@article{MTMT:31733569,
	title = {Consideration of skills in assembly lines and seru production systems},
	url = {https://m2.mtmt.hu/api/publication/31733569},
	author = {Abdullah, Md and Süer, Gürsel A.},
	doi = {10.1504/AJMSA.2019.110377},
	journal-iso = {AJMSA},
	journal = {ASIAN JOURNAL OF MANAGEMENT SCIENCE AND APPLICATIONS},
	volume = {4},
	unique-id = {31733569},
	issn = {2049-8683},
	year = {2019},
	eissn = {2049-8691},
	pages = {99}
}

@article{MTMT:30315073,
	title = {Selection of Assembly Systems; Assembly Lines vs. Seru Systems},
	url = {https://m2.mtmt.hu/api/publication/30315073},
	author = {Aboelfotoh, Aaya and Md Abdullah, Gürsel A Süer},
	doi = {10.1016/j.procs.2018.10.304},
	journal-iso = {PROC COMPUTER SCI},
	journal = {PROCEDIA COMPUTER SCIENCE},
	volume = {140},
	unique-id = {30315073},
	issn = {1877-0509},
	abstract = {In the world of manufacturing, a manufacturing strategy must be selected depending on which strategy would produce the higher output rate, in order to carry out a set of product operations. This study focuses on the steady-demand domain, where two strategies are feasible; Classical Assembly Line and Seru, a recent Japanese strategy that mostly involves single-worker assembly lines. Mathematical modelling is used to determine the worker-to-station and task-to-station assignments of product structures with different parameters to calculate the assembly line output rate. On the other hand, simpler calculations are sufficient to determine the Seru strategy output rate. The aim is to study the relationship between worker skill level variation and strategy selection by building a neural network model using NeuroSolutions. Skill levels were considered to be normally distributed from 1 to 7. Inputs such as number of tasks, product precedence network flexibility, and task processing time variation will also be incorporated to support the learning process of the neural network model. The neural network model will then be applied to predict the more favourable strategy that would produce the higher output rate, between the Classical Assembly and Seru approach. As a result, this will help serve as a guideline regarding when to use Classical Assembly vs. Seru strategy, using only the available information about the product and skill variation and without the need to solve any mathematical model. © 2018 The Authors. Published by Elsevier B.V.},
	year = {2018},
	pages = {351-358}
}

@inproceedings{MTMT:24188585,
	title = {Toward Human-Centric Factories: Requirements and Design Aspects of a Worker-Centric Job Allocator},
	url = {https://m2.mtmt.hu/api/publication/24188585},
	author = {Gokan, May and Omid, Maghazei and Marco, Taisch and Andrea, Bettoni and Marco, Cinus and Annarita, Matarazzo},
	booktitle = {Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World},
	doi = {10.1007/978-3-662-44733-8_52},
	publisher = {Springer Netherlands},
	unique-id = {24188585},
	year = {2014},
	pages = {417-424}
}