A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines

Abstract

Additive manufacturing (AM) has been recognised as a promising technology under the context of Industry 4.0, which is reshaping manufacturing paradigms. A prominent type of AM machine is the selective laser melting (SLM) machine, in which several parts may form a job and be produced concurrently. This paper aims to investigate a scheduling problem in an AM system with non-identical parallel SLM machines. Since, in this system, there might be differences in the material types of parts, the required setup time between two consecutive jobs on the relevant machine is dependent on their material types. Accordingly, a bi-objective mathematical model is extended for the problem, considering the makespan and the total tardiness penalty as two objective functions. Due to the high complexity of the problem, an efficient hybrid meta-heuristic algorithm is developed by combining the non-dominated sorting genetic algorithm (NSGA-II) with a novel learning-based local search founded on the k-means clustering algorithm and a regression neural network. The local search enhances the exploitation ability of the NSGA-II while intelligently being taught during the solving procedure. Finally, the superiority of the proposed hybrid algorithm is demonstrated through a computational experiment.

KEYWORDS:

• Scheduling
• additive manufacturing
• selective laser melting
• mathematical modelling
• learning-based local search

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The research has been supported by the Ministry of Education, Youth and Sports within the dedicated programme ERC CZ under the project POSTMAN with reference LL1902.

Notes on contributors

Mohammad Rohaninejad

Mohammad Rohaninejad is a researcher at the Department of Industrial Informatics, Institute of Informatics Robotics and Cybernetics at Czech Technical University in Prague. He obtained his PhD, M.Sc. and B.Sc. degrees in Industrial Engineering from Shahed University (2018), Bu-Ali Sina University (2012), and Iran University of Science and Technology (2005), respectively. His academic and professional experience has focused on operations research and especially on the development and application of quantitative techniques to the simulation and design of supply chain, logistics, and scheduling. He is currently researching on the application of machine learning in combinatorial optimisation problems. He is the author of several papers published and presented in international journals and conferences.

Reza Tavakkoli-Moghaddam

Reza Tavakkoli-Moghaddam is a Professor of Industrial Engineering at the College of Engineering, the University of Tehran in Iran. He obtained his PhD, M.Sc. and B.Sc. degrees in Industrial Engineering from the Swinburne University of Technology in Melbourne (1998), the University of Melbourne in Melbourne (1994), and Iran University of Science and Technology in Tehran (1989), respectively. He serves as the Editor-in-Chief of the Journal of Industrial Engineering published by the University of Tehran and the Editorial Board member of nine reputable academic journals. He is the recipient of the 2009 and 2011 Distinguished Researcher Awards and the 2010 and 2014 Distinguished Applied Research Awards at the University of Tehran, Iran. He has been selected as the National Iranian Distinguished Researcher in 2008 and 2010 by the MSRT (Ministry of Science, Research, and Technology) in Iran. He has obtained the outstanding rank as the top 1% scientist and researcher in the world elite group since 2014. Also, he received the Order of Academic Palms Award as a distinguished educator and scholar for the insignia of Chevalier dans l’Ordre des Palmes Academiques by the Ministry of National Education of France in 2019. He has published 5 books, 25 book chapters, and more than 1000 journal and conference papers.

Behdin Vahedi-Nouri

Behdin Vahedi-Nouri is a PhD student in Industrial Engineering at the College of Engineering, the University of Tehran in Iran. He obtained his M.Sc. and B.Sc. degrees in Industrial Engineering from Bu-Ali Sina University and Iran University of Science and Technology, respectively. His research interests include scheduling in manufacturing and healthcare systems, applied operations research, and logistics optimisation. He has published several papers in reputable journals and international conferences.

Zdeněk Hanzálek

Zdeněk Hanzálek received his PhD degree in Industrial Informatics from the Universite Paul Sabatier Toulouse, France, and the PhD degree in Control Engineering from the Czech Technical University (CTU) in Prague. He was with LAAS Toulouse, and with INPG Grenoble. Besides this, Zdenek founded and led the SW development team of the Porsche Engineering Services in Prague and further he founded Merica company dealing with production scheduling. Currently, he is a professor at CTU Prague, leading a group of 20 talented researchers and PhD students. His research interests include production scheduling, combinatorial optimisation, and self-driving cars.

Shadi Shirazian

Shadi Shirazian is an M.Sc. student in Industrial Engineering at the College of Engineering, the University of Tehran in Iran. She obtained her B.Sc. degree in Industrial Engineering from K. N. Toosi University of Technology. Her research interest comprises scheduling problems, additive manufacturing, and logistics.