Warehouse Robot Optimization

Overview

This project simulates a warehouse robot and finds the most efficient way to complete delivery tasks using a brute-force optimization approach.

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Problem

The robot operates in a 5×5 grid:

• R → Start position
• L → Loading dock
• S1–S6 → Shelves
• . → Empty space

Each task requires the robot to:

1. Go to the loading dock
2. Pick up an item
3. Deliver it to a shelf

The challenge is that task order affects total distance.

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Approach

• Generate all possible task orders (permutations)
• Compute total distance for each
• Select the minimum-cost solution

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Distance Metric

Uses Manhattan distance:

• Horizontal and vertical movement only
• No diagonal movement

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Result

• Finds the optimal solution for 1–3 tasks
• Demonstrates algorithmic problem-solving
• Limited scalability due to brute-force approach

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Reflection

This project improved my:

• Problem decomposition
• Algorithmic thinking
• Code structure and debugging

It also reinforced concepts like functions, control flow, and basic data structures.

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Run

git clone https://github.com/your-username/warehouse-robot-optimization.git
cd warehouse-robot-optimization
python3 Final.py