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

By Dmitry A. Ivanov, Alexandre Dolgui, Boris V. Sokolov, Frank Werner, Marina Ivanova. 2016

476 citations recorded by the source corpus.

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.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/361dd6ad67071dcb6eae9a4324a05be04229450e

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In Industrial and Manufacturing Engineering

Citations in the corpus

Smart manufacturing: Past research, present findings, and future directions (2016, unknown): “…et al. developed a dynamic model and algorithm for a short-term supply chain scheduling in a smart factory as applied researches of Smart Manufacturing, 14 while Choi et al. suggested a strategic plan and a systematic design for the efficient implementation and application of the virtual factory.”
Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain (2019, methodology): “The analytical optimality properties of the model presented as well as the efﬁcient computational algorithm have been previously presented and proven in the studies (Ivanov and Sokolov 2013; Ivanov, Dolgui, and Sokolov 2016; Ivanov et al. 2016, 2018; Sokolov, Dolgui, and Ivanov 2018).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “…2014b; Ivanov, Sokolov, and Pavlov 2014c; Ho et al. 2015; Gunasekaran, Subramanian, and Rahman 2015; Gupta, He, and Sethi 2015; Tukamuhabwa et al. 2015; Ivanov et al. 2016a, 2016b; Snyder et al. 2016; Chen, Xi, and Jing 2017; Ivanov 2017a; Ivanov et al. 2017; Jain et al. 2017; Yoon et al. 2018).”
Literature review on the ‘Smart Factory’ concept using bibliometric tools (2017, background): “In their proposed framework, Ivanov et al. (2016) developed a dynamic model and optimisation algorithm for short-term supply chain scheduling in smart factories.”
Blockchain technology and its relationships to sustainable supply chain management (2018, background): “For example, distributed supply chain coordination (Chan and Chan 2010; Ivanov et al. 2016), material and information flow coordination (Ivanov, Sokolov, and Raguinia 2014), virtual enterprises (Sarkis, Talluri, and Gunasekaran 2007; Crispim and de Sousa 2010), agile supply chain management (Sarkis ”