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

TLDR

Smart factories in Industry 4.0 rely on collaborative cyber‑physical systems, yet their supply chains are dynamic, making short‑term scheduling difficult due to changing machine structures, varying processing speeds, 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 authors develop a dynamic, non‑stationary scheduling model that temporally decomposes the problem, and implement it with a modified continuous maximum principle combined with mathematical optimisation, analysing its decomposition, complexity, optimality conditions and structural properties. The study discusses the advantages and limitations of the proposed scheduling approach.

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.