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

A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling

A university course timetabling problem is a combination of optimization problems. The problems are more challenging when a set of events need to be scheduled in the time slot, to be located to the suitable rooms, which is subjected to several sets of hard and soft constraints. All these constraints that exist as regulations within each resource for the event need to be fulfilled in order to achieve the optimum tasks. In addition, the design of course timetables for universities is a very difficult task because it is a non-deterministic polynomial, (NP) hard problem. This problem can be minimized by using a Micro Genetic Algorithm approach. This approach, encodes a chromosome representation as one of the key elements to ensure the infeasible individual chromosome produced is minimized. Thus, this study proposes an encoding chromosome representation using one-dimensional arrays to improve the Micro Genetic algorithm approach to soft constraint problems in the university course schedule. The research contribution of this study is in developing effective and feasible timetabling software using Micro Genetic Algorithm approach in order to minimize the production of an infeasible individual chromosome compared to the existing optimization algorithm for university course timetabling where UNITAR International University have been used as a data sample. The Micro Genetic Algorithm proposed has been tested in a test comparison with the Standard Genetic algorithm and the Guided Search Genetic algorithm as a benchmark. The results showed that the proposed algorithm is able to generate a minimum number of an infeasible individual chromosome. The result from the experiment also demonstrated that the Micro Genetic Algorithm is capable to produce the best course schedule to the UNITAR International University

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