Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment

• URL: http://arxiv.org/abs/2605.25888v1
• Date: Mon, 25 May 2026 14:16:45 GMT
• Title: Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
• Abstract summary: We study how an e-commerce firm should make real-time fulfillment decisions when multi-item customer orders arrive sequentially and future demand is unknown.<n>We formulate an adversarial online model with multiple FDCs, one regional distribution center (RDC), multi-unit multi-item orders, and item-specific and time-varying variable costs.<n>Our theoretical objective is to characterize when simple, interpretable, and implementable fulfillment rules can perform nearly as well as an optimal clairvoyant planner.
• Score: 12.192024626404482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study how an e-commerce firm should make real-time fulfillment decisions in a two-layer distribution network when multi-item customer orders arrive sequentially and future demand is unknown. The central managerial tension is whether to use scarce front distribution center (FDC) inventory to save current fulfillment cost or preserve that inventory for future orders that may be more valuable to serve locally. We formulate an adversarial online model with multiple FDCs, one regional distribution center (RDC), multi-unit multi-item orders, and item-specific and time-varying variable costs. Our theoretical objective is to characterize when simple, interpretable, and implementable fulfillment rules can perform nearly as well as an optimal clairvoyant planner. We develop a family of Gated Priority-based Greedy policies, derive competitive-ratio guarantees under both time-varying and time-invariant cost structures, and establish matching or near-matching lower bounds for any online algorithm. Numerical experiments show that the proposed policies perform strongly relative to generalized myopic and forecast-based benchmarks. The analysis yields managerial guidance on when local inventory should be protected, when splitting orders is worth the fixed-cost burden, and how the relative magnitudes of fixed and variable costs determine the value of more sophisticated optimization.

Related papers

• Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation [64.51088225051959]
  Heterogeneous AI systems can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost.<n>We formalize this tradeoff as an instance of Pandora's Box, the classical problem of optimal search with costly inspection.<n>Under a Gaussian signal model, the resulting policies have closed-form value-of-information expressions that determine, for each specialist and input, whether refining the value estimate is worth its cost.
  arXiv  Detail & Related papers  (2026-08-20T17:54:37Z)
• Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application [12.486418828615077]
  Sequential contextual programs model real-time decision systems in which each time commits to an action under uncertainty.<n>Off-the-shelf optimization solvers achieve high decision quality offline but typically run in seconds to minutes per instance.<n>This paper develops a learning-based optimization proxy: a scenario-embedded neural network trained offline on solver-generated labels, paired online with a decoder that enforces feasibility.
  arXiv  Detail & Related papers  (2026-06-24T03:45:52Z)
• Selective Ensemble Based on Preference-Directed Multi-Objective Bandits [90.75513823660775]
  We formalize a sequential decision problem under partially specified linear preferences.<n>We then propose a preference-directed upper confidence bound (PrefUCB) algorithm.<n> Experiments on large pre-trained model selective ensemble tasks and online asset allocation under institutional mandates validate our method.
  arXiv  Detail & Related papers  (2026-06-20T07:52:46Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• Plan Before You Trade: Inference-Time Optimization for RL Trading Agents [8.39123686251568]
  Reinforcement learning agents for portfolio management are typically trained and deployed as static policies.<n>We propose a plugin inference-time optimization framework inspired by Model Predictive Control (MPC)<n>Our framework is compatible with any pre-trained agent and adapts the policy to the forecaster's predictions without any retraining.
  arXiv  Detail & Related papers  (2026-05-12T18:58:03Z)
• One Global Model, Many Behaviors: Stockout-Aware Feature Engineering and Dynamic Scaling for Multi-Horizon Retail Demand Forecasting with a Cost-Aware Ordering Policy (VN2 Winner Report) [0.0]
  This report presents the winning solution for the VN2 Inventory Planning Challenge.<n>It combines a single global multi-horizon forecasting model with a cost-aware ordering policy.<n>Although developed for the VN2 setting, the proposed approach can be extended to real-world applications.
  arXiv  Detail & Related papers  (2026-01-26T19:36:52Z)
• Automatic Policy Search using Population-Based Hyper-heuristics for the Integrated Procurement and Perishable Inventory Problem [3.4792548480344245]
  We develop a discrete-event simulation environment to compare two optimization strategies for this multi-item, multi-supplier problem.<n>Results from twelve distinct instances demonstrate that the hyper-heuristic framework consistently identifies superior policies.
  arXiv  Detail & Related papers  (2025-11-02T01:27:52Z)
• Bayesian Optimization for Dynamic Pricing and Learning [0.306238659426286]
  Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand.<n>Traditional approaches often assume a specific parametric form for the demand function, enabling the use of reinforcement learning (RL) to identify near-optimal pricing strategies.<n>We propose a nonparametric approach to dynamic pricing that avoids restrictive modeling assumptions.
  arXiv  Detail & Related papers  (2025-10-14T12:28:06Z)
• 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)
• TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting [51.37167759339485]
  We propose Temporal-Aligned Transformer (TAT), a multi-horizon forecaster leveraging apriori-known context variables for improving predictive performance.<n>Our model consists of an encoder and decoder, both embedded with a novel Temporal Alignment Attention (TAA) designed to learn context-dependent alignment for peak demand forecasting.<n>We demonstrate that TAT brings up to 30% accuracy on peak demand forecasting while maintaining competitive overall performance compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-14T14:51:24Z)
• 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)
• Spatial Supply Repositioning with Censored Demand Data [10.797160099834306]
  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.
  arXiv  Detail & Related papers  (2025-01-31T15:16:02Z)
• 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)
• Deep Reinforcement Learning for Traveling Purchaser Problems [63.37136587778153]
  The traveling purchaser problem (TPP) is an important optimization problem with broad applications.<n>We propose a novel approach based on deep reinforcement learning (DRL), which addresses route construction and purchase planning separately.<n> Experiments on various synthetic TPP instances and the TPPLIB benchmark demonstrate that our DRL-based approach can significantly outperform well-established TPPs.
  arXiv  Detail & Related papers  (2024-04-03T05:32:10Z)
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
  Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies. Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
  arXiv  Detail & Related papers  (2023-10-24T01:00:01Z)

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.