A practical guide to applying operations research optimization patterns to AI agent planning problems. Covers four classic OR models — set covering, assignment, knapsack, and network flow — implemented in Python using Gurobi. Each pattern is mapped to a real agent planning scenario: skill coverage, project allocation, budget selection, and request routing. Includes code snippets, synthetic datasets, and Monte Carlo simulations showing 20–42% improvements over random allocation strategies.
Table of contents
What is Optimization in Operations Research?How to Optimize Agent Cost and Resource Allocation1. Set-Covering Problem – Agent Skill Coverage2. Assignment Problem – Agent Resource Allocation3. Knapsack Problem – Agent Budgeting4. Network Problem – Agent RoutingTake Home Message127 Impressions