 Award ID(s):
 2006526
 NSFPAR ID:
 10342728
 Date Published:
 Journal Name:
 Management Science
 ISSN:
 00251909
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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This paper studies a dynamic pricing problem under model misspecification. To characterize model misspecification, we adopt the εcontamination model—the most fundamental model in robust statistics and machine learning. In particular, for a selling horizon of length T, the online εcontamination model assumes that demands are realized according to a typical unknown demand function only for [Formula: see text] periods. For the rest of [Formula: see text] periods, an outlier purchase can happen with arbitrary demand functions. The challenges brought by the presence of outlier customers are mainly due to the fact that arrivals of outliers and their exhibited demand behaviors are completely arbitrary, therefore calling for robust estimation and exploration strategies that can handle any outlier arrival and demand patterns. We first consider unconstrained dynamic pricing without any inventory constraint. In this case, we adopt the FollowtheRegularizedLeader algorithm to hedge against outlier purchase behavior. Then, we introduce inventory constraints. When the inventory is insufficient, we study a robust bisectionsearch algorithm to identify the clearance price—that is, the price at which the initial inventory is expected to clear at the end of T periods. Finally, we study the general dynamic pricing case, where a retailer has no clue whether the inventory is sufficient or not. In this case, we design a metaalgorithm that combines the previous two policies. All algorithms are fully adaptive, without requiring prior knowledge of the outlier proportion parameter ε. Simulation study shows that our policy outperforms existing policies in the literature.more » « less

null (Ed.)The prevalence of ecommerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a thirdparty agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacypreserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for ddimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics.more » « less

Pricebased revenue management is an important problem in operations management with many practical applications. The problem considers a seller who sells one or multiple products over T consecutive periods and is subject to constraints on the initial inventory levels of resources. Whereas, in theory, the optimal pricing policy could be obtained via dynamic programming, computing the exact dynamic programming solution is often intractable. Approximate policies, such as the resolving heuristics, are often applied as computationally tractable alternatives. In this paper, we show the following two results for pricebased network revenue management under a continuous price set. First, we prove that a natural resolving heuristic attains O(1) regret compared with the value of the optimal policy. This improves the [Formula: see text] regret upper bound established in the prior work by Jasin in 2014. Second, we prove that there is an [Formula: see text] gap between the value of the optimal policy and that of the fluid model. This complements our upper bound result by showing that the fluid is not an adequate informationrelaxed benchmark when analyzing pricebased revenue management algorithms. Funding: This work was supported in part by the National Science Foundation [Grant CMMI2145661].more » « less

In this study, we provide an alternative approach for proving the preservation of concavity together with submodularity, and apply it to finite‐horizon non‐stationary joint inventory‐pricing models with general demands. The approach characterizes the optimal price as a function of the inventory level. Further, it employs the Cauchy–Schwarz and arithmetic‐geometric mean inequalities to establish a relation between the one‐period profit and the profit‐to‐go function in a dynamic programming setting. With this relation, we demonstrate that the one‐dimensional concavity of the price‐optimized profit function is preserved as a whole, instead of separately determining the (two‐dimensional) joint concavities in price (or mean demand/risk level) and inventory level for the one‐period profit and the profit‐to‐go function in conventional approaches. As a result, we derive the optimality condition for a base‐stock, list‐price (BSLP) policy for joint inventory‐pricing optimization models with general form demand and profit functions. With examples, we extend the optimality of a BSLP policy to cases with non‐concave revenue functions in mean demand. We also propose the notion of price elasticity of the slope (PES) and articulate the condition as that in response to a price change of the commodity, the percentage change in the slope of the expected sales is greater than the percentage change in the slope of the expected one‐period profit. The concavity preservation conditions for the additive, generalized additive, and location‐scale demand models in the literature are unified under this framework. We also obtain the conditions under which a BSLP policy is optimal for the logarithmic and exponential form demand models.

ABSTRACT When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically do not know precisely how customers will react to price changes in the short term, during a disruption. In this article, we investigate the customer's order variability and the firm's profit under several representative heuristic pricing strategies, including no change at all (fixed pricing strategy), changing the price only (naive pricing strategy), and adjusting the belief and price simultaneously (one‐period correction [1PC] and regression pricing strategies). We show that the fixed pricing strategy creates the most stable customer order process, but it brings lower profit than the naive pricing strategy in most cases. The 1PC pricing strategy produces a more volatile customer order process and smaller profit than the naive one does. Although the regression pricing strategy is a more advanced approach, it leads to lower profit and greater customer order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that (i) completely eliminating the customer order variability by employing a fixed pricing strategy is not advisable and adjusting the price to match supply with demand is necessary to improve the profit; (ii) frequently adjusting the belief about customer behaviors under imperfect information may increase the customer's order variability and reduce the firm's profit. The conclusions are robust to the inventory assumption (i.e., without or with inventory carryover) and the firm's objective (i.e., market clearance or profit maximization).