Toward Dynamic Scheduling through Evolutionary Computing

Real world scheduling requirements are related with complex systems operating in dynamic environments. This means that they are frequently subject to several kinds of random occurrences and perturbations, such as new job arrivals, machine breakdowns, employee’s sickness and jobs cancellation causing prepared schedules becoming easily outdated and unsuitable. Scheduling under this environment is known as dynamic. These problems pose additional challenges for optimization techniques. This paper outlines the limitations of static approaches to scheduling in the presence of dynamic environments and gives a review of currently developing research on real world scheduling problems, which are often complex, constrained and dynamic. We decided to explore the use of Evolutionary computing techniques for solving real-world optimization problems. Therefore, in this paper, we present a Genetic Algorithm based scheduling method, which is of practical utility, embedded in a simple framework to solve difficult problems in manufacturing environments. Key-Words: Dynamic Scheduling, Job-Shop Scheduling, Evolutionary Algorithms, Genetic Algorithms, Resource-Oriented Scheduling, Manufacturing.