摘要：Nowadays, industries cope with a wide range of situations and/or perturbations that endanger the
manufacturing productivity. Traditionally, manufacturing control systems are responsible for managing
the manufacturing scheduling and execution, as these have the capability of maintaining the
production operations regardless of a given perturbation. Still, the challenge of these systems is to
achieve an optimal performance after the perturbations occur. For this reason, manufacturing control
systems must incorporate a mechanism with intelligent capabilities to look for optimal performance
and operation reactivity regardless of any scenario. This paper proposes a generic control
strategy for a manufacturing control system for piloting the execution of a dynamic scheduling
problem, considering a new job arrival as the manufacturing perturbation. The study explores a
predictive-reactive approach that couples a genetic algorithm for the predictive scheduling and an
adaptive genetic algorithm for reactivity control aiming to minimize the weighted tardiness in a
dynamic manufacturing scenario. The results obtained from this proposal verify that the effectiveness
was improved by using adaptive metaheuristic in a dynamic scheduling problem, considering
absorbing the degradation caused by the perturbation.
关键词：Adaptive Genetic algorithm
Dynamic Scheduling
Manufacturing control
Predictive;Reactive
Optimality
Reactivity
