Event-Driven Dynamic Job Shop Scheduling Execution Based On Improved Genetic Algorithm And Ontology

This paper proposes an improved Genetic Algorithm (IGA)-combined with ontology module to solve dynamic job shop scheduling problem (DJSSP). The objective function of this scheduling problem is to minimize a weighted sum method of maximum complete time (makespan) and mean waiting time to periodically plan production. This scheduling method is applied to a flexible manufacturing system. A reschedule strategy is utilized to solve dynamic disturbances happened during manufacturing process. Experimental results show that schedule can be repaired efficiently and correctly without obviously affecting the scheduling performance.