On the use of empirical or artifical project data

This paper gives a brief overview of the artificial and empirical project data generated and collected by the researchers from the Operations Research and Scheduling (OR&S) group from Ghent University in Belgium. Artificial data are generated by project network generators under a strict design to control both the network structure and the resource constraints, while the empirical project data are collected over a time horizon of multiple years, using a standardized collection and classification method. All data are publicly available on the OR&S website www.projectmanagement.ugent. be/research/data) and can be used anywhere for academic purposes. More detailed information on the network and resource parameters used to generate the artificial data and the classification process for the collection of empirical data is available in a paper published in the Journal of Modern Project Management (Vanhoucke et al., 2016)

Breakthroughs in Shared Measurement and Social Impact

A surprising new breakthrough is emerging in the social sector: A handful of innovative organizations have developed web-based systems for reporting the performance, measuring the outcomes, and coordinating the efforts of hundreds or even thousands of social enterprises within a field. These nascent efforts carry implications well beyond performance measurement, foreshadowing the possibility of profound changes in the vision and effectiveness of the entire nonprofit sector. This paper, based on six months of interviews and research by FSG Social Impact Advisors, examines twenty efforts to develop shared approaches to performance, outcome, or impact measurement across multiple organizations. The accompanying appendices include a short description of each system and four more in-depth case studies

A First Step Towards Automatically Building Network Representations

To fully harness Grids, users or middlewares must have some knowledge on the topology of the platform interconnection network. As such knowledge is usually not available, one must uses tools which automatically build a topological network model through some measurements. In this article, we define a methodology to assess the quality of these network model building tools, and we apply this methodology to representatives of the main classes of model builders and to two new algorithms. We show that none of the main existing techniques build models that enable to accurately predict the running time of simple application kernels for actual platforms. However some of the new algorithms we propose give excellent results in a wide range of situations