RetailOpt-190: A Retail Supply Chain Benchmark for Text-to-Optimization

RetailOpt-190 is a solver-validated benchmark for evaluating semantic reliability in text-to-optimization. It tests whether LLM-based agents can reconstruct the intended optimization structure—not just produce runnable code.

Dataset Summary

RetailOpt-190 contains 190 retail supply chain optimization instances designed to test compositional consistency in LLM-generated optimization code. Each instance includes a natural-language problem description, structured JSON data, and ground truth solutions from a validated MILP solver.

The benchmark spans 8 scenario families and 38 archetypes covering core retail planning mechanisms:

Family	Name	Archetypes	Key Mechanisms
F1	Core Operations	4	Multi-period inventory, seasonal demand, perishability
F2	Assortment & Substitution	6	Product substitution, promotions, ultra-short shelf life
F3	Resource Constraints	4	Storage bottleneck, supply bottleneck, volumetric limits
F4	Demand Dynamics	6	Demand surge, supply risk, peak failure
F5	Feasibility Stress	4	Impossible demand, storage overflow, strict service traps
F6	Discrete Logistics	4	Lead time, MOQ, pack size, fixed order cost
F7	Network & Multi-Echelon	6	Transshipment, hub-spoke, multi-sourcing
F8	Omni-channel	4	Reverse logistics, labor constraints, sustainability

Languages

English

Dataset Structure

Data Fields

Field	Type	Description
scenario_id	string	Unique scenario identifier (e.g., retail_f1_base_v0)
prompt	string	Natural-language problem description with structure cues
data	string	JSON-formatted instance data (parse with json.loads())
reference_status	string	Ground truth solver status (OPTIMAL, INFEASIBLE, etc.)
reference_objective	float	Ground truth objective value (null if infeasible)

Data Splits

Split	Examples
test	190

Usage

Loading the Dataset

from datasets import load_dataset
import json

# Load dataset
dataset = load_dataset("junbolian/RetailOpt-190", split="test")

# Access a sample
sample = dataset[0]
print(sample['scenario_id'])  # e.g., "retail_f1_base_v0"
print(sample['prompt'][:200])  # First 200 chars of prompt

# Parse JSON data
data = json.loads(sample['data'])
print(data['periods'])  # Number of time periods
print(data['products'])  # List of products

Benchmarking Your Model

from datasets import load_dataset
import json

dataset = load_dataset("junbolian/RetailOpt-190", split="test")

for sample in dataset:
    # Get prompt and data
    prompt = sample['prompt']
    data = json.loads(sample['data'])

    # Generate code with your LLM
    generated_code = your_llm(prompt)

    # Execute generated code
    exec(generated_code, {'data': data})

    # Compare with ground truth
    print(f"Reference: {sample['reference_status']}, {sample['reference_objective']}")

Evaluation Metrics

• Execution Rate: Percentage of instances that run without error
• Accuracy: Percentage matching ground truth (status + objective within tolerance)
• Silent Failure Rate: Executable code with incorrect answer

Accuracy Tolerances

Family	Problem Type	Tolerance
F1-F5, F7-F8	LP / easy MIP	0.01%
F6	Hard MIP (MOQ, pack-size)	10%

Dataset Creation

Source Data

All instances are synthetically generated from 38 archetype specifications. Each archetype is instantiated with 5 numerical variants (v0-v4) via controlled parameter perturbations.

Annotations

Ground truth solutions are computed using a validated MILP solver (Gurobi) with the following settings:

• TimeLimit: 60 seconds
• MIPGap: 1%
• Threads: 1

Additional Information

Citation

@article{lian2026reloop,
  author    = {Junbo Jacob Lian and Yujun Sun and Huiling Chen and Chaoyu Zhang and Chung-Piaw Teo},
  title     = {ReLoop: Detecting Silent Failures in LLM-Generated Optimization Code via Behavioral Verification},
  journal   = {arXiv preprint},
  year      = {2026}
}

License

• Code: MIT
• Data: CC BY 4.0

Related Resources

• ReLoop Framework: https://github.com/junbolian/ReLoop - Complete implementation of the ReLoop verification pipeline