Using statistical-model-checking-based simulation for evaluating the robustness of a production schedule

Sara Himmiche 1 Alexis Aubry 1 Pascale Marangé 1 Jean-François Pétin 1 Marie Duflot 2
2 VERIDIS - Modeling and Verification of Distributed Algorithms and Systems
LORIA - FM - Department of Formal Methods , Inria Nancy - Grand Est, MPII - Max-Planck-Institut für Informatik
Abstract : Industry 4.0 implies new scheduling problems linked to the optimal using of flexible resources and to mass customisation of products. In this context, first research results show that Discrete Event Systems models and tools are a relevant alternative to the classical approaches for modelling scheduling problems and for solving them. Moreover, the challenges of the industry 4.0 mean taking into account the uncertainties linked to the mass customisation (volume and mix of the demand) but also to the states of the resources (failures, operation durations,. . .). The goal of this paper is to show how it is possible to use the simulation based on statistical model checking for taking into account these uncertainties and for evaluating the robustness of a given schedule.
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Submitted on : Wednesday, November 29, 2017 - 11:33:38 PM
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Sara Himmiche, Alexis Aubry, Pascale Marangé, Jean-François Pétin, Marie Duflot. Using statistical-model-checking-based simulation for evaluating the robustness of a production schedule. 7th Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA'17, Oct 2017, Nantes, France. ⟨hal-01652140⟩

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