Using weight aggregation in tabu search for multiobjective exams timetabling problem

EnExams timetabling is a difficult task in many educational institutions. We can distinct two major sets of constraints when defining exams timetabling problems, categorized in soft and hard constraints. Guaranteing that any student as a non overlapping exams schedule and that necessary requirements like rooms and teacher are available are consider hard constraints. An evenly distributed schedule, a short duration of the overall exams period can be regarded as soft constraints. To handle soft constraints under the hard constraints verification we adopted a multiobjective optimization approach. This problem is NP-hard for which we have developed an heuristic tabu search method to find a solution. Tabu search comprises an iterative local search defined as a neighborhood inspection of a certain point in the search space. To find an improved solution we have to evaluate points in this neighborhood which can be considered a multiple attribute decision problem. In this context we have used multicriteria methods in order to rank the solutions

An Assignment Problem and Its Application in Education Domain: A Review and Potential Path

This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign n subjects to m subjects in the best possible way.With that, this paper classified assignment problems into two, which are timetabling problem and allocation problem. The timetabling problem is further classified into examination, course, and school timetabling problems, while the allocation problem is divided into student-project allocation, new student allocation, and space allocation problems. Furthermore, the constraints, which are of hard and soft constraints, involved in the said problems are briefly elaborated.In addition, this paper presents various approaches to address various types of assignment problem. Moreover, direction and potential paths of problem solving based on the latest trend of approaches are also highlighted.As such, this review summarizes and records a comprehensive survey regarding assignment problem within education domain, which enhances one's understanding concerning the varied types of assignment problems, along with various approaches that serve as solution

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

This paper provides a mathematical treatment of the NP-hard post enrolment-based course timetabling problem and presents a powerful two-stage metaheuristic-based algorithm to approximately solve it. We focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators, the quality of solutions achieved is generally enhanced. Across a well-known suite of benchmark problem instances, our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature; however, we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why

COMBINATION OF GENETIC ALGORITHM AND TABU LIST IN THE CASE OF EXAM SCHEDULING

The exam scheduling system aims to provide solutions for scheduling exams that have been done manually. Several studies on scheduling have been done, one with a genetic algorithm. But there are still weaknesses that occur in the genetic algorithm is often trapped in local optimum. This study developed a scheduling system using a combination of genetic algorithms and tabu lists to address the weaknesses of the previous system. This research uses Immanuel Christian University data on Informatics Engineering course. Genetic algorithm and taboo list results show that genetic algorithm succeeded in avoiding local optimum by using taboo list as a requirement that genetic result value should not be used if the result is already in taboo list. The process of scheduling using genetic algorithms and tabu lists can reduce the generation of more than the genetic algorithm. But the disadvantage of genetic algorithms with taboo lists is that process time tends to be slower than the genetic algorithm without taboo lists

Fixture-scheduling for the Australian Football League using a Multi-objective Evolutionary Algorithm

AFL football is a team sport that entertains millions and contributes a huge amount of money to the Australian economy. Scheduling games in the AFL is difficult, as a number of different, often conflicting, factors must be considered. In this paper, we propose the use of a multi-objective evolutionary algorithm for determining such a schedule. We detail the technical details needed to apply a multi-objective evolutionary algorithm to this problem and report on experiments that show the effectiveness of this approach. Comparison with actual schedules used in the AFL demonstrates that this approach could make a useful contribution

Evolutionary multi-objective optimization in scheduling problems

Ph.DDOCTOR OF PHILOSOPH

An investigation of multi-objective hyper-heuristics for multi-objective optimisation

In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems

Ant algorithm hyperheuristic approaches for scheduling problems

For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques

Evolutionary computing for routing and scheduling applications

Ph.DDOCTOR OF PHILOSOPH