Inventory-Optimisation-Demo

Example code for a talk about inventory modelling and strategy optimisation in Python.

Using:

• simpy for discrete event simulation
• matplotlib for graphs
• epsilon-greedy algorithm for input optimisation
• reveal-js framework for the presentation
• excalidraw for diagrams

Slides viewable here

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Overview

Output

With only the Purchasing event

After adding aged stock clearout

After adding sales

Result of greedy-epsilon optimisation

Comparison of different Epsilon values (reward per iteration)

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Setup & running

Install required packages: pip install -r .\requirements.txt

Run the scripts with: python simulation.py or python optimisation.py

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Creating your own optimiser

Either use the simple greedy-epsilon-agent-based optimiser as a starting point, or in a new file:

from simulation import PurchaseOrder, Simulation

# Initial inputs
purchase_orders = [PurchaseOrder(0, 20), PurchaseOrder(100, 100)]

for iteration in range(100):
    simulation = Simulation(purchase_orders)
    simulation.run()
    simulation.plot()

    # Customise this calculation depending on requirements
    cost = sum(simulation.availabilities)
    
    # Calculate a new set of more-optimal inputs
    purchase_orders = []