Optimization & Decision Modeling

Prescriptive analytics using simulation, linear programming and decision analysis.

Operations Research (1): Models and Applications (National Taiwan University)

Learn linear, integer, and nonlinear optimization with Excel, then explore a staffing case in this introductory operations research course.

Excel

Operations Research (2): Optimization Algorithms (National Taiwan University)

Optimization & Decision Modeling

Study simplex, branch-and-bound, gradient descent, and Newton’s method, with Python, Gurobi, and a facility-location business case study.

Python

Advanced Business Analytics: Excel Optimization & Simulation (Johns Hopkins)

Optimization & Decision Modeling

Build Excel models that optimize decisions with Solver, test sensitivity, and simulate uncertainty, in a one-week course from Johns Hopkins.

Excel

Prescriptive analytics answers what you should do, not merely what will happen. Courses here teach you to express a business problem as a mathematical model with an objective and constraints, then solve it: linear and integer programming for allocation, scheduling and routing, Monte Carlo simulation for decisions under uncertainty, queueing models for capacity, and decision trees for sequential choices. Tools range from Excel Solver for accessibility to Python libraries such as PuLP, Pyomo and SimPy. A specialised but high-leverage area, most valuable in pricing, logistics, workforce scheduling and capital allocation.