By Tamas Kis
During this paper we learn a source restricted undertaking scheduling challenge within which the source utilization of every job could fluctuate through the years proportionally to its various depth. We formalize the matter through a combined integer-linear software, end up that possible answer life is NP-complete within the powerful feel and suggest a branch-and-cut set of rules for locating optimum recommendations. To this finish, we offer a whole description of the polytope of possible depth assignments to 2 variable-intensity actions attached via a priority constraint besides a quick separation set of rules. A computational review confirms the effectiveness of our technique on a number of benchmark situations.
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Additional resources for A branch-and-cut algorithm for scheduling of projects with variable-intensity activities
In the literature, some cGAs hybridized with local search methods have been published. Some examples are the cGAs for training recurrence artiﬁcial neural networks for solving the long-term dependency problem  or the XOR function , and the most recently hybrid cGAs proposed for the SAT problem by Folino et al. [91, 92], and by Luo and Liu , where the mutation operator is replaced by a local search step. This last algorithm has the particularity of being developed for running in the Graphic Processing Unit (GPU) of the computer, instead of using the Central Processing Unit (CPU).
11. pop); while ! 1 Steady State GA A pseudo-code of the steady state GA (ssGA) is given in Alg. 1. As it can be seen, it is a (μ+1)-GA. In each generation, two parents are selected from the whole population with a given selection criterion (line 5). These two individuals are recombined (line 6) and then one of the obtained oﬀsprings is mutated (line 7). The mutated individual is evaluated and then it is inserted back into the population, typically replacing the worst individual in the population (if the new one is better).
7. Comparison of the decentralized GAs.