This paper presents a simulation study for a resource-constrained project scheduling problem with multiple alternatives. We decide on a set of baseline schedules at the project planning phase, resulting in options to switch between execution modes of activities during project execution. We assess the performance of the set of baseline schedules under general mode implementation disruptions. A simple, yet effective algorithm is presented to construct the set of baseline schedules. Moreover, a general disruption system is proposed to model different disruption types, disruption dependencies and disruption sizes.

Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.

Although e-commerce has become a common basis for business transactions in most markets, other elements of e-business are not as widely used yet. This paper extends and operationalizes a previously developed e-business process model by Basu and Muylle (2007). Specifically, it presents evaluation measures for each process and an instrument based on those measures, and describes an effective approach to assess firms’ e-business capabilities. The assessment approach is illustrated using data from a diverse collection of firms in the Flanders region of Belgium. This is followed by an illustration of how such assessments can assist firms in identifying e-business initiatives in line with strategic and market priorities, using a subset of the firms as examples.

In this paper we introduce a novel human-centered data mining software system which was designed to gain intelligence from unstructured textual data. The architecture takes its roots in several case studies which were a collaboration between the Amsterdam-Amstelland Police, GasthuisZusters Antwerpen (GZA) hospitals and KU Leuven. It is currently being implemented by bachelor and master students of Moscow Higher School of Economics. At the core of the system are concept lattices which can be used to interactively explore the data. They are combined with several other complementary statistical data analysis techniques such as Emergent Self Organizing Maps and Hidden Markov Models.