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Deep Neural Networks Solve High-Dimensional Inventory Problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new simulation-based computational method using deep neural networks to solve high-dimensional stochastic joint replenishment problems. This approach approximates the discrete-time problem with a continuous-time impulse control problem, leveraging connections to backward stochastic differential equations and stochastic target problems. The resulting implementable inventory control policy has demonstrated performance matching or exceeding existing benchmarks in test cases up to 50 dimensions. AI

RANK_REASON This is a research paper detailing a novel computational method for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Neural Networks Solve High-Dimensional Inventory Problems

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This is a research paper detailing a novel computational method for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bar{\i}\c{s} Ata, Wouter van Eekelen, Yuan Zhong ·

    A Computational Method for Solving the Stochastic Joint Replenishment Problem in High Dimensions

    arXiv:2511.11830v2 Announce Type: replace-cross Abstract: We consider a discrete-time formulation for a class of high-dimensional stochastic joint replenishment problems. First, we approximate the problem by a continuous-time impulse control problem. Exploiting connections among …

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