Spatial Supply Repositioning with Censored Demand Data

• URL: http://arxiv.org/abs/2501.19208v2
• Date: Fri, 17 Oct 2025 02:54:36 GMT
• Title: Spatial Supply Repositioning with Censored Demand Data
• Authors: Hansheng Jiang, Chunlin Sun, Zuo-Jun Max Shen,
• Abstract summary: We consider a network inventory system motivated by one-way, on-demand vehicle sharing services.<n>Finding an optimal policy in such a general inventory network is analytically and computationally challenging.<n>Our work highlights the critical role of inventory in the viability of shared mobility businesses.
• Score: 10.797160099834306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a network inventory system motivated by one-way, on-demand vehicle sharing services. Under uncertain and correlated network demand, the service operator periodically repositions vehicles to match a fixed supply with spatial customer demand while minimizing costs. Finding an optimal repositioning policy in such a general inventory network is analytically and computationally challenging. We introduce a base-stock repositioning policy as a multidimensional generalization of the classical inventory rule to $n$ locations, and we establish its asymptotic optimality under two practically relevant regimes. We present exact reformulations that enable efficient computation of the best base-stock policy in an offline setting with historical data. In the online setting, we illustrate the challenges of learning with censored data in networked systems through a regret lower bound analysis and by demonstrating the suboptimality of alternative algorithmic approaches. We propose a Surrogate Optimization and Adaptive Repositioning algorithm and prove that it attains an optimal regret of $O(n^{2.5} \sqrt{T})$, which matches the regret lower bound in $T$ with polynomial dependence on $n$. Our work highlights the critical role of inventory repositioning in the viability of shared mobility businesses and illuminates the inherent challenges posed by data and network complexity. Our results demonstrate that simple, interpretable policies, such as the state-independent base-stock policies we analyze, can provide significant practical value and achieve near-optimal performance.

