Examination timetabling is a well-studied combinatorial optimization problem. We present a new hybrid algorithm for examination timetabling, consisting of three phases: a constraint programming phase to develop an initial solution, a simulated annealing phase to improve the quality of solution, and a hill climbing phase for further improvement.

Abstract-This paper investigates a genetic algorithm based hyperheuristic (hyper-GA) for scheduling geographically distributed training staff and courses. The aim of the hyper-GA is to evolve a good-quality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of low-level heuristics. Since the user only supplies a number of low-level problem-specific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyper-GA, presenting results for four versions of the hyper-GA as well as a range of simpler heuristics and applying them to five test data set 1.

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This paper is concerned with the d2 elopment of a mechanism for automatically setting the parameters as well as refining the choice function. Tod o so we apply the hyperheuristic to a real-world personnel sched5qUW problem, that of schedUEI: a sales summit. The remaind5 of the paper is structured as follows. We firstd escribe and formulate the sales summit schedI/Eq problem. This is followed by thed25H iption of our parameter-free hyperheuristic approach in section 3. Section 4 is d2 oted to experimental resultsand section 5concludx the paper. 2 The Sales Summit Scheduling Problem The sales summit sched uling problem is that of a commercial company that organises sales summits which involve two groups of company representatives: suppliers, who want to sell prodfifi/ or services, delegates who are representatives of companies that are potentially interested in purchasing the prod:qH and services. Suppliers pay a registration fee to have a stand at the sales summit and provid a list of thed2qIUUfi2 that they would like to meet. A meeting (between oned elegate and one supplier) is classified as Priority or Non-Priority d2 endEE on how strongly the supplier would like to meet thed elegate. Delegates pay no fee but instead cost money to the commercial company who pays for their travelling and hotel expenses. Besid2 meetings, seminars are organised whered elegates may meet otherd elegates. Eachd elegate providE a list of the seminars that he/she would like to attend and (if he/she is invited to the summit) is guaranteed attend nce to all seminars. In this instance of the sales summit schedI//H problem there are 4 meeting timeslots available for both seminars and meetings, where each seminar lasts three times as long as a supplier/d55W ate meeting. There are 43 suppliers, 99 ...

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Employee timetabling is the operation of assigning employees to tasks in a set of shifts during a xed period of time, typically a week. We present a general de nition of employee timetabling problems (ETPs) that captures many real-world problem formulations and includes complex constraints. The proposed model of ETPs can be represented in a tabular form that is both intuitive and ecient for constraint representation and processing. The constraint networks of ETPs include non-binary constraints and are dicult to formulate in terms of simple constraint solvers. We investigate the use of local search techniques for solving ETPs. In particular, we propose several versions of hill-climbing that make use of a novel search space that includes also partial assignments.

Abstract. A recent trend in local search concerns the exploitation of several different neighbourhood functions so as to increase the ability of the algorithm to navigate the search space. In this work we investigate the use of local search techniques based on various combinations of neighbourhood functions, and we apply it to a timetabling problem. In particular, we propose a set of generic operators that automatically compose neighbourhood functions, giving rise to more complex ones. In the exploration of large neighbourhoods, we rely on constraint techniques to prune the list of candidates. This way, we are able to select the most effective search technique through a systematic analysis of all possible combinations built upon a set of basic, humandefined, neighbourhood functions. The proposed ideas are applied to a practical problem, namely the socalled Course Timetabling problem. Our algorithms are systematically tested and compared on real-world instances. The experimental analysis shows that neighbourhood composition leads to much better results than traditional local search techniques. 1

Abstract- Course timetabling problems are real world constraint optimization problems that are often coped with in educational institutions, such as universities or high schools. In this paper, we present a variety of new operators that can be also applied in evolutionary algorithms for other timetabling problems, such as, exam timetabling. Operators include violation directed mutations, crossovers, and a successful violation directed hierarchical hill climbing method. Tests are performed on a small portion of a real data and results are promising. 1

This paper presents amultiple-retriev6 approach that partitions a large problem into smallsolvS40 sub-problems byrecursiv5/ inputting theunsolv/ part of the graph into the decision tree for retrievSW The adaptation combines theretriev4 partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependent upon problem-specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retriev CBR could be aneffectiv initialization method for local search methods such as hill climbing, tabu search and simulated annealing. Significant results are obtained from a wide range of experiments. An ev56qA9SW of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent aneffectiv initialization method for these approaches

A common weakness of local search metaheuristics, such as Simulated Annealing, in solving combinatorial optimisation problems, is the necessity of setting a certain number of parameters. This tends to make significantly increase the total amount of time required to solve the problem and often requires a high level of experience from the user. This paper is motivated by the goal of overcoming this drawback by employing "parameter-free" techniques in the context of automatically solving course timetabling problems.