New Algorithms for Examination Timetabling

Caramia, Massimiliano; Dell'Olmo, Paolo; Italiano, Giuseppe F.

doi:10.1007/3-540-44691-5_20

Cited by 69 publications

(38 citation statements)

References 12 publications

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“…Our approach is better than Di Gaspero and Schaerf (2001) for eight problem instances (car91, hec92 I, kfu93, lse91, sta83 I, tre92, ute92, uta92 I), Caramia et al (2001) for four problem instances (car91, car92, sta83 I, tre92), Paquete and Stuetzle (2002) for three problem instances (kfu93, lse91, ute92) and a tie with tre92 and (sta83 I) and Eley (2007) (car91) for one problem instance. Only two approaches (Caramia et al (2001) and Eley (2007)) provided results for pur93 and only three approaches (Caramia et al (2001), Merlot et al (2003) and Eley (2007)) provided result for rye93 while, Paquete and Stuetzle (2002) did not provide any result for car91, car92 and also for uta92 I. Caramia et al (2001), (3) Paquete and Stuetzle (2002), (4) , (5) Merlot et al (2003), (6) Eley (2007), (7) Best obtained using heuristic combinations for δ = 3 and n = 3) (The bold entries indicate the best results).…”

Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 89%

“…These approaches usually consist of multiple phases that first attempts to construct a feasible solution before proceeding with the improvement phases. Caramia et al (2001) proposed a multi phase local search algorithms that starts with a greedy scheduler to create a feasible timetable. A penalty-decreaser and penalty-trader then were used to improve the solution quality.…”

Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 99%

“…Only two approaches (Caramia et al (2001) and Eley (2007)) provided results for pur93 and only three approaches (Caramia et al (2001), Merlot et al (2003) and Eley (2007)) provided result for rye93 while, Paquete and Stuetzle (2002) did not provide any result for car91, car92 and also for uta92 I. Caramia et al (2001), (3) Paquete and Stuetzle (2002), (4) , (5) Merlot et al (2003), (6) Eley (2007), (7) Best obtained using heuristic combinations for δ = 3 and n = 3) (The bold entries indicate the best results).…”

Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 99%

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A constructive approach to examination timetabling based on adaptive decomposition and ordering

et al. 2011

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In this study, we investigate a constructive local search approach for examination timetabling which employs an adaptive decomposition strategy as its key feature. This strategy automatically divides examinations into difficult and easy sets. The examinations in the difficult set are considered to be hard to place into a timetable slot and hence are listed before the ones in the easy set. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics, separately. Initially, the examinations are placed into the easy set. During the construction process, the examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment than the others for subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that its performance is promising and comparable to existing constructive and improvement approaches.

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Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 89%

Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 99%

Section: Comparison To the Previous Improvement Approachesmentioning

confidence: 99%

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A constructive approach to examination timetabling based on adaptive decomposition and ordering

et al. 2011

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“…Different higher level heuristics/techniques employed within a hyper-heuristic framework include Case Based Reasoning 25 , choice functions 31 and meta-heuristic methods 32,33 . Many of the current state of the art approaches in exam timetabling employ specially tailored heuristic/meta-heuristics methods 20,22,[34][35][36][37][38][39] . This kind of approach is also typical for other scheduling problems.…”

Section: Timetabling Problemsmentioning

confidence: 99%

“…Table 2 also presents the current best published results on these benchmarks by 8 other approaches reported in the literature 20,22,[34][35][36][37][38][39] . The best results are presented in bold.…”

Section: Experiments On Real-world Problemsmentioning

confidence: 99%

Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems

Burke

Dror

Petrović

et al. 2005

Operations Research/Computer Science Interfaces Series

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Abstract:This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.

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Iterative coloring extension of a maximum clique

Caramia

Dell'Olmo²

2001

Naval Research Logistics

Self Cite

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Abstract:In this paper we present an improved branch and bound algorithm for the vertex coloring problem. The idea is to try to extend the coloring of a maximum clique to its adjacent vertices. If this succeeds, its successive neighbors are considered; in case of failure (i.e., in the case the initial colors are not sufficient), working on the subgraph induced by the maximum clique and its neighborhood, the lower bound is improved by seeking for an optimal coloring of this subgraph by branch and bound. The process is repeated iteratively until the whole graph is examined. The iterative scheme exploits a further lower bound obtained by integrating a simple algorithm into the maximum clique search, and a new method to compute upper bounds on subgraphs. Furthermore, a new branching rule and a method for the selection of the initial maximum clique are presented. Extensive computational results and comparisons with existing exact coloring algorithms on random graphs and benchmarks are given.

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New Algorithms for Examination Timetabling

Cited by 69 publications

References 12 publications

A constructive approach to examination timetabling based on adaptive decomposition and ordering

A constructive approach to examination timetabling based on adaptive decomposition and ordering

Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems

Iterative coloring extension of a maximum clique

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