Related papers

• Non-Stationary Inventory Control with Lead Times [0.4927882324444362]
  We study non-stationary single-item, periodic-review inventory control problems.<n>We analyze how demand non-stationarity affects learning performance across inventory models.
  arXiv  Detail & Related papers  (2026-02-05T15:53:37Z)
• MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization [56.074760766965085]
  Group-Relative Policy Optimization has emerged as an efficient paradigm for aligning Large Language Models (LLMs)<n>We propose MAESTRO, which treats reward scalarization as a dynamic latent policy, leveraging the model's terminal hidden states as a semantic bottleneck.<n>We formulate this as a contextual bandit problem within a bi-level optimization framework, where a lightweight Conductor network co-evolves with the policy by utilizing group-relative advantages as a meta-reward signal.
  arXiv  Detail & Related papers  (2026-01-12T05:02:48Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning [66.4260157478436]
  We study reinforcement learning in the policy learning setting.<n>The goal is to find a policy whose performance is competitive with the best policy in a given class of interest.
  arXiv  Detail & Related papers  (2025-07-06T14:40:05Z)
• Quantile-Optimal Policy Learning under Unmeasured Confounding [55.72891849926314]
  We study quantile-optimal policy learning where the goal is to find a policy whose reward distribution has the largest $alpha$-quantile for some $alpha in (0, 1)$.<n>Such a problem suffers from three main challenges: (i) nonlinearity of the quantile objective as a functional of the reward distribution, (ii) unobserved confounding issue, and (iii) insufficient coverage of the offline dataset.
  arXiv  Detail & Related papers  (2025-06-08T13:37:38Z)
• Accelerating RL for LLM Reasoning with Optimal Advantage Regression [52.0792918455501]
  We propose a novel two-stage policy optimization framework that directly approximates the optimal advantage function.<n>$A$*-PO achieves competitive performance across a wide range of mathematical reasoning benchmarks.<n>It reduces training time by up to 2$times$ and peak memory usage by over 30% compared to PPO, GRPO, and REBEL.
  arXiv  Detail & Related papers  (2025-05-27T03:58:50Z)
• Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand [7.289672463326423]
  We study the offline sequential feature-based pricing and inventory control problem.<n>Our goal is to leverage the offline dataset to estimate the optimal pricing and inventory control policy.
  arXiv  Detail & Related papers  (2025-04-14T02:57:51Z)
• Distributionally Robust Policy Learning under Concept Drifts [33.44768994272614]
  This paper studies a more nuanced problem -- robust policy learning under the concept drift.<n>We first provide a doubly-robust estimator for evaluating the worst-case average reward of a given policy.<n>We then propose a learning algorithm that outputs the policy maximizing the estimated policy value within a given policy class.
  arXiv  Detail & Related papers  (2024-12-18T19:53:56Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [86.99017195607077]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents.<n>Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Scalable Online Exploration via Coverability [45.66375686120087]
  Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We introduce a new objective, $L_Coverage, which generalizes previous exploration schemes and supports three fundamental desideratas. $L_Coverage enables the first computationally efficient model-based and model-free algorithms for online (reward-free or reward-driven) reinforcement learning in MDPs with low coverability.
  arXiv  Detail & Related papers  (2024-03-11T10:14:06Z)
• Efficient Methods for Non-stationary Online Learning [61.63338724659592]
  We present efficient methods for optimizing dynamic regret and adaptive regret, which reduce the number of projections per round from $mathcalO(log T)$ to $1$.<n>We also study an even strengthened measure, namely the interval dynamic regret'', and reduce the number of projections per round from $mathcalO(log2 T)$ to $1$.
  arXiv  Detail & Related papers  (2023-09-16T07:30:12Z)
• Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems [12.487990897680422]
  We present a novel optimization strategy for image reconstruction tasks under analysis-based image regularization. We parameterize such regularizers using potential functions that correspond to weighted extensions of the $ell_pp$-vector and $mathcalS_pp$ Schatten-matrix quasi-norms. We show that thanks to the convergence guarantees of our proposed minimization strategy, such optimization can be successfully performed with a memory-efficient implicit back-propagation scheme.
  arXiv  Detail & Related papers  (2023-08-10T17:59:46Z)
• Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization [2.9016349714298157]
  We argue that inventory management presents unique opportunities for the reliable application of deep reinforcement learning (DRL)<n>The first is Hindsight Differentiable Policy Optimization (HDPO), which uses pathwise gradients from offline counterfactual simulations to directly and efficiently optimize policy performance.<n>We exploit Graph Neural Networks (GNNs) as a natural inductive bias for encoding supply chain structure, demonstrate that they can represent optimal and near-optimal policies in two theoretical settings, and empirically show that they reduce data requirements across six diverse inventory problems.
  arXiv  Detail & Related papers  (2023-06-20T02:58:25Z)
• Cooperative Thresholded Lasso for Sparse Linear Bandit [6.52540785559241]
  We present a novel approach to address the multi-agent sparse contextual linear bandit problem. It is first algorithm that tackles row-wise distributed data in sparse linear bandits. It is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret.
  arXiv  Detail & Related papers  (2023-05-30T16:05:44Z)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation [69.0695698566235]
  We study reinforcement learning with linear function approximation and adversarially changing cost functions. We present a computationally efficient policy optimization algorithm for the challenging general setting of unknown dynamics and bandit feedback.
  arXiv  Detail & Related papers  (2023-01-30T17:26:39Z)
• Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning [23.222448307481073]
  We propose a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage. Our algorithm combines the marginalized importance sampling framework with the actor-critic paradigm. We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.
  arXiv  Detail & Related papers  (2023-01-30T07:53:53Z)
• COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation [73.17078343706909]
  offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset. We present an offline constrained RL algorithm that optimize the policy in the space of the stationary distribution. Our algorithm, COptiDICE, directly estimates the stationary distribution corrections of the optimal policy with respect to returns, while constraining the cost upper bound, with the goal of yielding a cost-conservative policy for actual constraint satisfaction.
  arXiv  Detail & Related papers  (2022-04-19T15:55:47Z)
• Globally Convergent Policy Search over Dynamic Filters for Output Estimation [64.90951294952094]
  We introduce the first direct policy search algorithm convex which converges to the globally optimal $textitdynamic$ filter. We show that informativity overcomes the aforementioned degeneracy.
  arXiv  Detail & Related papers  (2022-02-23T18:06:20Z)
• Online Allocation with Two-sided Resource Constraints [44.5635910908944]
  We consider an online allocation problem subject to lower and upper resource constraints, where the requests arrive sequentially. We propose a new algorithm that obtains $1-O(fracepsilonalpha-epsilon)$ -competitive ratio for the offline problems that know the entire requests ahead of time.
  arXiv  Detail & Related papers  (2021-12-28T02:21:06Z)
• Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy [95.98698822755227]
  We make the first attempt to study risk-sensitive deep reinforcement learning under the average reward setting with the variance risk criteria. We propose an actor-critic algorithm that iteratively and efficiently updates the policy, the Lagrange multiplier, and the Fenchel dual variable.
  arXiv  Detail & Related papers  (2020-12-28T05:02:26Z)
• Online Stochastic Optimization with Wasserstein Based Non-stationarity [12.91020811577007]
  We consider a general online optimization problem with multiple budget constraints over a horizon of finite time periods. The objective of the decision maker is to maximize the cumulative reward subject to the budget constraints. This formulation captures a wide range of applications including online linear programming and network revenue management.
  arXiv  Detail & Related papers  (2020-12-13T04:47:37Z)
• Provably Efficient Safe Exploration via Primal-Dual Policy Optimization [105.7510838453122]
  We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation. We present an provably efficient online policy optimization algorithm for CMDP with safe exploration in the function approximation setting.
  arXiv  Detail & Related papers  (2020-03-01T17:47:03Z)

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.