Optimizing Dispatching Rules for Stochastic Job Shop Scheduling

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Ferreira, C (Author) Other; figueira, g (Author) FEUP; Pedro Amorim (Author) FEUP

Conference proceedings International

Title: Proceedings of the 18th International Conference on Hybrid Intelligent Systems (HIS 2018)

Pages: 321-330

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00Q-GFR

DOI: 10.1007/978-3-030-14347-3_31

Abstract (EN): Manufacturing environments commonly present uncertainties and unexpected schedule disruptions. The literature has shown that in these environments simple and fast dynamic dispatching rules are efficient sequencing methods. However, most of the works in the automated designing of these rules have considered deterministic processing times. This work aims to design dispatching rules for problem settings similar to the ones found in real environments such as uncertain processing times and sequence-dependent setup times. We use Genetic Programming to generate efficient rules for stochastic job shops with setup times. We show that the generated rules outperform benchmark dispatching rules, specially in settings with high setup time levels. © 2020, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific