Jochen Gönsch

Using flexible products to cope with demand uncertainty in revenue management

While flexible products have been popular for many years in practice, they have only recently gained attention in the academic literature on revenue management. When selling a flexible product, a firm retains the right to specify some of its details later. The relevant point in time is after the sale, but often before the provision of the product or service, depending on the customers’ need to know the exact specification in advance. The resulting flexibility can help to increase revenues if capacity is fixed and the demand to come difficult to forecast. We present several revenue management models and control mechanisms incorporating this kind of flexible products. An extensive numerical study shows how the different approaches can mitigate the negative impact of demand forecast errors.

Dynamic control mechanisms for revenue management with flexible products

Revenue management with flexible products has experienced a growing interest in the academic literature within the last few years. Flexible products allow supply-side substitution between resources and can therefore help to maximize overall revenue as well as capacity utilization in markets with highly uncertain demand. This paper addresses the question of how the mathematical models which have been developed for capacity control with flexible products should be used over time to exploit the substitution opportunities, while keeping practical applicability in mind. Several dynamic control mechanisms are proposed, each of which makes use of the flexibility to a different extent. A comprehensive computational study shows the potential of the different approaches by revealing their strengths and weaknesses.

Using Dynamic Programming Decomposition for Revenue Management with Opaque Products

Opaque products enable service providers to hide specific characteristics of their service fulfillment from the customer until after purchase. Prominent examples include internet-based service providers selling airline tickets without defining details, such as departure time or operating airline, until the booking has been made. Owing to the resulting flexibility in resource utilization, the traditional revenue management process needs to be modified. In this paper, we extend dynamic programming decomposition techniques widely used for traditional revenue management to develop an intuitive capacity control approach that allows for the incorporation of opaque products. In a simulation study, we show that the developed approach significantly outperforms other well-known capacity control approaches adapted to the opaque product setting. Based on the approach, we also provide computational examples of how the share of opaque products as well as the degree of opacity can influence the results.

An EMSR-Based Approach for Revenue Management with Integrated Upgrade Decisions

We consider the revenue management problem of capacity control with integrated upgrade decision-making. The dynamic programming formulation of this problem is hard to solve to optimality, even in the single-leg case, because multiple hierarchical resource types must be considered simultaneously. Therefore, in this paper, we propose a new heuristic approach that generalizes the idea behind the well-known single-leg EMSR-a procedure to multiple resource types. Similar to EMSR-a, our approach is based on the computation of protection levels, but additionally allows for the integrated consideration of upgrades. In addition, we derive control policies for typical demand arrival patterns. As an extension, we propose a generalization of our approach that allows for arbitrarily ordered prices with respect to the upgrade hierarchy. Furthermore, we perform a number of computational experiments to investigate the performance of the new approach compared to other capacity control methods that incorporate upgrades. We consider typical airlines’ single-leg scenarios with 10 (re)optimizations throughout the booking horizon. The results show that our approach can significantly outperform existing methods in terms of the total achieved revenue, including dynamic programming decomposition approaches that are proposed in literature, as well as successive planning approaches that are widely used in commercial revenue management systems.

A Survey on Risk-Averse and Robust Revenue Management

Abstract Many industries use revenue management to balance uncertain, stochastic demand and inflexible capacity. Popular examples include airlines, hotels, car rentals, retailing, and manufacturing. The classical revenue management approaches considered in theory and practice are based on two assumptions. First, demand – as the only uncertain variable – follows a known distribution and, second, risk-neutrality justifies the maximization of expected revenue. Recently, two related streams of literature emerged that do not need these assumptions. Research on risk-averse revenue management acknowledges that, in practice, many decision makers are risk-averse. Research on robust revenue management focuses worst-case scenarios without a known demand distribution, which is especially relevant for new and extremely unstable businesses. This paper motivates the consideration of risk-averse and robust revenue management. We briefly introduce revenue managements’ two main methods – capacity control and dynamic pricing – in the classical, risk-neutral setting. Then, we provide an exhaustive review of the literature on risk-averse and robust capacity control and dynamic pricing. In doing so, the relevant decision criteria are briefly introduced. Finally, possible avenues for future research are outlined.

