Operations Research: Assignment and Transportation Problems

Efficient resource allocation and logistics planning are critical to reducing costs and maximizing productivity in any organization. This text-based course guides you through the core mathematical frameworks used to solve complex assignment and transportation challenges. You will transition from understanding basic optimization concepts to confidently formulating and solving real-world allocation problems. By reading through clear explanations and structured written examples, you will master both manual algorithmic steps and modern programmatic approaches to optimization. What you'll learn: 1. Understand foundational terminology of transportation networks and assignment matrices. 2. Apply the Hungarian Method to solve complex resource and job allocation problems. 3. Calculate Initial Basic Feasible Solutions using Northwest Corner, Least Cost, and Vogel's Approximation methods. 4. Optimize transportation routes using the Modified Distribution method. 5. Formulate linear programming models for logistics and supply chain scenarios. 6. Practice formulating optimization problems using modern Python libraries. The course begins with essential definitions and foundational mathematical concepts before moving into step-by-step algorithmic solutions. You will then explore how these traditional manual techniques translate into modern digital optimization workflows. This course is designed for beginners in operations research, logistics, business analytics, or computer science, requiring no prior background in advanced optimization. Start reading today to master the mathematical foundations of efficient logistics and resource management.