International audienceSmart 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
Abstract:In Sweden, where forests cover more than 60% of the land area, silviculture and the use of forest products by industry and society play crucial roles in the national carbon balance. A scientific challenge is to understand how different forest management and wood use strategies can best contribute to climate change mitigation benefits. This study uses a set of models to analyze the effects of different forest management and wood use strategies in Sweden on carbon dioxide emissions and removals through 2105. If the present Swedish forest use strategy is continued, the long-term climate change mitigation benefit will OPEN ACCESS
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