Cyclic transfers in school timetabling

In this paper we propose a neighbourhood structure based on sequential/cyclic moves and a cyclic transfer algorithm for the high school timetabling problem. This method enables execution of complex moves for improving an existing solution, while dealing with the challenge of exploring the neighbourhood efficiently. An improvement graph is used in which certain negative cycles correspond to the neighbours; these cycles are explored using a recursive method. We address the problem of applying large neighbourhood structure methods on problems where the cost function is not exactly the sum of independent cost functions, as it is in the set partitioning problem. For computational experiments we use four real world data sets for high school timetabling in the Netherlands and England.We present results of the cyclic transfer algorithm with different settings on these data sets. The costs decrease by 8–28% if we use the cyclic transfers for local optimization compared to our initial solutions. The quality of the best initial solutions are comparable to the solutions found in practice by timetablers

Cyclic transfers in school timetabling

In this paper we propose a neighbourhood structure based\ud on sequential/cyclic moves and a Cyclic Transfer algorithm for the high school timetabling problem. This method enables execution of complex moves for improving an existing solution, while dealing with the challenge of exploring the neighbourhood efficiently. An improvement graph is used in which certain negative cycles correspond to the neighbours; these cycles are explored using a recursive method. We address the problem of applying large neighbourhood structure methods on problems where the cost function is not exactly the sum of independent cost functions, as it is in the set partitioning problem. For computational experiments we use four real world datasets for high school timetabling in the Netherlands and England. We present results of the cyclic transfer algorithm with different settings on these datasets. The costs decrease by 8% to 28% if we use the cyclic transfers for local optimization compared to our initial solutions. The quality of the best initial solutions are comparable to the solutions found in practice by timetablers

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

Structure based partial solution search for the examination timetabling problem.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The aim of this work is to present a new approach, namely, Structure Based Partial Solution Search (SBPSS) to solve the Examination Timetabling Problem. The success of the Developmental Approach in this problem domain suggested that the strategy of searching the spaces of partial timetables whilst constructing them is promising and worth pursuing. This work adopts a similar strategy. Multiple timetables are incrementally constructed at the same time. The quality of the partial timetables is improved upon by searching their partial solution spaces at every iteration during construction. Another key finding from the literature survey revealed that although timetables may exhibit the same behaviour in terms of their objective values, their structures or exam schedules may be different. The challenge with this finding is to decide on which regions to pursue because some regions may not be worth investigating due to the difficulty in searching them. These problematic areas may have solutions that are not amenable to change which makes it difficult to improve them. Another reason is that the neighbourhoods of solutions in these areas may be less connected than others which may restrict the ability of the search to move to a better solution in that neighbourhood. By moving to these problematic areas of the search space the search may stagnate and waste expensive computational resources. One way to overcome this challenge is to use both structure and behaviour in the search and not only behaviour alone to guide the search. A search that is guided by structure is able to find new regions by considering the structural components of the candidate solutions which indicate which part of the search space the same candidates occupy. Another benefit to making use of a structure-based search is that it has no objective value bias because it is not guided by only the objective value. This statement is consistent with the literature survey where it is suggested that in order to achieve good performance the search should not be guided by only the objective value. The proposed method has been tested on three popular benchmark sets for examination timetabling, namely, the Carter benchmark set; the benchmark set from the International Timetabling competition in 2007 and the Yeditepe benchmark set. The SBPSS found the best solutions for two of the Carter problem instances. The SBPSS found the best solutions for four of the competition problem instances. Lastly, the SBPSS improved on the best results for all the Yeditepe problem instances

