NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies. (15th June 2021)

• Record Type: Journal Article
• Authors: Lang, Sebastian; Reggelin, Tobias; Schmidt, Johann; Müller, Marcel; Nahhas, Abdulrahman
• Is Part Of: Expert systems with applications. Volume 172(2021)
• Publication Date: 2021-06-15
• Subjects: Artificial intelligence -- Machine learning -- Neural network -- Neuro evolution -- Scheduling -- Flow shop; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114666
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Examination of NeuroEvolution of Augmenting Topologies for production scheduling. Overview of strategies of utilizing neural networks for flow shop scheduling. Comparison of NeuroEvolution of Augmenting Topologies with state-of-the-art methods. Abstract: 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.