Distributed scatter search for the examination timetabling problem

http://doc.utwente.nl/75057/1/PATAT10_Proceedings.pdf#page=225Examination Timetabling for Universities is a problem with significant practical importance. It belongs to the general class of educational timetabling problems and has been exposed to numerous approaches for solving it. We propose a parallel/distributed solution which is based on the metaheuristic method Scatter Search combined with Path Relinking in an attempt to diversify the search procedure by producing promising new timetables. Our approach improves on the best publicly available results for the datasets of ITC2007 (International Timetabling Competition 2007-2008). The constraint of limited execution time that was imposed by ITC2007 was disregarded in an effort to pursue the best values our approach could reach. We consider this specific examination timetabling problem as a “test bed” for timetabling problems in general and we expect to provide insight for developing effective solution processes for other practical scheduling problems

Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

Intelligent examination timetabling system using hybrid intelligent water drops algorithm

This paper proposes Hybrid Intelligent Water Drops (HIWD) algorithm to solve Tamhidi programs uncapacitated examination timetabling problem in Universiti Sains Islamic Malaysia (USIM).Intelligent Water Drops algorithm (IWD) is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to better drops.The results of this study prove that the proposed algorithm can produce a high quality examination timetable in shorter time in comparison with the manual timetable

Grammatical evolution hyper-heuristic for combinatorial optimization problems

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains

A step counting hill climbing algorithm

This paper presents a new single-parameter local search heuristic named Step Counting Hill Climbing algorithm (SCHC). It is a very simple method in which the current cost serves as an acceptance bound for a number of consecutive steps. This is the only parameter in the method that should be set up by the user. Furthermore, the counting of steps can be organized in different ways; therefore the proposed method can generate a large number of variants and also extensions. In this paper, we investigate the behaviour of the three basic variants of SCHC on the university exam timetabling problem. Our experiments demonstrate that the proposed method shares the main properties with the Late Acceptance Hill Climbing method, namely its convergence time is proportional to the value of its parameter and a non-linear rescaling of a problem does not affect its search performance. However, our new method has two additional advantages: a more flexible acceptance condition and better overall performance. In this study we compare the new method with Late Acceptance Hill Climbing, Simulated Annealing and Great Deluge Algorithm. The Step Counting Hill Climbing has shown the strongest performance on the most of our benchmark problems used

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive