Optimization for Practitioners
Concept & Motivation
Every organization allocates scarce resources — people, money, time, capacity, materials. Most do it by experience, spreadsheets, and negotiation. A few do it optimally.
Mathematical optimization has been transforming logistics, manufacturing, finance, and operations for decades. But the methods remain locked behind academic jargon and specialist software, inaccessible to the managers and planners who face these problems daily.
This workshop opens the door. You learn to see optimization problems in your daily work, formulate them precisely, and solve them using tools that range from Excel to industrial-grade solvers.
What You’ll Learn
- Recognizing optimization problems — decision variables, constraints, and objectives. Training your eye to spot them in scheduling, allocation, routing, and planning
- Linear Programming (LP) — the foundational method. Classic problems (production mix, diet/blending, transportation) that teach the structure
- Mixed Integer Linear Programming (MILP) — when decisions are binary (yes/no, open/close, assign/skip). Scheduling, facility location, crew assignment
- Formulation practice — translating business narratives into mathematical models. The hardest and most valuable skill
- Solver tools — from Excel Solver (quick validation) to open-source solvers (HiGHS, COIN-OR) to commercial platforms (Gurobi). When to use which
- Interpreting results — sensitivity analysis, shadow prices, reduced costs. Understanding not just what the optimal solution is, but why and what changes it
- Optimization vs. heuristics vs. simulation — knowing the limits. When good-enough beats optimal, and when it doesn’t
Who This Is For
- Operations managers and production planners allocating resources across shifts, machines, or projects
- Supply chain professionals optimizing routing, inventory, or supplier selection
- Financial analysts working on portfolio allocation, capital budgeting, or cost minimization
- Anyone whose job involves deciding how to split limited resources across competing demands
Basic algebra is sufficient. The course builds from business narratives to mathematical formulations step by step.
Format & Duration
2-day intensive workshop (on-site). Day 1: LP and MILP theory with classic examples, solver setup, and guided formulation exercises. Day 2: participants formulate and solve an optimization problem from their own domain, with coaching and peer review.
What Makes This Course Different
Operations research has been taught in universities for 70 years. It’s rarely taught to the people who actually make the decisions it’s designed to support. This course bridges that gap.
The academic foundation (Dantzig’s simplex method, branch-and-bound, duality theory) is taught at the intuition level — you understand why sensitivity analysis works, not just how to read the report. The consulting experience provides formulation patterns from real industries: pharmaceutical production scheduling, financial portfolio allocation, logistics network design.
Participants use our Aipokit platform to see optimization applied to resource-constrained production planning — a live demonstration of what these methods look like embedded in a real system.
Next Steps
Graduates are prepared for Supply Chain & Production Planning (c-scp-1), which applies optimization methods specifically to demand forecasting, MRP, inventory management, and network design.