Project Overview

This project involved creating a sophisticated warehouse simulation model using AnyLogic. The primary goal was to provide a data-driven tool for optimizing warehouse operations and forecasting future resource requirements.

Simulations vs. AI/ML models

Simulations and AI/ML models are different ways of modeling the world: Simulation start from explicit rules and equations about how a system works, while AI/ML models start from data and learn patterns without knowing the underlying rules in advance. They increasingly complement each other, but conceptually they sit on opposite ends of a “rules vs data” spectrum.

Key Features

• Digital Twin: The model functions as a digital twin of the physical warehouse.
• Data-Driven Decisions: Enables decision-making based on simulated performance metrics.
• Resource Planning: Helps predict future needs for staffing, equipment, and space.

Technologies Used

Here is a snippet of the kind of logic involved:

// Example of a custom agent function in AnyLogic
if (pallet.isReadyForShipping()) {
    send(pallet, outboundTruck);
}

You can find more details in the official AnyLogic documentation.