Evolutional reactive scheduling for agile manufacturing systems

Abstract

A predetermined production schedule is often disturbed in agile manufacturing systems, due to unscheduled disruptions, such as delays of manufacturing operations and addition of new jobs. The objective of the research is to propose a new reactive scheduling method based on the Genetic Algorithm (GA), which generates improved production schedules reactively against the disturbances. A basic reactive scheduling method was proposed in the previous research. The proposed method continuously creates new feasible production schedules, until a new production schedule satisfies the given constraint or all the manufacturing operations have started. This paper deals with a new evolutional method to improve the performance of the GA-based reactive scheduling process for adding new jobs. Several computational experiments were carried out for the delays of manufacturing operations and the addition of new jobs by using the developed prototype system for reactive scheduling, in order to verify the effectiveness of the proposed method.

Keywords:

• Reactive scheduling
• Genetic algorithm
• Additional job
• Lower bound value
• Variety of individual