Logistics Management & Geospatial Route Planning With Python

Optimizing logistics cost and shipping routes with Python, linear programming, OR Tools, geospatial mapping, and Folium

What you'll learn

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 solution implementation

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

Requirements

No previous experience in logistics management is required

Basic knowledge in Python

Description

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 optimizationLearn about logistics optimization workflow. This section covers data collection, defining problems, model formulation, optimization and simulation, geospatial mapping and visualisation, solution implementation and monitoringLearn how to optimize production cost using linear programmingLearn how to optimize transportation cost using linear programmingLearn how to optimize air freight and sea freight cost using linear programmingLearn how to display geospatial map using Folium and GeoPyLearn how to optimize shipping route with linear programming and FoliumLearn how to optimize sea freight route with Google OR Tools and FoliumLearn how to calculate distance using Haversine formulaLearn how to analyze and choose most optimal location for warehouseLearn how to map warehouse, factory, and customer base locations using FoliumLearn how to calculate optimal order quantity and reorder pointLearn how to calculate safety stockLearn how to optimize truck load capacity and fuel cost using linear programmingLearn how to optimize FTL vs LTL cost using linear programmingLearn how to estimate delivery time using machine learningLearn how to map customer base locations using Folium heatmap

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Introduction to Logistics Management & Route Optimization

Lecture 5 Introduction to Logistics Management & Route Optimization

Section 4: Logistics Optimization Workflow

Lecture 6 Logistics Optimization Workflow

Section 5: Finding & Downloading Logistics Dataset From Kaggle

Lecture 7 Finding & Downloading Logistics Dataset From Kaggle

Section 6: Production Cost Optimization with Linear Programming

Lecture 8 Production Cost Optimization with Linear Programming

Section 7: Transportation Cost Optimization with Linear Programming

Lecture 9 Transportation Cost Optimization with Linear Programming

Section 8: Air Freight & Sea Freight Cost Optimization with Linear Programming

Lecture 10 Air Freight & Sea Freight Cost Optimization with Linear Programming

Section 9: Displaying Geospatial Map with Folium & GeoPy

Lecture 11 Displaying Geospatial Map with Folium & GeoPy

Section 10: Optimizing Shipping Route with Linear Programming & Folium

Lecture 12 Optimizing Shipping Route with Linear Programming & Folium

Section 11: Optimizing Sea Freight Route with Google OR Tools & Folium

Lecture 13 Optimizing Sea Freight Route with Google OR Tools & Folium

Section 12: Selecting Optimal Location for Warehouse

Lecture 14 Calculating Distance Using Haversine Formula

Lecture 15 Analyzing & Choosing Most Optimal Location for Warehouse

Lecture 16 Mapping Warehouse, Factory, and Customer Base Locations using Folium

Section 13: Calculating Optimal Order Quantity & Reorder Point

Lecture 17 Calculating Optimal Order Quantity & Reorder Point

Section 14: Calculating Safety Stock

Lecture 18 Calculating Safety Stock

Section 15: Truck Load Capacity & Fuel Cost Optimization with Linear Programming

Lecture 19 Truck Load Capacity & Fuel Cost Optimization with Linear Programming

Section 16: FTL vs LTL Cost Optimization with Linear Programming

Lecture 20 FTL vs LTL Cost Optimization with Linear Programming

Section 17: Estimating Delivery Time with Machine Learning

Lecture 21 Estimating Delivery Time with Machine Learning

Section 18: Mapping Customer Base Locations with Folium Heatmap

Lecture 22 Mapping Customer Base Locations with Folium Heatmap

Section 19: Conclusion & Summary

Lecture 23 Conclusion & Summary

Logistics professionals who are interested in optimizing shipping cost using linear programming,Transportation professionals who are interested in optimizing delivery routes