Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach

• URL: http://arxiv.org/abs/2501.18049v1
• Date: Wed, 29 Jan 2025 23:23:54 GMT
• Title: Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
• Authors: Jianyu Xu, Xuan Wang, Yu-Xiang Wang, Jiashuo Jiang,
• Abstract summary: We study an online learning horizon where we make joint pricing and inventory decisions to maximize the overall net profit. We develop an efficient algorithm that utilizes a "Confidence Bound" strategy over multiple OCO.
• Score: 20.70943884841438
• License:
• Abstract: 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 stochastic dependence of demands on the price, which complicates the resource allocation process and introduces significant non-convexity and non-smoothness to the problem. To solve this problem, we develop an efficient algorithm that utilizes a "Lower-Confidence Bound (LCB)" meta-strategy over multiple OCO agents. Our algorithm achieves $\tilde{O}(\sqrt{Tmn})$ regret (for $m$ suppliers and $n$ consumers), which is optimal with respect to the time horizon $T$. Our results illustrate an effective integration of statistical learning methodologies with complex operations research problems.

Related papers

• Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices [13.68761797358598]
  We study the dynamic pricing problem with knapsack, addressing the challenge of balancing exploration and exploitation under resource constraints. We introduce three algorithms tailored to different informational settings: a Boundary Attracted Re-solve Method for full information, an online learning algorithm for scenarios with no prior information, and an estimate-then-select re-solve algorithm that leverages machine-learned informed prices with known upper bound of estimation errors.
  arXiv  Detail & Related papers  (2025-01-24T00:46:52Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Deep Generative Demand Learning for Newsvendor and Pricing [7.594251468240168]
  We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem. We propose a novel approach leveraging conditional deep generative models (cDGMs) to address these challenges. We provide theoretical guarantees for our approach, including the consistency of profit estimation and convergence of our decisions to the optimal solution.
  arXiv  Detail & Related papers  (2024-11-13T14:17:26Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation [8.089950414444115]
  Two novel online pricing mechanisms are proposed for heterogeneous edge resource allocation. The mechanisms operate in real-time and do not require prior knowledge of demand distribution. The proposed posted pricing schemes allow users to select and pay for their preferred resources, with the platform dynamically adjusting resource prices based on observed historical data.
  arXiv  Detail & Related papers  (2023-02-14T10:21:14Z)
• Computation Resource Allocation Solution in Recommender Systems [19.456109814747048]
  We propose a computation resource allocation solution (CRAS) that maximizes the business goal with limited computation resources and response time. The effectiveness of our method is verified by extensive experiments based on the real dataset from Taobao.com.
  arXiv  Detail & Related papers  (2021-03-03T08:41:43Z)
• Online Apprenticeship Learning [58.45089581278177]
  In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms.
  arXiv  Detail & Related papers  (2021-02-13T12:57:51Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• Regularized Online Allocation Problems: Fairness and Beyond [7.433931244705934]
  We introduce the emphregularized online allocation problem, a variant that includes a non-linear regularizer acting on the total resource consumption. In this problem, requests repeatedly arrive over time and, for each request, a decision maker needs to take an action that generates a reward and consumes resources. The objective is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer subject to the resource constraints.
  arXiv  Detail & Related papers  (2020-07-01T14:24:58Z)
• Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation [49.69139684065241]
  Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
  arXiv  Detail & Related papers  (2020-04-02T17:04:52Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.