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

Aspects of computerised timetabling

This research considers the problem of constructing high school timetables using a computer. In the majority of high schools, termly or yearly timetables are still being produced manually. Constructing a timetable is a hard and time consuming task which is carried out repeatedly thus a computer program for assisting with this problem would be of great value. This study is in three parts. First. an overall analysis of the problem is undertaken to provide background knowledge and to identify basic principles in the construction of a school timetable. The characteristics of timetabling problems are identified and the necessary data for the construction of a timetable is identified. The first part ends with the production of a heuristic model for generating an initial solution that satisfies all the hard constraints embodied in the curriculum requirements. The second stage of the research is devoted to designing a heuristic model for solving a timetable problem with hard and medium constraints. These include constraints like the various numbers of common periods, double periods and reducing the repeated allocation of a subject within any day. The approaches taken are based on two recently developed techniques, namely tabu search and simulated annealing. Both of these are used and comparisons of their efficiency are provided. The comparison is based on the percentage fulfilment of the hard and medium requirements. The third part is devoted to one of the most difficult areas in timetable construction, that is the softer requirements which are specific to particular schools and whose satisfaction is not seen as essential. This section describes the development of an expert system based on heuristic production rules to satisfy a range of soft requirements. The soft requirements are studied and recorded as rules and a heuristic solution is produced for each of the general requirements. Different levels of rule are developed, from which the best possible solution to a particular timetable problem is expertly produced. Finally, possible extensions of the proposed method and its application to other types of the timetabling problem are discussed

Courses timetabling based on hill climbing algorithm

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots

Time Table Generation: Constraint Programming through Random Function Approach

Time table Generation process involves satisfaction of number of constraints. The proposed system usesrandom function approach for generation of time table as well as satisfaction of constraints. In each step of algorithm, constraints are checked and modified constraint status is considered for nextiteration of algorithm

Genetic Algorithm For University Course Timetabling Problem

Creating timetables for institutes which deal with transport, sport, workforce, courses, examination schedules, and healthcare scheduling is a complex problem. It is difficult and time consuming to solve due to many constraints. Depending on whether the constraints are essential or desirable they are categorized as ‘hard’ and ‘soft’, respectively. Two types of timetables, namely, course and examination are designed for academic institutes. A feasible course timetable could be described as a plan for the movement of students and staff from one classroom to another, without conflicts. Being an NP-complete problem, many attempts have been made using varying computational methods to obtain optimal solutions to the timetabling problem. Genetic algorithms, based on Darwin\u27s theory of evolution is one such method. The aim of this study is to optimize a general university course scheduling process based on genetic algorithms using some defined constraints

Personal Unique Time Table Generator For Students in UTP

This project is to create a new system that generate unique timetable for students in Universiti Teknologi Petronas which include function for reducing time consumption and automate student manual process during timetabling or find alternative of slot. EasyPHP is use to create dynamic web application including the algorithm. Other than that, this project also aims to improvise the current timetable in term of Human Computer Interaction where better visual design and application of colour are included. As add on, this project can provide backup timetable in various medium such as smartphone, social network and email as both of them are the most gadget and site used by students nowadays. At same time, students can do discussion from the medium aforementioned such as Facebook’s group and GoogleGroup’s thread

Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity