Fleet Allocation Optimization Using Dynamic Programming

In this 14-minute presentation, we tackle a classic airline fleet allocation problem using Dynamic Programming (DP). The goal is to transport exactly 513 passengers while minimizing total operating costs, using three aircraft types with different capacities, fixed startup costs, and variable operating costs. 📌 What you’ll learn: How to model fleet allocation as a DP problem Backward induction (recursive optimization) State (remaining passengers) and decision (number of aircraft) definitions Bellman equation for cost minimization Step-by-step solution for a real-world example 📊 Fleet specifications (from the example): Type 1: Capacity 200 | Cost = 10 + 3x Type 2: Capacity 170 | Cost = 8 + 3x Type 3: Capacity 80 | Cost = 5 + 2x ✅ Constraint: Nonnegative integer number of aircraft, demand must be met exactly. This method is widely applicable in transportation, logistics, and resource allocation problems.