Stochastic cases of the dynamic job shop problem based on the genetic algorithm to minimize.

Up to now, the majority of researches on scheduling assume the difficulty of scheduling the job shop manufacturing system, especially the Dynamic Job Shop Scheduling Problem (DJSSP) which is the main purpose of our contribution. Looking to minimize the makespan value (Cmax), setup times and precedence constraints are considered. During the did work, we have proposed an inspired Genetic Algorithm(GA) approach based on the genetic operators to solve the DJSSP. The main task of the proposed method is to show how efficiently the system will schedule dynamically the new jobs. Eventually, the DJSSP based GA approach is proven to be successfully solved through experimental results. Hence, this later make it possible to generate minimal makespan values compared to the well known priority dispatching rules.