An application of a real-time scheduling system for turbulent manufacturing environments
Classical scheduling approaches are not effective in real plants since they assume highly unrealistic assumptions that are seldom found in practice. These assumptions mainly deal with the consideration of the manufacturing environment as static. But in most real manufacturing installations some unexpected events take place. Ignoring these events may lead to schedules of unacceptable performance. Moreover, a quick reaction to changes is necessary, taking into account the significant amount of indirect costs involved in the derived interruptions. Reactivity and flexibility have to become key requirements at high control levels since they can contribute to shorten throughput times, improve equipment utilisation rates and service to customers. There is a need to extend classical scheduling models, so that they are able to better represent the factory. Genetic algorithms stand for one of the most promising alternative techniques to the classical ones. They show non-linearity, domain independence, robustness and flexibility. In this article, an evolutionary genetic algorithm has been used for sequencing job-shop problems in a real-time environment. Untoward events such as lack of materials or tools, change in production orders or unavailability of machines have been represented in the model and a solution using genetic algorithms has been designed and implemented. In the context of a European project belonging to the Competitive and Sustainable Growth programme, a prototype of a dynamic scheduler using genetic algorithms is described and some conclusions are drawn regarding the feasibility and quality of the solution.
real-time production scheduling genetic and evolutionary algorithms