A genetic algorithm approach to job shop scheduling problems with a batch allocation issue

With respect to the CUDA framework, our parallel GA consists of two levels, a fine-grained GA at the lower level and an island GA at the upper level, as presented in Fig. At the

x Each anchor node is composed of two sensors ( a bar containing two microphones), installed in such a manner that the bars of the different adjacent anchors are

commandes de pharmacie. Ceci représente bien sûr un soulagement pour les IDE qui jugent bien souvent cette activité fastidieuse à réaliser. Malheureusement, le gain de temps lié

The pro- posed methodology relies on a simple indirect binary representation of the chro- mosome and simple genetic operators (one-point crossover and bit-flip muta- tion),

The simulation model corresponds to a possible organisation of a hybrid flow shop with five produc- tion stages and is briefly described in below. As input, the simulation receives

To summarize, the problem dealt with here is the minimization of the weighted number of tardy jobs in a re-entrant flow shop with precedence constraints, release

Table 4 reports the size of the critical tree for five branching rules (two static rules Forward, Alternate, and three dynamic strategies MaxSum, MinMin and MinBranch) and two

We have compared the pro- posed approach (denoted as f-NSGA2-LS) against the single-objective genetic algorithm (GA) that works only on the modal value of TFNs (denoted as crisp-GA),