Scheduling Algorithms: Challenges Towards Smart Manufacturing


  • Abebaw Degu Workneh Euro-Mediterranean University of Fez, Euromed Research Center, Fes, Morocco
  • Maha Gmira Euro-Mediterranean University of Fez, Euromed Research Center, Fes, Morocco



Scheduling Algorithm, Smart Manufacturing, Production Scheduling, Industry 4.0


Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms' characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario.




How to Cite

A. D. Workneh and M. Gmira, “Scheduling Algorithms: Challenges Towards Smart Manufacturing”, IJECES, vol. 13, no. 7, pp. 587-600, Sep. 2022.



Original Scientific Papers