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Project Title
RouteOptim: Advanced Routing Solutions for Modern Delivery
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Timeline:
60 Days
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Tech Stack:
Python
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Demo Link:
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Client Name:
Project Detail:
RouteOptim is an innovative startup solution that focuses on optimizing routing for businesses, whether they're making deliveries, pickups, or performing services at multiple locations. Our aim is to minimize transit time and costs while maximizing customer satisfaction.
Problem Statement:
In the modern fast-paced world, efficient routing has become crucial for businesses. With fuel costs rising, unpredictable urban traffic patterns, and customer demand for timely services, traditional routing methods have become inadequate. Startups, in particular, need affordable, efficient, and quick solutions to remain competitive and ensure profitability.
Solution:
By using advanced operations research techniques combined with real-time data analytics, RouteOptim offers dynamic routing solutions. It adapts to changing conditions like traffic or weather, prioritizes deliveries based on urgency, and even considers variables like vehicle capacity.
Features:
- Dynamic Route Adjustments: Adapts to real-time changes ensuring the most efficient route at any given time.
- Capacity Considerations: Takes into account the capacity of vehicles ensuring no overload.
- Priority Deliveries: Routes can be optimized based on the urgency of deliveries or pickups.
- Analytics Dashboard: A visual representation for businesses to see routes, costs, and predicted delivery times.
Use Cases:
- E-commerce Startups: Ensuring timely deliveries to customers even during peak sale periods.
- Food Delivery Services: Prioritizing deliveries based on food type to ensure hot and fresh food.
- Cab Services: Optimizing routes for pooled rides or scheduled pickups.
Operations Research Specific Points:
- Objective: Minimize total transit time and cost while ensuring timely deliveries and pickups.
- Variables and constraints: 400+ decision variables (routes, stops, etc.), 250+ constraints (vehicle capacity, delivery windows, etc.)
- Methodology: Mixed Integer Linear Programming (MILP) and heuristic algorithms for route optimization.
- Performance matrix: Achieved an average of 28% reduction in total route time and 22% reduction in costs for our clients.
- Model results: Users of RouteOptim report higher customer satisfaction, reduced fuel costs, and an overall increase in delivery efficiency.
Technologies Used:
- Programming Languages: Python, Java
- Frameworks: OR-Tools, Flask
- Tools: Gurobi, React JS, Google Maps API