Frontiers in Service Science: Data-Driven Revenue Management: The Interplay of Data, Model, and Decisions
Revenue management (RM) is the application of analytical methodologies and tools that
predict consumer behavior and optimize product availability and prices to maximize a firm's …
predict consumer behavior and optimize product availability and prices to maximize a firm's …
Towards agnostic feature-based dynamic pricing: Linear policies vs linear valuation with unknown noise
In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products
(described by feature vectors) on the fly by learning from the binary outcomes of previous …
(described by feature vectors) on the fly by learning from the binary outcomes of previous …
Learning to Bid in Contextual First Price Auctions✱
In this work, we investigate the problem of how to bid in repeated contextual first price
auctions for a single learner (the bidder). Concretely, at each time t, the learner receives a …
auctions for a single learner (the bidder). Concretely, at each time t, the learner receives a …
Semi-parametric contextual pricing algorithm using cox proportional hazards model
Contextual dynamic pricing is a problem of setting prices based on current contextual
information and previous sales history to maximize revenue. A popular approach is to …
information and previous sales history to maximize revenue. A popular approach is to …
Pricing experimental design: causal effect, expected revenue and tail risk
D Simchi-Levi, C Wang - International Conference on …, 2023 - proceedings.mlr.press
When launching a new product, historical sales data is often not available, leaving price as a
crucial experimental instrument for sellers to gauge market response. When designing …
crucial experimental instrument for sellers to gauge market response. When designing …
Offline feature-based pricing under censored demand: A causal inference approach
Problem definition: We study a feature-based pricing problem with demand censoring in an
offline, data-driven setting. In this problem, a firm is endowed with a finite amount of …
offline, data-driven setting. In this problem, a firm is endowed with a finite amount of …
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
In this paper, we consider the supervised pretrained transformer for a class of sequential
decision-making problems. The class of considered problems is a subset of the general …
decision-making problems. The class of considered problems is a subset of the general …
Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
J Xu, X Wang, YX Wang, J Jiang - arxiv preprint arxiv:2501.18049, 2025 - arxiv.org
We study an online learning problem on dynamic pricing and resource allocation, where we
make joint pricing and inventory decisions to maximize the overall net profit. We consider the …
make joint pricing and inventory decisions to maximize the overall net profit. We consider the …
Power of Second Opportunity: Dynamic Pricing with Second Chance
C Ma, S Tang, Z Zhang - Tsinghua Science and Technology, 2024 - ieeexplore.ieee.org
In this paper, we consider the following dynamic pricing problem. Suppose the market price
v_t of an item arriving at time t is determined by v_t=θ^Tx_t, where x_t is the feature vector of …
v_t of an item arriving at time t is determined by v_t=θ^Tx_t, where x_t is the feature vector of …
Transfer Learning for Nonparametric Contextual Dynamic Pricing
F Wang, F Jiang, Z Zhao, Y Yu - arxiv preprint arxiv:2501.18836, 2025 - arxiv.org
Dynamic pricing strategies are crucial for firms to maximize revenue by adjusting prices
based on market conditions and customer characteristics. However, designing optimal …
based on market conditions and customer characteristics. However, designing optimal …