IEEE TRANSACTIONS ON EDUCATION, VOL. 51, NO. 4, NOVEMBER 2008 439 Efficient Fuzzy Evolutionary Algorithm-Based Approach for Solving the Student Project Allocation Problem Dipti Srinivasan, Senior Member, IEEE, and Lily Rachmawati, Student Member, IEEE Abstract—This paper presents a solution framework for the student project allocation (SPA) problem which is based on evo- lutionary algorithms (EAs), a biologically inspired stochastic, population-based search paradigm. Project-based assessment is a common component of engineering courses that are conducted in universities around the world. In their final year of study, a list of projects is made available by the academic staff and students are required to select a specific number of options from this list. The department then assigns a suitable project to each student such that preferred projects can be allocated to as many students as possible. While student interest is the primary criteria, several additional factors need to be considered such as project prerequisites, load balancing of staff commitments, and other specific university requirements. The allocation problem can therefore be seen as a complex multiobjective problem with multiple constraints. The EA-based project allocation system was recently developed and implemented in a large university department to automate this process, and to improve the matching of students to their desired projects. The solution which provides the highest level of satisfac- tion in meeting the varied objectives is then used to allocate projects to students. This new automated system is not only able to achieve a very high level of user satisfaction, but is also able to do so in a very short time, resulting in significant time savings. Index Terms—Evolutionary algorithm (EA), hybrid fuzzy evolu- tionary algorithm, student project allocation (SPA) problem. I. INTRODUCTION P ROJECT-BASED assessment is a common component of engineering courses conducted in universities around the world, with final-year students often being required to embark on a project that typically spans two semesters. The aim of this component is to encourage students to learn how to apply skills acquired in the classroom and to contemplate innovative ways of solving problems. The final-year projects (FYPs) are usu- ally open-ended in nature, giving the students flexibility in ju- diciously selecting viable alternatives, and challenging them to innovate in terms of initiating new concepts and designs [7]. The benefits of this exercise include the intrinsic rewards that come with solving real-life problems and the inculcation of the skills necessary for independent learning. It is believed that such projects lay the foundation for lifelong learning. Manuscript received November 23, 2006; revised July 25, 2007. Current ver- sion published November 5, 2008. The authors are with the Department of Electrical and Computer Engi- neering, National University of Singapore, Singapore 119260, Singapore (e-mail: dipti@nus.edu.sg). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TE.2007.912537 Typically, a list of projects is proposed by the university’s aca- demic staff and by researchers from institutions within the uni- versity. Students are required to select a specific number of these projects, list them in order of preference, and submit the list to their department. The challenge of finding a satisfactory match between students and the available projects, given overlapping student choices and other university requirements, brings about what is known as the student project allocation (SPA) problem. This problem has repeatedly proven to be computationally de- manding and time-consuming. The sheer size of the solution space means that the probability of finding an optimal solution using conventional resource allocation approaches is very small [1]–[6]. A “satisfactory” allocation is one that can be deemed both effective and efficient [1], [2], such that it optimizes stu- dent satisfaction while dealing suitably with the combinatorial complexity of the problem. In large university departments, the allocation of projects to students is typically performed manually by a human decision maker (DM) in a process that requires several iterations and takes several weeks to complete. Over the past few years, sev- eral techniques have been proposed to solve such complex re- source allocation problems [1]–[4]. However, most of these ap- proaches cannot be directly applied to the SPA problem in large universities because there are simply too many conflicting goals and constraints. The purpose of this paper is to present a novel method of addressing the SPA problem, taking into account all of the considerations that have been raised so far: computational efficiency, student satisfaction, and departmental satisfaction. Keeping in mind the limited number of published works in the field of SPA, the following is a review of promising de- velopments that have occurred in the last few years. An expert system in this particular application domain was designed and presented by Teo and Ho in [5]. This knowledge-based model was applied to a single-objective SPA problem on a dataset in- volving 413 projects and 330 pairs of students who were allowed to submit a list of up to ten projects in order of preference. The algorithm operated in a sequential manner through the list of preferences. A project preferred by a student-pair is allocated di- rectly when there is no competition for that project. Otherwise, the project is allocated randomly to one of the competing stu- dent-pairs. Thus, the algorithm proceeds with the next-ranked preferences for student pairs yet to be allocated any projects. The algorithm managed to allocate a project within the preferred list of 10 projects to 79.9% of the student pairs. In [6], Anwar and Bahaj proposed an integer programming approach which consumes less computational time. Their in- 0018-9359/$25.00 © 2008 IEEE