Optimization, Operations Research, Python, Programming, Data Science.
t = np.arange(t_span[0], t_span[1]) return { (m, j): np.random.choice(t) for m in machines for j in jobs } def _random_sequences(self, machines, jobs): np.random.seed(self.seed) return { j: JobSequence(np.random.permutation(machines)) for j in jobs } Now we can instantiate random problems at ease