Prescriptive Analytics in Procurement: Reducing Process Costs

In obtaining low-cost goods, the indirect expenses associated with sourcing suppliers can be substantial compared to the potential advantages of lower direct purchase costs. We addressed this problem as an exploration vs. exploitation trade-off. The proposed methodology uses a Bayesian technique to learn a stochastically optimal sourcing strategy directly from quotation data. We illustrate our approach using real quotation data for the procurement of electronic resistors (n=201,187). Rather than making optimal predictions, we concentrate on making optimal decisions. In doing so, we offered a significant improvement in purchase and procurement process costs. Our model is also more robust to prediction errors

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Global Multi-Objective Optimisation utilising Surrogate Models

While global multi-objective optimization problems continue to emerge in aerospace engineering, conventional optimization methods, in particular, evolutionary algorithms such as the Non-dominated Sorting Genetic Algorithm, have shown their capability to solve such problems. However, one distinctive disadvantage of these conventional methods is that they generally require a large number of function evaluations, which makes them incompatible with computationally intensive numerical simulations that are often employed in aerospace design problems. This thesis substantiates the idea that this limitation can be overcome by using surrogate based optimization, in particular multi-objective Bayesian global optimization that utilizes Kriging as a surrogate model and Expected Hypervolume Improvement as an infill criteria. With this approach, it is possible to obtain the Pareto front with a relatively small computational budget. This is demonstrated through test cases that are conducted by solving analytical optimization problems. The results show that Bayesian optimization is able to reduce the function evaluations by 51 times for the bi-objective problem, and by 91 times for the three-objectives problem compared to genetic algorithms. Furthermore, its applicability is tested in two aerospace design problems, where function evaluations were performed through Computational Fluid Dynamics (CFD) and Computational Aeroacoustic (CAA) simulations. The proposed optimization method returns Pareto fronts which contain various design trade-offs that result in improved performance in terms of the desired objectives, with a reasonable number of function evaluations. Firstly, in the aerodynamic shape optimization, it is able to obtain the Pareto front, which contains airfoil designs with a combination of reduced drag and reduced pitching moment. Secondly, the aerodynamic-aeroacoustic shape optimization is performed where the Pareto front is obtained for airfoil designs with three objectives: reduced drag, reduced pitching moment and reduced aeroacoustic noise. This thesis demonstrates the efficiency of the Bayesian global optimization framework by showing how the Pareto front can be obtained at a relatively smaller number of function evaluations compared to some of the conventional multi-objective optimization methods. Moreover, the results obtained from the applied problems verify its capability for practical applications in aerospace design. Hence, the outcomes of this thesis highlight the potential of multi-objective Bayesian global optimization for multidisciplinary design optimization problems in the field of aerospace engineering

Nondestructive Testing (NDT)

The aim of this book is to collect the newest contributions by eminent authors in the field of NDT-SHM, both at the material and structure scale. It therefore provides novel insight at experimental and numerical levels on the application of NDT to a wide variety of materials (concrete, steel, masonry, composites, etc.) in the field of Civil Engineering and Architecture

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Energy: A continuing bibliography with indexes, issue 19

A bibliographical list of 1339 reports, articles, and other documents introduced into the NASA scientific and technical information system from July 1, 1978 through September 30, 1978 are presented

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton