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1. Network Revenue Management Under a Spiked Multinomial Logit Choice Model

   https://doi.org/10.1287/opre.2022.2281  

   Cao, Yufeng; Kleywegt, Anton J.; Wang, He (July 2022, Operations Research)

   Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a choice model called the spiked multinomial logit (spiked-MNL) model was proposed. We study a network revenue management problem under the spiked-MNL choice model. We show that efficient sets, that is, assortments that offer a Pareto-optimal tradeoff between revenue and resource use, are nested-by-revenue when the spike effect is nonnegative. We use this result to show how a deterministic approximation of the stochastic dynamic program can be solved efficiently by solving a small linear program. The solution of the small linear program is used to construct a booking limit policy, and we prove that the policy is asymptotically optimal. This is the first such result for a booking limit policy under a choice model, and our proof uses an approach that is different from those used for previous asymptotic optimality results. Finally, we evaluate different revenue management policies in numerical experiments using both synthetic and airline data. 

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2. Cost Based Nonlinear Pricing

   https://doi.org/10.1145/3580507.3597697  

   Bergemann, Dirk; Heumann, Tibor; Morris, Stephen (July 2023, ACM)

   Hartline, Jason (Ed.)

   The arrival of digital commerce has lead to an increasing use of personalization and differentiation strategies. With differentiated products along the quality dimension and/or the quantity dimension comes the need for nonlinear pricing policies or second degree price discrimination. The optimal pricing strategies for quality and quantity differentiated products were first investigated by Mussa and Rosen (1978) and Maskin and Riley (1984), respectively. The optimal pricing strategies were shown to depend heavily on the prior distribution of the private information regarding the types, and ultimately the willingness-to-pay of the buyers. Yet, frequently the sellers possess only weak and incomplete information about the distribution of demand. This paper aims to develop robust pricing policies that are independent of specific demand distributions and provide revenue guarantees across all possible distributions. 

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3. Incentivized Self-Rebalancing Fleet in Electric Vehicle Sharing

   https://doi.org/10.1080/24725854.2021.1928340  

   Wu, Yuguang; Chen, Minmin; Wang, Xin (July 2021, IISE Transactions)

   null (Ed.)

   With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid. 

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4. The Power of Opaque Products in Pricing

   https://doi.org/10.1287/mnsc.2020.3750  

   Elmachtoub, Adam N.; Hamilton, Michael L. (August 2021, Management Science)

   null (Ed.)

   We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. 

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5. Robust Auctions for Revenue via Enhanced Competition

   https://doi.org/10.1287/opre.2019.1929  

   Roughgarden, Tim; Talgam-Cohen, Inbal; Yan, Qiqi (July 2020, Operations Research)

   Most results in revenue-maximizing mechanism design hinge on “getting the price right”—selling goods to bidders at prices low enough to encourage a sale but high enough to garner nontrivial revenue. This approach is difficult to implement when the seller has little or no a priori information about bidder valuations or when the setting is sufficiently complex, such as matching markets with heterogeneous goods. In this paper, we apply a robust approach to designing auctions for revenue. Instead of relying on prior knowledge regarding bidder valuations, we “let the market do the work” and let prices emerge from competition for scarce goods. We analyze the revenue guarantees of one of the simplest imaginable implementations of this idea: first, we enhance competition in the market by increasing demand (or alternatively, by limiting supply), and second, we run a standard second price (Vickrey) auction. In their renowned work from 1996 , Bulow and Klemperer [Bulow J, Klemperer P (1996) Auctions vs. negotiations. Amer. Econom. Rev. 86(1):180–194.] apply this method to markets with single goods. As our main result, we give the first application beyond single-parameter settings, proving that, simultaneously for many valuation distributions, this method achieves expected revenue at least as good as the optimal revenue in the original market. Our robust and simple approach provides a handle on the elusive optimal revenue in multiitem matching markets and shows when the use of welfare-maximizing Vickrey auctions is justified, even if revenue is a priority. By establishing quantitative tradeoffs, our work provides guidelines for a seller in choosing among two different revenue-extracting strategies: sophisticated pricing based on market research or advertising to draw additional bidders. 

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