Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

Timetabling in constraint logic programming

In this paper we describe the timetabling problem and its solvability in a Constraint Logic Programming Language. A solution to the problem has been developed and implemented in ECLiPSe, since it deals with finite domains, it has well-defined interfaces between basic building blocks and supports good debugging facilities. The implemented timetable was based on the existing, currently used, timetables at the School of Informatics at out university. It integrates constraints concerning room and period availability

Using IT Support to improve the quality of Peer Assisted Learning

Peer assisted learning (PAL) is one way to increase the empowerment of students through their learning practices and, hence, enhance their learning journey. PAL involves students mentoring groups of academically less experienced students; develops the quality and diversity of student learning, and enables students to become active partners in their learning experience. PAL supports student transition into higher education and there is evidence that it can aid retention in the early weeks of degree study. Retention is becoming a key issue for universities and one of the key performance indicators (KPIs) of quality education under the strategy for higher education set out by the current government. The PAL³ project is funded by Learn Higher and is an on going project investigating the use of IT support to improve the quality of Peer Assisted Learning. The project has set up a learning environment for students, and a knowledge base for PAL student mentors and PAL and other academic staff. This paper reports on initial findings from the project which can be divided into two strands. Firstly, the compilation of a staff knowledge base has highlighted the fact that PAL is known by different names and has different meanings in different places. We provide an initial classification. Secondly, the PAL student environment, which has been implemented and used by the student cohort and their PAL student mentors, has highlighted issues that were not envisaged at the beginning of the study and this has implications for future work

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

Solving the course scheduling problem by constraint programming and simulated annealing

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 87-62)Text in English; Abstract: Turkish and Englishix, 80 leavesIn this study it has been tackled the NP-complete problem of academic class scheduling (or timetabling). The aim of this thesis is finding a feasible solution for Computer Engineering Department of İzmir Institute of Technology. Hence, a solution method for course timetabling is presented in this thesis, consisting of two phases: a constraint programming phase to provide an initial solution and a simulated annealing phase with different neighbourhood searching algorithms. When the experimental data are obtained it is noticed that according to problem structure, whether the problem is tightened or loosen constrained, the performance of a hybrid approach can change. These different behaviours of the approach are demonstrated by two different timetabling problem instances. In addition to all these, the neighbourhood searching algorithms used in the simulated annealing technique are tested in different combinations and their performances are presented

Agent based approach to University Timetabling Problem

A concept of agent-based approach to timetabling problem is presented. Based on the problem description and with its formalization the term agent is introduced. Agents act on behalf of entities taking part in the timetabling process (activities, rooms and students) and they interact to maximize their own utility. Also a brief overview of existing approaches is presented