arXiv:2606.26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. While integrated models capture interactions between these decisions, practical warehouse systems often require decomposed approaches due to organizational boundaries, differing responsibilities, or limited data availability. Existing studies primarily evaluate algorithms for isolated subproblems or fixed subproblem combinations for specific warehouse settings, but lack a general mechanism to determ
The increasing complexity of logistics and the advancements in AI research are converging, making automated optimization of warehouse operations a critical area of focus.
This development allows for more efficient and adaptable supply chain management, directly impacting operational costs and delivery speeds for businesses.
Traditional, inflexible warehouse management systems will be augmented or replaced by AI-driven, context-aware optimization pipelines that dynamically adjust to real-world conditions.
- · Logistics companies
- · E-commerce platforms
- · Warehouse automation providers
- · AI software developers
- · Inefficient manual warehouses
- · Legacy WMS providers
- · Labor-intensive logistics models
Improved efficiency and reduced errors in order fulfillment processes across various industries.
Increased demand for AI expertise and specialized hardware in warehouse and logistics sectors.
Potential for fully autonomous dark warehouses operating with minimal human intervention, reshaping industrial employment.
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Read at arXiv cs.AI