Optimized travel route planning and cost analysis

• Day: 2026-01-14
• Time: 17:30 to 20:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Travel Optimization, Python, Cost Analysis, Google Flights

Description

Session Goal: The session aimed to optimize travel routes and costs using Python algorithms and data analysis tools.

Key Activities:

• Developed a Python script for optimizing travel routes using a directed weighted graph approach.
• Utilized Google Flights for price tracking and itinerary optimization.
• Analyzed solver results to determine optimal paths and costs for travel routing.
• Implemented a tour ranking algorithm for flight nodes.

Achievements:

• Successfully created a workflow for travel cost optimization and route planning.
• Analyzed solver results to gain insights into travel routing involving multiple cities.
• Implemented a Python algorithm for ranking feasible tours between flight nodes.

Pending Tasks:

• Further refine the cost matrix to account for potential asymmetries in edge costs.
• Explore additional features in Google Flights for enhanced itinerary planning.

Evidence

• source_file=2026-01-14.sessions.jsonl, line_number=1, event_count=0, session_id=629475c36c07e40bdabc680316ba00e813d71e2e72ad88f3a5f1c16b99b0105c
• event_ids: []