Welcome to Logistics Management & Geospatial Route Planning with Python course. This is a comprehensive project based course where you will learn how to optimize logistics operations using linear programming, manage and balance inventory effectively, and plan efficient shipping routes with geospatial mapping. This course is a perfect combination between logistics and operation research, making it an ideal opportunity to practice your supply chain skills while improving your technical knowledge in route optimization. In the introduction session, you will learn the basic fundamentals of logistics management, such as getting to know logistics operation key components and common problems in logistics. Then, in the next section, you will learn how logistics optimization works. This section will cover data collection, defining problems, model formulation, optimization and simulation, geospatial mapping and visualisation, solution implementation and monitoring. Afterward, you will also learn how to find and download logistics dataset from Kaggle, it is a platform that provides a wide range of high quality datasets across many sectors. Once everything is all set, then we will start the project. Firstly, we are going to optimize production cost using linear programming. By doing so, it will help companies to minimize manufacturing expenses while still meeting product demand efficiently. Following that, we are going to optimize transportation cost using linear programming to streamline the movement of products from factories to warehouses at the lowest possible cost. In the next section, we are also going to optimize air freight and sea freight cost using linear programming. This will enable us to reduce shipment costs when shipping goods internationally. Before getting into route optimization, we are going to learn basic geospatial mapping by displaying Folium maps, entering longitude and latitude coordinates, and calculating distance between two cities. After building that foundation, we are going to plan and optimize shipping routes using linear programming and visualize those routes interactively on a geospatial map. Next, we are also going to optimize sea freight routes using Google OR Tools and display the results with Folium. In the next section, we are going to find the most optimal warehouse locations using linear programming and the Haversine formula. This method will enable us to choose the best location for minimizing delivery distances. Following that, we are going to optimize inventory management by determining the optimal reorder point using the Economic Order Quantity formula and we are also going to calculate safety stock levels to avoid stockouts. Afterwards, we are going to optimize truck capacity and fuel cost using linear programming, ensuring each truck is used efficiently with minimal fuel waste. Continuing further, we are going to optimize shipment mode selection between FTL and LTL options using linear programming. This will help us to find the most perfect combination that results in the lowest overall shipping cost. After that, we are going to predict delivery time using a machine learning model, specifically Random Forest, by doing so, we will be able to estimate how long deliveries will take under different conditions. Last but not least, at the end of the course, we are going to map customer base locations using a heatmap created with Folium, allowing us to visualize high demand delivery areas and enable us to make better logistics decisions in the future.

Firstly, before getting into this course, we need to ask these questions to ourselves, why is logistics management very important? Why should we optimize shipping routes? Well here is my answer, efficient logistics management is the backbone of any successful business, ensuring that products are delivered on time, reducing operational costs, and improving overall operational efficiency. By leveraging optimization and geospatial mapping for route planning, companies can map the most efficient delivery routes, minimize delays, and optimize resource allocation. This not only increases profit margins but also strengthens the supply chain, reducing unnecessary expenses and leading to more sustainable business growth.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of logistics management and route optimization

• Learn about logistics optimization workflow. This section covers data collection, defining problems, model formulation, optimization and simulation, geospatial mapping and visualisation, solution implementation and monitoring

• Learn how to optimize production cost using linear programming

• Learn how to optimize transportation cost using linear programming

• Learn how to optimize air freight and sea freight cost using linear programming

• Learn how to display geospatial map using Folium and GeoPy

• Learn how to optimize shipping route with linear programming and Folium

• Learn how to optimize sea freight route with Google OR Tools and Folium

• Learn how to calculate distance using Haversine formula

• Learn how to analyze and choose most optimal location for warehouse

• Learn how to map warehouse, factory, and customer base locations using Folium

• Learn how to calculate optimal order quantity and reorder point

• Learn how to calculate safety stock

• Learn how to optimize truck load capacity and fuel cost using linear programming

• Learn how to optimize FTL vs LTL cost using linear programming

• Learn how to estimate delivery time using machine learning

• Learn how to map customer base locations using Folium heatmap