Scheduling projects is a difficult and time consuming process, and has far-reaching implications for any organization's operations. By generalizing various aspects of project scheduling, decision makers are enabled to capture reality and act accordingly. In the context of the MISTA 2013 conference, the first MISTA challenge, organized by the authors, introduced such a general problem model: the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The present paper reports on the competition and provides a discussion on its results. Furthermore, it provides an analysis of the submitted algorithms, and a study of their common elements. By making all benchmark datasets and results publicly available, further research on the MRCMPSP is stimulated.

We present one general high-level hyper-heuristic approach for addressing two timetabling problems in the health care domain: the patient admission scheduling problem and the nurse rostering problem. The complex combinatorial problem of patient admission scheduling has only recently been introduced to the research community. In addition to the instance that was introduced on this occasion, we present a new set of benchmark instances. Nurse rostering, on the other hand, is a well studied operations research problem in health care. Over the last years, a number of problem definitions and their corresponding benchmark instances have been introduced. Recently, a new nurse rostering problem description and datasets were introduced during the first Nurse Rostering Competition. In the present paper, we focus on this nurse rostering problem description.The main contribution of the paper constitutes the introduction of a general hyperheuristic approach, which is suitable for addressing two rather different timetabling problems in health care. It is applicable without much effort, provided a set of lowlevel heuristics is available for each problem. We consider the instances of both health B. Bilgin · P. Demeester ( ) · M. Misir · W.B. Bilgin et al.care problems for testing the general applicability of the hyper-heuristic approach. Also, improvements to the previous best results for the patient admission scheduling problem are presented. Solutions to the new nurse rostering instances are presented and compared with results obtained by an integer linear programming approach.