Deep Neural Networks Solve High-Dimensional Inventory Problems

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]

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• backward stochastic differential equations
• deep neural networks
• impulse control problem
• inventory control policy
• stochastic joint replenishment problem

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• other

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