Today’s technology facilitates selling strategies that were unthinkable only a few years ago. One increasingly popular strategy uses incompletely specified products (ICSPs). The seller retains the right to specify some details of the product or service after the sale. The selling strategies’ main advantages are an additional dimension for market segmentation and operational flexibility due to supply-side substitution possibilities. Since the strategy became popular with Priceline and Hotwire in the travel industry about two decades ago, it has increasingly been adopted by other industries with stochastic demand and limited capacity as well. At the same time, it is actively researched from the perspectives of strategic operations management, empirics, and revenue management.

This paper first describes the application of ICSPs in practice. Then, we introduce the different research communities that are active in this field and relate the terminology they use. The main part is an exhaustive review of the literature on selling ICSPs from the different perspectives. Here, we complement a tabular overview with an introduction into the community and a detailed description of each paper. Finally, possible directions for future research are outlined.

We see that strategic operations management has described advantages of ICSPs over other strategies in a variety of settings, but also identified countervailing effects. Today, empirical research is confined to hotels and airlines and largely disconnected from the other perspectives. Operational papers are ample, but mostly concerned with the availability of ICSPs. Research on operational (dynamic) pricing is surprisingly scarce.

Recently, the standard dynamic programming model of network revenue management has been extended for integrated upgrade decision-making. However, opposed to the original model, heuristically breaking the extended model down into a series of single-leg problems by dynamic programming decomposition in order to allow for real-world application is not possible. This is because the model’s state space does not incorporate resources but commitments reflecting already sold products and capacity consumption is only resolved at the end of the booking horizon, thereby considering upgrade options. In this paper, we consider arbitrary airline networks with upgrades being performed separately on each flight leg. We show that in this case, there are two reformulations of the extended model. First, we prove that an ad hoc formulation, in which upgrades are technically performed immediately after a sale, is completely equivalent. Second, we present another reformulation whose idea is adapted from linear programing-based production planning with alternative machine types. We prove that the resulting dynamic program is also equivalent. The advantage of both reformulations is that their state space is based on either real or virtual resources instead of commitments. Thus, dynamic programming decomposition techniques can again be applied. Despite the formal equivalence of both reformulations, applying decomposition techniques leads to different approximations and thus to potentially different results when applied in practice. Therefore, we finally numerically examine the approaches regarding revenue performance and discuss airline revenue management settings in which they differ.

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.

In many service industries, firms offer a portfolio of similar products based on different types of resources. Mismatches between demand and capacity can therefore often be managed by using product upgrades. Clearly, it is desirable to consider this possibility in the revenue management systems that are used to decide on the acceptance of requests. To incorporate upgrades, we build upon different dynamic programming formulations from the literature and gain several new structural insights that facilitate the control process under certain conditions. We then propose two dynamic programming decomposition approaches that extend the traditional decomposition for capacity control by simultaneously considering upgrades as well as capacity control decisions. While the first approach is specifically suited for the multi-day capacity control problem faced, for example, by hotels and car rental companies, the second one is more general and can be applied in arbitrary network revenue management settings that allow upgrading. Both approaches are formally derived and analytically related to each other. It is shown that they give tighter upper bounds on the optimal solution of the original dynamic program than the well-known deterministic linear program. Using data from a major car rental company, we perform computational experiments that show that the proposed approaches are tractable for real-world problem sizes and outperform those disaggregated, successive planning approaches that are used in revenue management practice today.

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

Today’s technology facilitates selling strategies that were unthinkable only a few years ago. One increasingly popular strategy uses incompletely specified products (ICSPs). The seller retains the right to specify some details of the product or service after the sale. The selling strategies’ main advantages are an additional dimension for market segmentation and operational flexibility due to supply-side substitution possibilities. Since the strategy became popular with Priceline and Hotwire in the travel industry about two decades ago, it has increasingly been adopted by other industries with stochastic demand and limited capacity as well. At the same time, it is actively researched from the perspectives of strategic operations management, empirics, and revenue management.

This paper first describes the application of ICSPs in practice. Then, we introduce the different research communities that are active in this field and relate the terminology they use. The main part is an exhaustive review of the literature on selling ICSPs from the different perspectives. Here, we complement a tabular overview with an introduction into the community and a detailed description of each paper. Finally, possible directions for future research are outlined.

We see that strategic operations management has described advantages of ICSPs over other strategies in a variety of settings, but also identified countervailing effects. Today, empirical research is confined to hotels and airlines and largely disconnected from the other perspectives. Operational papers are ample, but mostly concerned with the availability of ICSPs. Research on operational (dynamic) pricing is surprisingly scarce.

A major benefit of flexible products is that they allow for supply-side substitution even after they have been sold. This helps improve capacity utilization and increase the overall revenue in a stochastic environment. As several authors have shown, flexible products can be incorporated into the well-known deterministic linear program (DLP) of revenue management׳s capacity control. In this paper, we show that flexible products have an additional “value of flexibility” due to their supply-side substitution possibilities, which can be captured monetarily. However, the DLP-based approaches proposed so far fail to capture this value and, thus, steadily undervalue flexible products, resulting in lower overall revenues. To take the full potential of flexible products into account, we propose a new approach that systematically increases the revenues of flexible products when solving the DLP and performing capacity control. A mathematical function of variables available during the booking horizon represents this artificial markup and adapts dynamically to the current situation. We determine the function׳s parameters using a standard simulation-based optimization method. Numerical experiments show that the benefits of the new approach are biggest when low value demand arrives early. Revenues are improved by up to 5% in many settings.

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.

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.