Optimizing conditional value-at-risk in dynamic pricing

Many industries use dynamic pricing on an operational level to maximize revenue from selling a fixed capacity over a finite horizon. Classical risk-neutral approaches do not accommodate the risk aversion often encountered in practice. We add to the scarce literature on risk aversion by considering the risk measure conditional value-at-risk (CVaR), which recently became popular in areas like finance, energy, or supply chain management. A key aspect of this paper is selling a single unit of capacity, which is highly relevant in, for example, the real estate market. We analytically derive the optimal policy and obtain structural results. The most important managerial implication is that the risk-averse optimal price is constant over large parts of the selling horizon, whereas the price continuously declines in the standard setting of risk-neutral dynamic pricing. This offers a completely new explanation for the price-setting behavior often observed in practice. For arbitrary capacity, we develop two algorithms to efficiently compute the value function and evaluate them in a numerical study. Our results show that applying a risk-averse policy, even a static one, often yields a higher CVaR than applying a dynamic, but risk-neutral, policy.

Consumer Choice Modeling in Product Line Pricing: Reservation Prices and Discrete Choice Theory

In the literature on product line pricing, consumer choice is often modeled using the max-surplus choice rule. In terms of this concept, consumer preferences are represented by so-called reservation prices and the deterministic decision rule is to choose the product that provides the highest positive surplus. However, the distribution of the reservation prices often seems somewhat arbitrary. In this paper, we demonstrate how reservation prices can be obtained using discrete choice analysis and that these two concepts are not as different as often perceived in the literature. A small example illustrates this approach, using data from a discrete choice model taken from the literature.

Discrete Choice Modelling (Teil II)

Nachdem im ersten Teil dieses Beitrages (WiSt, Nr. 7/ 2008) die theoretischen Grundlagen der wichtigsten Discrete Choice Modelltypen vorgestellt wurden, ist der Schwerpunkt dieses Teils der eigentliche Entwicklungsprozess von konkreten Ausprägungen dieser Modelltypen (Modellspezifikationen). Dazu wird zunächst die Auswahl eines Modelltyps, die Definition von Nutzenfunktionen sowie die Ermittlung von Werten für die enthaltenen Parameter (Abschn. 1) behandelt. Danach werden die vielfältigen Möglichkeiten bei der Auswahl der zur Modellermittlung benötigten Datengrundlage thematisiert (Abschn. 2). Der Beitrag schließt mit einem tabellarischen Überblick über am Markt angebotene Standardsoftware zur Discrete Choice Modellierung (Abschn. 3) und einer kurzen Zusammenfassung (Abschn. 4).

Shared Mobility Systeme

Mit ihrer zunehmenden Verbreitung in der Praxis werden Shared Mobility Systeme immer häufiger Gegenstand von Forschungsaktivitäten. Dieser Beitrag gibt einen Überblick über die verschiedenen Planungsprobleme aus Sicht des Operations Research. So ist auf strategiescher Ebene über Standorte von Stationen und die Flottengröße zu entscheiden. Operativ müssen etwa Fahrzeuge effizient von übervollen zu leeren Stationen transportiert werden. Es werden jeweils beispielhafte Modellierungsansätze vorgestellt.

Dynamic Programming Decomposition for Choice-Based Revenue Management with Flexible Products

We reconsider the stochastic dynamic program of revenue management with flexible products and customer choice behavior as proposed in the seminal paper by Gallego et al. [Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. Working paper, Columbia University, New York]. In the scientific literature on revenue management, as well as in practice, the prevailing strategy to operationalize dynamic programs is to decompose the network by resources and solve the resulting one-dimensional problems. However, up to now, these dynamic programming decomposition approaches have not been applicable to problems with flexible products, because the underlying state space is based on commitments rather than resources.In this paper, we contribute to the existing research by presenting an approach to operationalize revenue management with flexible products and customer choice in a dynamic programming environment. In particular, we propose a generic and formal procedure that transforms the original dynamic program with flexible products into an equivalent dynamic program with a resource-based state space. This reformulation renders the application of dynamic programming decomposition approaches possible. The procedure is based on Fourier-Motzkin elimination and is applicable to arbitrary network revenue management settings with regard to the considered network structure and the number and specifications of flexible products. Numerical experiments show a superior revenue performance of the new approach with average revenues close to the expected upper bound from the choice-based deterministic linear program (CDLP). Moreover, our reformulation improves revenues by up to 8% compared with an extended variant of a standard choice-based approach without flexible products that immediately assigns flexible products after sale.