A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 - Mines Saint-Étienne Access content directly
Journal Articles International Journal of Production Research Year : 2016

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Abstract

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.
Fichier principal
Vignette du fichier
Ivanov2016.pdf (620.79 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

emse-01109312 , version 1 (15-11-2021)

Licence

Identifiers

Cite

Dmitry Ivanov, Alexandre Dolgui, Boris Sokolov, Frank Werner, Ivanova Marina. A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 2016, 54 (2), pp.386-402. ⟨10.1080/00207543.2014.999958⟩. ⟨emse-01109312⟩
2211 View
754 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More