Solving Scheduling Problems by

We deene a general methodology to deal with a large family of scheduling problems. We consider the case when some of the constraints are expressed through the minimization of a loss function. We study with more details a benchmark example consisting in some jigsaw puzzle problem with additional constraints. This is the occasion to discuss some algorithmic issues typical of scheduling problems, such as the apparition of small unused gaps or the representation of proportionality constraints. This is also an opportunity to carry on an experimental comparison between the Metropolis algorithm, simulated annealing and the iterated energy transformation method, and to see whether asymptotical theoretical results are a good guide towards practically eecient algorithms.