NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies

The article investigates the application of NeuroEvolution of Augmenting Topologies (NEAT) to generate and parameterize artificial neural networks (ANN) on determining allocation and sequencing decisions in a two-stage hybrid flow shop scheduling environment with family setup times. NEAT is a machine-learning and neural architecture search algorithm, which generates both, the structure and the hyper-parameters of an ANN. Our experiments show that NEAT can compete with state-of-the-art approaches in terms of solution quality and outperforms them regarding computational efficiency. The main contributions of this article are: (i) A comparison of five different strategies, evaluated with 14 different experiments, on how ANNs can be applied for solving allocation and sequencing problems in a hybrid flow shop environment, (ii) a comparison of the best identified NEAT strategy with traditional heuristic and metaheuristic approaches concerning solution quality and computational efficiency.

Automated summary

The study investigates the application of NEAT algorithm in a two-stage hybrid flow shop scheduling environment and finds that NEAT performs well in terms of solution quality and computational efficiency.