Claudius Steinhardt

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.

A review of choice-based revenue management : theory and methods

Over the last fifteen years, the theory and practice of revenue management has experienced significant developments due to the need to incorporate customer choice behavior. In this paper, we portray these developments by reviewing the key literature on choice-based revenue management, specifically focusing on methodological publications of availability control over the years 2004–2017. For this purpose, we first state the choice-based network revenue management problem by formulating the underlying dynamic program, and structure the review according to its components and the resulting inherent challenges. In particular, we first focus on the demand modeling by giving an overview of popular choice models, discussing their properties, and describing estimation procedures relevant to choice-based revenue management. Second, we elaborate on assortment optimization, which is a fundamental component of the problem. Third, we describe recent developments on tackling the entire control problem. We also discuss the relation to dynamic pricing. Finally, we give directions for future research.

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.

Optimal product line pricing in the presence of budget-constrained consumers

The product line pricing problem is generally defined as a sellers task to determine the optimal prices for each product in a product line while accounting for both demand-side and supply-side restrictions. In this paper, we contribute to the existing literature by incorporating consumers’ budget considerations into the sellers optimisation problem. Despite playing an important role in many applications, such as the pricing of tickets for sporting or theatre seasons, budget constraints have not been considered in the academic literature in the context of product line pricing to date.

Model-based decision support for optimal brochure pricing: applying advanced analytics in the tour operating industry

The research presented in this paper is motivated by an industry project conducted with TUI Deutschland, Germany’s leading tour operator. We consider the decision problem of optimally determining hotel room prices to be published in the tour operator’s brochure, which is usually valid for a half-year period. In practice, this task is performed manually by a large number of pricing specialists, each of whom is in charge of setting up to 100,000 prices. In this paper, we develop an advanced analytics approach to provide decision support for this task. More precisely, we propose a mixed integer linear programming-based approach, involving state-of-the-art methods from data analysis and optimization. In this context, we formally introduce the brochure pricing problem as a new optimization problem and present several alternative mathematical model formulations. The problem incorporates demand-side behavior by including a general attraction model whose parameters can be obtained from past booking data. Furthermore, we present different real-world scenarios of model-based decision support, showing how the brochure pricing problem and some variants thereof can be integrated into the manual decision making process, given the requirement of using standard optimization software. For example, the model-based approach can help the pricing specialist balance the objective of profit maximization and the disadvantage of a very complicated pricing structure.

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).

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.

A Model-Based Approximation of Opportunity Cost for Dynamic Pricing in Attended Home Delivery

For online retailers with attended home delivery business models, the decisive factor for promising dynamic time slot pricing decisions is the quality of the opportunity cost approximation concerning incoming customer requests. For this purpose, we present a novel approximation approach based on mixed-integer linear programming that we integrate into the de facto standard dynamic pricing framework prevalent in the academic literature. Our approximation combines the most current information regarding the customers accepted to date with a forecast of expected customers to come that is adapted during the progress of the booking horizon. Thus, future customer requests demand management, i.e. the consequences of future pricing decisions, is anticipated. We approximate the retailer’s vehicle routes and thus delivery costs of expected customers by a dynamic seed-based scheme in which potential seeds’ locations as well as related distance approximations are dynamically adjusted under consideration of the locations of already accepted customers. In a computational study, we compare the approach to established pricing approaches in practice and to the state-of-the-art dynamic pricing policy. We show that our approach constantly yields the highest profit, specifically given a tight capacity level. We further provide implications for practical use. We show that, even for large-scale implementations in a real-time environment, our approach is applicable by using parallel computing and by only periodically recalculating opportunity cost. Even then, our approach leads to very good results.