The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.

Abstract. Block-to-block and block-to-point kriging predictions based on block data are proposed. Blocks may be regular (mesh data) or of more general shapes. Under the assumptions of second-order stationarity and isotropicity, we show how to lessen the number of calculations of relevant block-to-block covariances. As illus-trations, a mesh data of population and a simulated block data on convex polygons are analyzed.

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The increase in the population of students in the higher institute of learning and crave for literacy has brought about the expansion of academic activities in our Universities. This has caused clashes in the use of limited available resources of each institution. In the past, ways have been sought to consider how the resources could be maximized, but to no avail. Sequel to this, this work aimed at developing an automated timetabling system which solved the problem faced in examinations scheduling. The optimization technique used was Genetic Algorithm (GA) for the scheduling. Automated exam invigilation timetabling system was developed and tested. A Faculty of Science in a reputable institution of learning in Nigeria was selected as a case study. The use of GA was suitable for solving problem(s) such as, under-utilization of available resources, clashes in scheduled courses ’ examination, faced by timetable scheduler, due to its optimization approach. GA is efficient for maximizing limited available resources. Considering the setbacks associated with the manual system of exam timetable construction, such as clashes of carry-over courses, insecurity of files, poor planning and waste of resources in terms of materials and time to effect correction on timetable: the automated system is more efficient with respect to resource optimization and online accessibility to generated timetable.