Developed and Implemented Bus Route Optimization Algorithms

• Day: 2023-11-25
• Time: 07:40 to 08:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Algorithms, Optimization, Dijkstra, Python, Transportation

Description

Session Goal: The session aimed to develop and implement algorithms for optimizing public bus routes using minimum path algorithms, specifically focusing on Dijkstra’s algorithm.

Key Activities:

• Proposed a framework for simplifying bus routes using minimum path algorithms, involving network modeling, stop elimination, and route efficiency evaluation.
• Designed an exercise to apply Dijkstra’s algorithm for optimizing urban bus routes, including function development for minimum path calculation and stop optimization.
• Developed Python functions: cm(recorrido, grid) for calculating the minimum path using Dijkstra’s algorithm and eliminar(recorrido, paradas) for optimizing routes by removing stops.
• Provided pseudocode for Dijkstra’s algorithm, formatted to align with Cormen’s book, emphasizing algorithmic logic.
• Outlined a problem statement for optimizing bus routes in the city of Optimoville, focusing on minimizing total distance and improving public transport efficiency.

Achievements:

• Successfully developed and implemented Python functions for route optimization.
• Created pseudocode representations to aid in understanding and teaching the algorithmic process.

Pending Tasks:

• Further testing and validation of the developed functions in real-world scenarios.
• Exploration of additional optimization techniques to enhance route efficiency.

Evidence

• source_file=2023-11-25.sessions.jsonl, line_number=2, event_count=0, session_id=190b2eb6084fea5e9979c13faf33ef3df424d62ac9b94bfc9b165f4c07ca2730
• event_ids: []