Leveraging Graphical Network Theory: Route Planning Optimization
In today’s fast-paced world, businesses are constantly seeking innovative solutions to streamline operations and enhance efficiency. One area where optimization can make a significant impact is route planning, especially for companies like ours at Sanku’s Logistics, where every trip’s efficiency directly translates to cost savings and improved service delivery. In this article, I’ll walk you through our journey of developing an optimized model for route planning using graphical network theory, and how it has revolutionized our operational efficiency.
At Sanku’s Logistics, we are committed to leveraging technology to address real-world challenges, particularly in public health. Our integration with Sanku’s logistics and route planning into the end-to-end MLOps platform showcased the capability of our platform to tackle societal issues like combating malnutrition by fortifying foods with essential nutrients. However, we encountered challenges with the malfunctioning dosifier, leading to unnecessary maintenance visits. This prompted us to develop a solution that systematically identifies the most optimal routes, considering various factors such as traffic patterns, warehouse locations, maintenance costs, distance, delivery deadlines, and team capacity.
To tackle this challenge, we turned to graphical network theory, a powerful tool for modeling and optimizing complex systems. Graphical network theory allows us to represent our logistics network as a graph, with nodes representing locations such as warehouses, satellite warehouses, and geofenced millers locations, and edges representing the routes between them. By analyzing this graph, we can identify the most efficient routes that minimize costs and maximize service quality.For further reference please follow this github link with the dataset and the model https://github.com/DennisKevogo/Sanku-ML-Models/tree/main/Route%20Planning%20Optimization%20Model
Our first step was to gather data on our logistics network, including information on warehouse locations, customer demand, delivery deadlines, and historical traffic patterns. We then used this data to construct a graph representing our logistics network, with nodes representing warehouses and customer sites and edges representing the routes between them.
Next, we applied various algorithms from graphical network theory to optimize our route planning process. One such algorithm is Dijkstra’s algorithm, which calculates the shortest path between two nodes in a graph. By applying Dijkstra’s algorithm to our logistics network, we were able to identify the shortest routes between our warehouses and customer sites, minimizing travel time and fuel costs.
Additionally, we incorporated other factors such as delivery deadlines and team capacity into our optimization model. For example, we developed a heuristic algorithm that considers both the distance to a customer site and the time remaining until the delivery deadline to prioritize deliveries and ensure that critical orders are delivered on time.
The result of our efforts was a highly optimized route planning model that significantly improved our operational efficiency. By systematically identifying the most optimal routes, we were able to reduce the number of trips and hours spent on the road, cutting down on fuel costs and maintenance visits. Moreover, our improved route planning process enabled us to meet delivery deadlines more consistently, enhancing customer satisfaction and loyalty.
Below is an image of how we served the model on streamlit for the UI.
Link: https://trackings.streamlit.app/
Looking ahead, we plan to further enhance our route planning model by integrating it with our end-to-end MLOps platform. By incorporating features such as a feature store and a library of pre-built model templates, we aim to streamline machine learning deployments and further improve our operational efficiency.
In conclusion, our journey of developing an optimized model for route planning using graphical network theory has demonstrated the power of technology to drive operational efficiency and improve service delivery. By leveraging advanced algorithms and data-driven insights, we have transformed our route planning process, reducing costs, and enhancing customer satisfaction. As we continue to innovate and evolve, we are confident that our optimized route planning model will serve as a valuable tool for driving positive change and improving the lives of people around the world.
