Modeling Strategy by Adaptive Genetic Algorithm for Production Reactive Scheduling with Simultaneous Use of Machines and AGVs

The problem of production scheduling of manufacturing systems is a typical NP-hard optimization problem and several researchers have been using the genetic algorithms (GAs) as a search method, since these algorithms have the capacity of globally exploring the search space. However, it is reported that traditional GAs often suffers from the weaknesses of premature convergence as well as parameter and operator dependence. For this, in this paper it is proposed a modeling strategy by adaptive GA (AGA) for production reactive scheduling of manufacturing systems with shared resources and simultaneous use of machines and AGVs. The aim of this paper is to get a good production reactive schedule in order to achieve a good makespan values in a low response obtaining time. The results of this paper were validated in large scenarios and compared with the results of two other approaches. These results are presented and discussed in this paper.