factory-optimizer 1.0.1

Factory Operations Research & Optimisation Library

factory-optimizer

Factory Operations Research & Optimisation Library

A Python library that solves real factory operations problems using proven OR (Operations Research) techniques. Give it your factory data — materials, costs, supply, demand — and it returns the mathematically optimal plan.

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Install

pip install factory-optimizer

Quick Start

from factory_optimizer import FactoryOptimizer

fo = FactoryOptimizer()
# A welcome guide prints automatically on first import!

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What it solves

Problem	Method	Function
Production Planning	Linear Programming (Simplex)	optimize_production()
Transportation / Distribution	Vogel's Approximation (VAM)	optimize_transportation()
What-if analysis	Scenario simulation engine	simulate_production() / simulate_transportation()

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Usage

1. Production Planning

result = fo.optimize_production(
    resources={"material_A": 500, "material_B": 300},
    products={
        "prod_1": {"materials": {"A": 2, "B": 1}, "profit": 50},
        "prod_2": {"materials": {"A": 1, "B": 2}, "profit": 40},
    },
    report=True   # prints a formatted console report
)

print(result["quantities"])    # {"prod_1": 233.33, "prod_2": 33.33}
print(result["total_profit"])  # 13000.0

2. Transportation Planning

result = fo.optimize_transportation(
    supply={"Factory_1": 100, "Factory_2": 150},
    demand={"Customer_1": 80, "Customer_2": 120, "Customer_3": 50},
    cost_matrix=[[4, 6, 8],
                 [5, 4, 3]],
    report=True
)

print(result["total_cost"])  # 990.0
print(result["plan"])        # {"Factory_1": {"Customer_1": 80, ...}, ...}

3. Scenario Simulation

sim = fo.simulate_production(
    resources={"material_A": 500, "material_B": 300},
    products={
        "prod_1": {"materials": {"A": 2, "B": 1}, "profit": 50},
        "prod_2": {"materials": {"A": 1, "B": 2}, "profit": 40},
    },
    scenarios=[
        {
            "name": "Material A supply +20%",
            "changes": {
                "resources": {"material_A": {"delta": 20, "mode": "percent"}}
            }
        },
        {
            "name": "Prod_1 profit +$10",
            "changes": {
                "products": {"prod_1": {"profit": {"delta": 10, "mode": "absolute"}}}
            }
        },
    ],
    report=True
)

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Requirements

• Python >= 3.8
• scipy >= 1.7.0
• numpy >= 1.21.0

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License

MIT