Automated Algorithm Selection for Warehouses: CASOP Synthesizes Over 1 Million Valid Optimization Pipelines

Hello. This is Keito Inoshita from Affectosphere Group.

SCM consultants and logistics DX teams know the problem well. “Where do we slot which products?” “How do we batch orders for picking?” “What routing path does the picker follow?” Each question has its specialists — and each specialist optimizes independently.

But these three decisions are tightly coupled. Change storage assignment, and the optimal batch grouping shifts. Change the batching, and the routing assumptions break. Optimizing each in isolation rarely achieves global efficiency.

A June 2026 arXiv preprint (Bischoff et al., arXiv:2606.26852) proposes CASOP — Context-Aware Synthesis of Optimization Pipelines — a framework that handles all three jointly and automatically recommends the right algorithm combination for any given warehouse.

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Today’s 3 Points

1. CASOP integrates storage assignment, order batching, and picker routing into a unified five-component framework that synthesizes optimization pipelines automatically.
2. Tested across 7 benchmark sets, CASOP generated over 1 million valid pipelines and demonstrated automated recommendation of the best algorithm combination for each warehouse profile.
3. The full framework is published as open-source, allowing practitioners to use and extend it immediately.

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① Why Coupled Decisions Break Siloed Optimization

The core challenge in warehouse optimization is interdependence.

Storage assignment determines which products go to which shelf locations. Order batching groups customer orders into picking tours. Picker routing determines the shortest path to complete each batch.

Optimize these separately and a familiar failure mode emerges. ABC-ranked storage changes the item distribution your batching algorithm assumed. Regroup the batches and the routing algorithm’s shelf access patterns break down. Each component is locally optimal; the system as a whole is not.

CASOP’s premise is that the pipeline — the full combination of algorithms across all three phases — needs to be synthesized and evaluated as a unit.

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② CASOP’s Five Components

The framework is built from five interlocking components.

The algorithm repository is a structured catalog of algorithms available for each decision phase. For storage assignment this includes methods like ABC ranking and affinity-based placement; for order batching, seed algorithms and nearest-neighbor approaches; for routing, S-shaped traversal and largest-gap methods, among others.

Semantic data cards provide a structured vocabulary for describing warehouse characteristics: SKU count, order frequency, average lines per order, aisle layout dimensions. These translate a real warehouse’s context into a format the system can reason over.

The problem taxonomy classifies warehouse optimization problems by their structural properties. Distinguishing a high-frequency small-order operation from a low-SKU high-volume one allows algorithm selection to be profile-specific rather than generic.

The pipeline synthesizer combines the repository, data cards, and taxonomy to generate candidate algorithm combinations for a given warehouse profile. Across 7 benchmark sets, this component produced over 1 million valid pipelines.

The evaluator ranks the generated pipelines through simulation and surfaces the recommended combination with supporting performance metrics.

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③ What Over 1 Million Pipelines Actually Means

The scale of enumeration matters. One million valid pipelines means the solution space has been explored systematically rather than sampled heuristically.

Traditional warehouse consulting relies on accumulated practitioner intuition — “for this type of operation, this algorithm family tends to work.” That knowledge is tacit, difficult to transfer, and slow to update when new methods appear.

CASOP replaces the heuristic with a documented map. For a given warehouse profile, which algorithm combination performed best in simulation? The answer is now retrievable from a structured search rather than recalled from memory.

The open-source release also means the repository can be extended — new algorithms added, domain-specific taxonomies built — without rebuilding the framework from scratch.

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WMS Add-on and Consulting Diagnostic Tool Applications

Two business applications follow directly from this architecture.

The first is a WMS add-on module. An input form collects the customer’s warehouse parameters — SKU count, daily order volume, average lines per order, layout dimensions — converts them to a semantic data card, runs the synthesizer, and surfaces ranked pipeline recommendations. For SIs selling WMS platforms to 3PL providers or logistics subsidiaries, this becomes a differentiating “automated algorithm selection” feature.

The second is a standardized SCM consulting diagnostic tool. The workflow maps naturally: intake questionnaire → semantic data card → pipeline synthesis → expected improvement estimate → recommendation report. This replaces a consultant-dependent proposal process with one anchored in simulation results. The claim “this algorithm combination is recommended for your profile” gains measurable backing.

For both use cases, practical KPIs are picker travel distance reduction, order processing time per line, and daily throughput (orders per picker-hour).

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From Tacit Knowledge to Documented Evidence

Warehouse optimization has long been driven by expert intuition — pattern-matched recommendations from practitioners who have seen many operations. That expertise is valuable and irreplaceable in full. But CASOP suggests that a meaningful portion of it can be formalized: algorithm repository + taxonomy + simulation evaluation.

There are real-world variables the framework does not directly model — seasonal demand shifts, workforce scheduling constraints, damage rates by storage zone. A full consulting engagement still needs to account for these.

But the ability to say “our recommendation is grounded in a systematic search across 1 million+ evaluated pipelines for warehouses with your profile” changes the evidentiary foundation of the proposal. That seems like genuine value for logistics DX teams trying to move from intuition-based to data-grounded decision making.

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Reference

1. Bischoff, J., Meyer, A., Mohring, U., Dunke, F., Barlang, M., Subas, Ö. N., Kutabi, H., Nickel, S., & Furmans, K. (2026). Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization. arXiv preprint.

* This article was written in part with AI assistance and may contain inaccuracies.