Low-Cost Emergent Dynamic Scheduling for Flexible Job Shops

Abstract

Flexible production is a typical representative of high-end manufacturing and is also a manifestation of a country’s national production capability. Compared with other production modes, the key distinction of flexible production is that all four dimensions of production (i.e., machines, operations, products, and orders) can be scheduled dynamically. Although many studies have investigated the flexible job shop scheduling problem, most have limited dynamic support and cannot deal with multidimensional dynamic production. This study, therefore, proposed a fine-grained system state description model, which was used to analyze the maximum production completion time. In the presence of a dynamic event, the model was able to quickly assign priorities to products according to the cost loss of each product. The system can therefore dynamically respond to events in a timely manner while reducing production costs and losses. Finally, we used a large number of orders to evaluate the proposed algorithm, which demonstrated millisecond-level response capability and low-cost maintenance capability. Compared with existing algorithms, the proposed algorithm reduced cost loss by up to 11%.