Decision making with fair ranking

Abstract and Figures Ranking is a responsible process because it involves working with sensitive attributes that can discriminate alternatives. Due to the availability of a large amount of data for automated processing, ranking is increasingly in use in decision making. Therefore, concepts of algorithmic fairness in the field of classification in machine learning find their place in fair ranking methods. This paper provides an overview of fair ranking terms, fair ranking challenges, and fair ranking algorithms from the state-of-the-art literature

Equivalent geometric errors of rotary axes and novel algorithm for geometric errors compensation in a nonorthogonal five-axis machine tool

The indirect identification of the geometric errors (GEs) in the rotary axis of a machine tool yields six equivalent GEs (EGEs) that are position-dependent; through an analytical proof, this study demonstrates that these errors also represent four position-independent GEs of the axis. Moreover, a novel algorithm using ball bar measurements to calculate the EGEs of a nutating rotary B-axis and a rotary C-axis is presented herein. This paper also presents a new analytical solution for the actual inverse kinematics of a nonorthogonal five-axis machine tool; this solution is used for GE compensation. The presented algorithms are implemented in a self-developed software that alters the nominal numerical control code in order to eliminate GEs. The compensation accuracy and efficiency are tested using a simulation system. The results demonstrate that the proposed compensation algorithm eliminates all identified GEs. Lastly, a cutting test executed on a machine confirms that the proposed algorithms considerably improve machining accuracy

Reducing Risks in Energy Innovation Projects: Complexity Theory Perspective

The aim of this paper is to contribute to existing work on project risk management, and energy innovation projects, using the lens of complexity theory. By regarding energy innovation projects as complex adaptive systems, and linking complexity theory elements to the possibilities for reducing risks of energy innovation projects, the authors conducted empirical research on a representative sample of 100 subjects. The authors used a questionnaire that was formed on the basis of a previously designed research model, which unifies several different management fields, and a large number of phenomena previously studied independently. Therefore, it has a holistic approach to the topic. The results of this research suggest that considering the elements of complexity theory had a positive impact on reducing risks in energy innovation projects, in all analyzed aspects including specific, operational, and especially regarding social and behavioral aspects. This paper strives to support and encourage better results in energy innovation projects by reducing their risks, and hopes to bring additional value by introducing a new risk philosophy, based on complexity theory. Lessons learnt regarding each issue of this research are points of concern for project managers