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omnitower-solver-config
alirezaaminzadeh/omnitower-solver-config
omnitower-solver-config is a machine learning model from alirezaaminzadeh. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository publishes the solver configuration and benchmark summary for the Omnichannel Fulfillment and Returns Control Tower. It is a configuration / decision-model artifact, not a trained neural network: the "m…
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At a glance
- License
- mit
- Access
- Public
- Created
- Aug 8, 2026
- Updated
- Aug 8, 2026
- SHA
- c2f738f8
Model card
From the Hugging Face model README
Omnichannel Fulfillment — Solver Configuration
This repository publishes the solver configuration and benchmark summary for the Omnichannel Fulfillment and Returns Control Tower. It is a configuration / decision-model artifact, not a trained neural network: the "model" is a multi-stage optimization pipeline combining a greedy-marginal-cost order allocator, an Adaptive Large Neighborhood Search improvement loop, a bid-price-style dynamic admission controller, and a rule-based reverse-logistics disposition policy.
Components
| Stage | Method |
|---|---|
| Inventory Allocation / Order Fulfillment | Greedy Marginal Cost heuristic over candidate sources (DC, regional WH, cross-dock, supplier, store) |
| Allocation Improvement | Adaptive Large Neighborhood Search (destroy random batch, reinsert by priority, accept if cost improves) |
| Assortment Substitution | Substitute-group lookup triggered on stockout |
| Dynamic Admission Control | Scarcity-scored accept / defer-to-backorder decision per incoming order |
| Multi-Option Routing | Fast / Economical / Green objective-weighted allocation for the same order |
| Reverse Logistics | Rule-based disposition: restock store / restock DC / repair / recycle / dispose |
Cost Model
| Term | Value (USD) |
|---|---|
| Per km, standard shipping | 0.055 |
| Per km, express shipping | 0.145 |
| Per unit picked | 1.35 |
| Per unit packed | 0.85 |
| Per split shipment | 3.20 |
See config.json for the full manifest and the latest benchmark summary
across all five demo network scenarios.
Intended Use
Decision-support and portfolio demonstration for omnichannel retail fulfillment and reverse-logistics planning. Not certified for live operational use; all data is synthetic.
Limitations
- Synthetic network topology and order stream, not a real retailer's data.
- No live carrier-rate, WMS, or OMS feed integration.
- Return quality scoring and disposition rules are simplified surrogates of a real grading/QA workflow.
- License
- mit
- Created
- Aug 8, 2026
- Updated
- Aug 8, 2026