Metaheuristics for Scheduling

Analyze different neighborhood structures (e.g., swap, insertion) for local search.

Evaluate the trade-off between exploration and exploitation in metaheuristic design.

Apply the principles of Genetic Algorithms (GA), including chromosome representation and crossover operators.

Apply Simulated Annealing (SA) for neighborhood search, including acceptance criteria and cooling schedules.

Implement the core components of a Tabu Search (TS) algorithm, including the tabu list and aspiration criteria.