMNL model is considered to be a powerful choice model that captures heterogeneous cross-effects in demand and that can approximate any random utility choice model arbitrarily closely (McFadden and Train 2000). It has received increasing attention in the related field of product assortment (PA) optimization, involving a seller’s discrete decisions about the selection of products and their prices (see, e.g., Feldman and Topaloglu 2015, Kunnumkal 2015, Méndez-Díaz 2015). Since the PA problem is NP-hard under the MMNL choice model (Rusmevichientong et al. 2014, Désir et al. 2014), much work has been focused on deriving upper bounds and efficient approximations, with the recent exception of Sen et al. (2017) who propose an exact conic MIP approach. On the other hand, the MMNL model and its incorporation into the DMMDP problem has only received scant attention in the dynamic pricing literature (e.g., Keller et al. 2014), despite its theoretical and practical relevance. The more common approach so far has been to incorporate the standard single-segment MNL choice model into the DMMDP problem (see, e.g., Dong et al. 2009, Zhang and Lu 2013, Keller et al. 2014). The logit profit function is known to be concave with respect to demand (Hanson and Martin 1996, Song and Xue 2007, Dong et al. 2009, Li and Huh 2011). In case of a single customer segment, there is a linearizable one-to-one mapping between product prices and MNL choice probabilities such that the demand model satisfies some regularity conditions, and the resulting optimization problem DMMDP is a convex optimization (minimization) problem in demand. In case the logit choice model encompasses multiple customer segments with heterogeneous price sensitivity parameters, the convex problem structure can still be maintained if it is feasible to simultaneously quote individual prices for the same product to each segment according to a first- or third-degree price discrimination (Schön 2010a, b). However, this requires the capability to identify à priori which customer segment an incoming sales request belongs to. In the more common case considered here, where the same product is offered at a uniform price to all customer requests occurring at the same time, the convexity property is lost, since non- convex constraints need to be introduced to ensure price consistency across segments with overlapping consideration sets. Accordingly, the DMMDP problem under MMNL choice is non-linear non-convex and thus difficult to solve in general. How to efficiently solve the continuous pricing problem with multiple segments to optimality is still an open problem, and we want to contribute to narrow this gap. Our contributions are as follows: • First, we analyze the DMMDP problem with continuous prices and price consistency constraints under the MMNL choice model in detail with regard to its mathematical structure. • We present an approximate optimization problem to derive an upper bound on the optimal profit and to determine heuristic solutions. The approximate problem is convex, and can therefore be solved efficiently even for large problem instances. An experimental study shows that the approach is very promising with regard to run time performance and solution quality. • We present a convex mixed-integer programming approach that allows to tighten the upper bound arbitrarily close-to-optimum and to determine provably near-optimal solutions of the original problem; to our knowledge, this is the first approach to approximately tackle the problem under the MMNL choice model with a performance guarantee; in our experiments, we are able to approximately solve medium-sized problem instances in reasonable time. Furthermore, we discuss the potential benefits we gain by allowing prices to be continuous rather than restricting them to discrete values with regard to solution quality and run time performance. • The suggested dynamic pricing approach is applied to a real-world revenue management case study of the German long-distance railway network