Planning and scheduling problems have a vital role in most manufacturing and production systems. The complexity of such problems which are usually hard combinational optimization ones prevent the implementation of exact solving methodologies which could give the optimal solution. It is the reason why engineers prefer to use metaheuristics which are able to produce good solutions in a reasonable computation time. The metaheuristic approaches can be separate in two classes: the local search techniques and the global ones. Among the local search techniques the Tabu search is the more known. The other methods usually involve a part of stochastic approach, like the Simulated Annealing, the Genetic or Evolutionary Algorithms, the Ant Colony Optimization or the Particle Swarm Optimisation. An important difficulty which appears in complex optimization problems is the existence of constraints which can be strict and inviolable or soft but with penalization which increase strongly with the degree of violation.
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