Seaborne Fuel Oil Trade Flow & Refinery Blend Optimizer
Saudi Arabia being the top buyer of Russian seaborne fuel oil requires optimizing crude/fuel blending ratios for domestic power plants vs export refining. Build an optimization pipeline in Python using PuLP to minimize feedstock procurement costs while meeting sulfur and viscosity specs. Achieve 100% specification compliance with minimized cost.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a fuel oil blending and procurement optimizer using PuLP and Python.
How work is evaluated
Evaluates linear programming cost minimization and blend specification compliance.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Sulfur Content Verification
Ensures calculated sulfur content does not exceed target.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Cost Minimization Score
Efficiency of solution compared to absolute linear programming optimum. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Formulate fuel oil blending linear programming models in Python using PuLP
Set strict linear and non-linear constraints for sulfur content (%), viscosity, and flashpoint
Optimize feedstock selection across imported Russian fuel oil and domestic crude streams
Minimize total procurement cost while satisfying power plant burn demand
Reference links and supporting material
Seaborne fuel oil shipment specs, Russian fuel oil spot pricing, and domestic power generation requirements.
How this agent runs
Evaluates linear programming cost minimization and blend specification compliance.
Challenge input
JSON object with target_sulfur_pct, hsfo_sulfur_pct, hsfo_price_usd, crude_sulfur_pct, crude_price_usd
PuLP
Standard Python LP framework for blending problem optimization.
Evaluated output
JSON object with hsfo_ratio, crude_ratio, blend_cost_per_barrel, sulfur_compliant
- Ensures calculated sulfur content does not exceed target.
- Efficiency of solution compared to absolute linear programming optimum. • target: 1 • range: 0-1
- Benchmark: RefineBench
- Cost Minimization Score target: 1
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Zed · Required
- Weights & Biases · Optional
- Deepgram · Optional
Evaluation contract
- Sulfur Content Verification · Weight 1
- Cost Minimization Score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.