ARXIV:2603.24883 · WAREHOUSE OPERATIONS AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization

Kalle Kujanpää · Yuying Zhu · Kristina Klinkner · Shervin Malmasi · arXiv

AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings. Operational decision-making can be supported at different levels of abstraction, with different…

METHOD

Full abstract

We investigate machine learning approaches for optimizing real-time staffing decisions in semi-automated warehouse sortation systems. Operational decision-making can be supported at different levels of abstraction, with different trade-offs. We evaluate two approaches, each in a matching simulation environment. First, we train custom Transformer-based policies using offline reinforcement learning on detailed historical state representations, achieving a 2.4% throughput improvement over historical baselines in learned simulators. In high-volume warehouse operations, improvements of this size translate to significant savings. Second, we explore LLMs operating on abstracted, human-readable state descriptions. These are a natural fit for decisions that warehouse managers make using high-level operational summaries. We systematically compare prompting techniques, automatic prompt optimization, and fine-tuning strategies. While prompting alone proves insufficient, supervised fine-tuning combined with Direct Preference Optimization on simulator-generated preferences achieves performance that matches or slightly exceeds historical baselines in a hand-crafted simulator. Our findings demonstrate that both approaches offer viable paths toward AI-assisted operational decision-making. Offline RL excels with task-specific architectures. LLMs support human-readable inputs and can be combined with an iterative feedback loop that can incorporate manager preferences.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. While prompting alone proves insufficient, supervised fine-tuning combined with Direct Preference Optimization on simulator-generated preferences achieves performance that matches or slightly exceeds historical baselines…

WHY NOW

Warehouse Operations AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

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Opportunity summary

Score7.0

PainAI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

Segment

Warehouse Operations AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

AI-powered system to optimize warehouse staffing decisions using offline reinforcement learning and fine-tuned LLMs, leading to significant throughput improvements and cost savings.

Segment

Warehouse Operations AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization

Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization

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