Rabbit Complement

We focus on the resource provisioning problem of a cloud consumer from an Infrastructure-as-a-Service type of cloud. The cloud provider offers two deployment options, which can be mixed and matched as appropriate. Cloud instances may be reserved for a fixed time period in advance at a smaller usage cost per hour but require a full commitment and payment for the entire contract duration. In contrast, on-demand instances reflect a pay-as-you-go policy at a premium. The trade-off between these two options is rooted in the inherent uncertainty in demand and price and makes it attractive to complement a base reserved capacity with on-demand capacity to hedge against the spikes in demand. This paper provides several novel multi-stage stochastic programming formulations to enable a cloud consumer to handle the cloud resource provisioning problem at a tactical level. We first formulate the cloud resource provisioning problem as a risk-neutral multi-stage stochastic program, which serves as the base model for further modeling variants. In our second set of models, we also incorporate a certain concept of system reliability. In particular, chance constraints integrated into the base formulation require a minimum service level met from reserved capacity, provide more visibility into the future available capacity, and smooth out expensive on-demand usage by hedging against possible demand fluctuations. An extensive computational study demonstrates the value of the proposed models by discussing computational performance, gleaning practical managerial insights from the analysis of the solutions of the proposed models, and quantifying the value of the stochastic solutions. (C) 2020 Elsevier B.V. All rights reserved.

Fused deposition modelling (FDM) is the most common additive manufacturing technology used for thermoplastic components. This layers-based manufacturing process results into direct links between printing parameters and the polymer mesostructure by means of porosity and structural anisotropy. These dependencies along with other features of thermoplastic polymers (i.e., nonlinearities, viscous and thermal responses) makes its constitutive modelling very challenging. This work distances from studies that model the 3D printing process. Instead, we aim at complementing such approaches with a continuum model to describe the macroscopic behaviour of FDM thermoplastics while preserving links with printing parameters. Prior to the modelling conceptualisation, experimental characterisation tests are conducted on ABS specimens to evaluate the influence of printing parameters on the macroscopic mechanical response. The physical fundamentals behind the deformation and failure mechanisms are identified and motivate the new constitutive model. This model is formulated for finite deformations within a thermodynamically consistent framework. The model accounts for: nonlinear response; anisotropic hyperelasticity related to a transversely isotropic distribution of porous; strain rate dependency; macroscopic stiffness dependent on 3D printing processing. Finally, the model is numerically implemented and calibrated for ABS with original experiments, demonstrating its suitability.