Heuristic approaches for real world examination timetabling problems

The examination timetabling (exam-timeslot-room assignment) problem involves assigning exams to a specific or limited number of timeslots and rooms, with the aim of satisfying the hard constraints and the soft constraints as much as possible. Most of the techniques reported in the literature have been applied to solve simplified examination benchmark datasets, available within the scientific literature. In this research we bridge the gap between research and practice by investigating a problem taken from the Universiti Malaysia Pahang (UMP), a real world capacitated examination timetabling problem. This dataset has several novel constraints, in addition to those commonly used in the literature. Additionally, the invigilator scheduling problem (invigilator assignment) was also investigated as it has not received the same level of research attention as the examination scheduling (although it is just as important to educational institutions). The formal models are defined, and constructive heuristics was developed for both problems in which the overall problems are solved with a two-phase approach which involves scheduling the exam to timeslot and room, and follows with scheduling the invigilator. During the invigilator assignment, we assume that there is already an examination timetable in place (i.e. previously generated). It reveals that the invigilator scheduling solution dependent on the number of rooms selected from the exam-timeslot-room assignment phase (i.e. a lesser number of used rooms would minimises the invigilation duties for staff), this encourages us to further improve the exam-timeslot-room timetable solution. An improvement on the result was carried out using modified extended great deluge algorithm (modified-GDA) and multi-neighbourhood GDA approach (that use more than one neighbourhood during the search). The modified-GDA uses a simple to understand parameter and allows the boundary that acts as the acceptance level, to dynamically change during the search. The propose approaches able to produce good quality solution when compared to the solutions from the proprietary software used by UMP. In addition, our solutions adhere to all hard constraints which the current systems fail to do. Finally, we extend our research onto investigating the Second International Timetabling Competition (ITC2007) dataset as it also contains numerous constraints much similar to UMP datasets. Our propose approach able to produce competitive solutions when compared to the solutions produced by other reported works in the literature

Search methodologies for examination timetabling

Working with examination timetabling is an extremely challenging task due to the difficulty of finding good quality solutions. Most of the studies in this area rely on improvement techniques to enhance the solution quality after generating an initial solution. Nevertheless, the initial solution generation itself can provide good solution quality even though the ordering strategies often using graph colouring heuristics, are typically quite simple. Indeed, there are examples where some of the produced solutions are better than the ones produced in the literature with an improvement phase. This research concentrates on constructive approaches which are based on squeaky wheel optimisation i.e. the focus is upon finding difficult examinations in their assignment and changing their position in a heuristic ordering. In the first phase, the work is focused on the squeaky wheel optimisation approach where the ordering is permutated in a block of examinations in order to find the best ordering. Heuristics are alternated during the search as each heuristic produces a different value of a heuristic modifier. This strategy could improve the solution quality when a stochastic process is incorporated. Motivated by this first phase, a squeaky wheel optimisation concept is then combined with graph colouring heuristics in a linear form with weights aggregation. The aim is to generalise the constructive approach using information from given heuristics for finding difficult examinations and it works well across tested problems. Each parameter is invoked with a normalisation strategy in order to generalise the specific problem data. In the next phase, the information obtained from the process of building an infeasible timetable is used. The examinations that caused infeasibility are given attention because, logically, they are hard to place in the timetable and so they are treated first. In the adaptive decomposition strategy, the aim is to automatically divide examinations into difficult and easy sets so as to give attention to difficult examinations. Within the easy set, a subset called the boundary set is used to accommodate shuffling strategies to change the given ordering of examinations. Consequently, the graph colouring heuristics are employed on those constructive approaches and it is shown that dynamic ordering is an effective way to permute the ordering. The next research chapter concentrates on the improvement approach where variable neighbourhood search with great deluge algorithm is investigated using various neighbourhood orderings and initialisation strategies. The approach incorporated with a repair mechanism in order to amend some of infeasible assignment and at the same time aiming to improve the solution quality

