Content of the Course

• Heuristic Methods for Combinatorial Optimization
• Construction Heuristics and Perturbative Searches for
• Routing problems (Traveling Salesman Problem)
• Permutation problems (Single Machine Total Weighted Tardiness Problem)
• Assignment problems (Graph Coloring Problem)
• Metaheuristics
• Variable Neighborhood Descent, Variable Depth Search, Dynasearch
• Randomised Iterative Improvement, Probabilistic Iterative Improvement, Simulated Annealing, Tabu Search, Dynamic Local Search
• Iterated Local Search, Greedy Randomised Adaptive Search Procedures, Adaptive Iterated Construction Search
• Evolutionary Algorithm, Memetic Algorithm, Scatter Search, Path Relinking
• Ant Colony Optimisation, Cross Entropy Method, Estimation of Distribution Algorithm
• Applications
• Routing
• Traveling Salesman
• Vehicle Routing
• Timetabling
• Graph Coloring
• Educational Timetabling
• Employee Timetabling
• Scheduling
• Single Machine Scheduling
• Flow Shop Scheduling
• Group Shop Scheduling
• Empirical Methods
• Descriptive Statistics (Exploratory Data Analysis)
• Kinds of Data, Performance Measures
• Histogram, Density Distribution, Cumulative Distribution
• Scatter plots, Boxplots, Interaction plots, Correlation plots
• Linear Regression, Non-Linear Regression, Smoothing
• Inference Statistics
• Experimental Design
• Hypotheis Testing and Estimation
• Comparing Two Samples, Parametric and Non-Parametric Tests
• Comparing Multi Samples: Analysis of Variance and Multiple-Comparisons
• Sequential Testing
• R, the free software environment for statistical computing and graphics
• Further Notions
• Multi-objective optimization, stochastic optimization