Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem

Abstract

In this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain processing times. The NEAT-based approach is experimentally compared with the SPT and FIFO scheduling rules by designing problem instances. The results show that the NEAT-based scheduling method can obtain solutions with better convergence while responding quickly compared to the scheduling rules.

Keywords

• hybrid flow shop
• reinforcement learning
• Neuro-Evolution of Augmenting Topologies
• makespan
• dynamic scheduling