A Multi-Population BRKGA for Energy-Efficient Job Shop Scheduling with Speed Adjustable Machines

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Homayouni, SM (Author) Other; Dalila B.M.M. Fontes (Author) FEP; Fontes, Fernando A C C (Author) FEUP

Conference proceedings International

Title: Metaheuristics - 14th International Conference, MIC 2022, Syracuse, Italy, July 11-14, 2022, Proceedings

Pages: 513-518

Indexing

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Other information

Authenticus ID: P-00X-XV2

DOI: 10.1007/978-3-031-26504-4_41

Abstract (EN): Energy-efficient scheduling has become a new trend in industry and academia, mainly due to extreme weather conditions, stricter environmental regulations, and volatile energy prices. This work addresses the energy-efficient Job shop Scheduling Problem with speed adjustable machines. Thus, in addition to determining the sequence of the operations for each machine, one also needs to decide on the processing speed of each operation. We propose a multi-population biased random key genetic algorithm that finds effective solutions to the problem efficiently and outperforms the state-of-the-art solution approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific