In this paper we present a modification to the Dynamic Assignment Vehicle Routing Problem. This problem arises in parcel to vehicle assignment where the destination of the parcels is not known up to the assignment of the parcel to a delivering route. The assignment has to be done immediately without the possibility of re-assignment afterwards. We extend the original problem with a generalisation of the definition of capacity, with an unknown workload, unknown number of parcels per day, and a generalisation of the objective function. This new problem is defined and various methods are proposed to come to an efficient solution method.
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This paper introduces the Analysis Framework of Face Interaction (AFFI) which is developed based on a new face dimension termed Face Confirmation − Face Confrontation at two levels: Individual level within the group and Collective level between groups. This proposed framework of face analysis reveals a dearth of research on face confrontation as essential communication strategies. It also points out how the mainstream research on facework has been limited on the collective level of analysis. The authors argue that using AFFI will help researchers reduce cultural over-generalisation; enable them to involve more specific cultural, contextual and situational characteristics of each face case to analyse face negotiation from a more holistic perspective.
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Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
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