A New Framework for Dynamic Deterministic Job-Shop Scheduling problems Using Genetic Algorithms

The problem of finding good solutions to scheduling problems is very important to real manufacturing systems, since the production rate and production costs are very dependent on the schedules used for controlling the work in the system. Most research in scheduling focuses on optimisation of static problems, where all problem data are known before scheduling starts. However many real world optimisation problems are dynamic, in which changes may occur continually. This paper presents a scheduling system, based on Genetic Algorithms for the resolution of the deterministic Job-Shop Scheduling Problem (JSSP), which considers the existence of different job release dates and job due dates, and different assembly levels. This approach is based on a decomposition of the Job-Shop Scheduling Problem into a series of deterministic Single Machine Scheduling Problem (SMSP). A Genetic Algorithm (GA) solves each SMSP, and the obtained solutions are integrated at the end. A coordination mechanism is proposed.