Daniel-Carpenter/Metaheuristics

Modeling with Linear/Integer/Binary Programming & Metaheuristics

Advanced Analytics & Metaheuristics

Linear, Integer, and Binary Programming (w/Mixed variants)
Also covers key neighborhood and population based algorithm implementations

Interesting Projects Below

Linear Programming

• Production Modeling - (View problem context)
• Citywide Planning - (View problem context)
• Scheduling, inventory management, multiperiod planning, and cutting stock problems

Advanced Network Flow Modeling

• Minimize Transportation costs - (View problem context)
• Generalized Network Flows to Model Decay

Mixed Linear and Integer (and/or) Binary Programming

• Choosing among various projects with Advanced Mixed-Binary-Linear - (View problem context)
• Supplier choice with Mixed-Binary-Programming / Piece-wise programming - (View problem context)

Heuristic & Metaheuristic Approaches

• Hill Climbing Variants using Knapsack Problem (Best/first accept and random restarts/walk) - (View problem context)
• Neighborhood & Population Hueristics w/Knapsack: (View problem context)
  • Simulated Annealing
  • Genetic Algorithm
• Particle Swarm Optimization to solve Schwefel Benchmark Function - (View problem context)

See Concepts and Notes here

Includes topics from above, plus multiple objective opt, branch and bound methods, and other neighborhood metaheuristics

Textbooks and Resources

• Practical Optimization: a Gentle Introduction
• How to Solve it: Modern Hueristics
• Handbook of Metaheuristics
• AMPL Handbook
• Applied Mathematical Programming
• Evolutionary Algorithms for Solving Multi-objective Problems