Heuristic approaches for real world examination timetabling problems

The examination timetabling (exam-timeslot-room assignment) problem involves assigning exams to a specific or limited number of timeslots and rooms, with the aim of satisfying the hard constraints and the soft constraints as much as possible. Most of the techniques reported in the literature have been applied to solve simplified examination benchmark datasets, available within the scientific literature. In this research we bridge the gap between research and practice by investigating a problem taken from the Universiti Malaysia Pahang (UMP), a real world capacitated examination timetabling problem. This dataset has several novel constraints, in addition to those commonly used in the literature. Additionally, the invigilator scheduling problem (invigilator assignment) was also investigated as it has not received the same level of research attention as the examination scheduling (although it is just as important to educational institutions). The formal models are defined, and constructive heuristics was developed for both problems in which the overall problems are solved with a two-phase approach which involves scheduling the exam to timeslot and room, and follows with scheduling the invigilator. During the invigilator assignment, we assume that there is already an examination timetable in place (i.e. previously generated). It reveals that the invigilator scheduling solution dependent on the number of rooms selected from the exam-timeslot-room assignment phase (i.e. a lesser number of used rooms would minimises the invigilation duties for staff), this encourages us to further improve the exam-timeslot-room timetable solution. An improvement on the result was carried out using modified extended great deluge algorithm (modified-GDA) and multi-neighbourhood GDA approach (that use more than one neighbourhood during the search). The modified-GDA uses a simple to understand parameter and allows the boundary that acts as the acceptance level, to dynamically change during the search. The propose approaches able to produce good quality solution when compared to the solutions from the proprietary software used by UMP. In addition, our solutions adhere to all hard constraints which the current systems fail to do. Finally, we extend our research onto investigating the Second International Timetabling Competition (ITC2007) dataset as it also contains numerous constraints much similar to UMP datasets. Our propose approach able to produce competitive solutions when compared to the solutions produced by other reported works in the literature

Search methodologies for examination timetabling

Working with examination timetabling is an extremely challenging task due to the difficulty of finding good quality solutions. Most of the studies in this area rely on improvement techniques to enhance the solution quality after generating an initial solution. Nevertheless, the initial solution generation itself can provide good solution quality even though the ordering strategies often using graph colouring heuristics, are typically quite simple. Indeed, there are examples where some of the produced solutions are better than the ones produced in the literature with an improvement phase. This research concentrates on constructive approaches which are based on squeaky wheel optimisation i.e. the focus is upon finding difficult examinations in their assignment and changing their position in a heuristic ordering. In the first phase, the work is focused on the squeaky wheel optimisation approach where the ordering is permutated in a block of examinations in order to find the best ordering. Heuristics are alternated during the search as each heuristic produces a different value of a heuristic modifier. This strategy could improve the solution quality when a stochastic process is incorporated. Motivated by this first phase, a squeaky wheel optimisation concept is then combined with graph colouring heuristics in a linear form with weights aggregation. The aim is to generalise the constructive approach using information from given heuristics for finding difficult examinations and it works well across tested problems. Each parameter is invoked with a normalisation strategy in order to generalise the specific problem data. In the next phase, the information obtained from the process of building an infeasible timetable is used. The examinations that caused infeasibility are given attention because, logically, they are hard to place in the timetable and so they are treated first. In the adaptive decomposition strategy, the aim is to automatically divide examinations into difficult and easy sets so as to give attention to difficult examinations. Within the easy set, a subset called the boundary set is used to accommodate shuffling strategies to change the given ordering of examinations. Consequently, the graph colouring heuristics are employed on those constructive approaches and it is shown that dynamic ordering is an effective way to permute the ordering. The next research chapter concentrates on the improvement approach where variable neighbourhood search with great deluge algorithm is investigated using various neighbourhood orderings and initialisation strategies. The approach incorporated with a repair mechanism in order to amend some of infeasible assignment and at the same time aiming to improve the solution quality

Harmony Search-Based Hyper-Heuristic For Scheduling Problems

Scheduling problems are encountered in every field which are typically related to services such as in hospital, transportation, and also educational institutions. These problems can be extremely difficult when its involves a large number of events or resources to be scheduled and a wide variety of constraints which need to be satisfied or taken into consideration. Hence, many heuristic and meta-heuristic approaches have been introduced to solve these problems. However, some institutions look for a method that can deal with a wide range of scheduling problems without spending a lot of money and times as long the solutions produced are acceptable