Final Year Project Allocations for Undergraduate Engineering Students in TNE Programs

Final year project allocations become a challenging task, particularly, in the case of a large number of undergraduate students enthusiast to get a project of their interest and/or to work with a supervisor of their choice. The problem is challenging as the interest of all the students should be matched while keeping the staff workload in balance. It becomes a matching problem with the constraints of staff workload, student preferences, and staff skillset. Particularly, in the Transnational Education (TNE) programs, the physical availability (or lack of it) of the staff plays an important part in the student project selections which gives an additional challenge to the allocation problem. Authors provide a review of different final year project allocation methods currently in practice and discuss their strengths and weaknesses with respect to the constraints highlighted. Authors finally conclude by discussing an algorithm which can work effectively and efficiently in the context of project allocations for TNE programs

Super-stability in the Student-Project Allocation Problem with Ties

The Student-Project Allocation problem with lecturer preferences over Students ( Open image in new window ) involves assigning students to projects based on student preferences over projects, lecturer preferences over students, and the maximum number of students that each project and lecturer can accommodate. This classical model assumes that preference lists are strictly ordered. Here, we study a generalisation of Open image in new window where ties are allowed in the preference lists of students and lecturers, which we refer to as the Student-Project Allocation problem with lecturer preferences over Students with Ties ( Open image in new window ). We investigate stable matchings under the most robust definition of stability in this context, namely super-stability. We describe the first polynomial-time algorithm to find a super-stable matching or to report that no such matching exists, given an instance of Open image in new window . Our algorithm runs in O(L) time, where L is the total length of all the preference lists. Finally, we present results obtained from an empirical evaluation of the linear-time algorithm based on randomly-generated Open image in new window instances. Our main finding is that, whilst super-stable matchings can be elusive, the probability of such a matching existing is significantly higher if ties are restricted to the lecturers’ preference lists

An Algorithm for Strong Stability in the Student-Project Allocation Problem With Ties

We study a variant of the Student-Project Allocation problem with lecturer preferences over Students where ties are allowed in the preference lists of students and lecturers (spa-st). We investigate the concept of strong stability in this context. Informally, a matching is strongly stable if there is no student and lecturer l such that if they decide to form a private arrangement outside of the matching via one of l’s proposed projects, then neither party would be worse off and at least one of them would strictly improve. We describe the first polynomial-time algorithm to find a strongly stable matching or report that no such matching exists, given an instance of spa-st. Our algorithm runs in O(m2) time, where m is the total length of the students’ preference lists

Super-stability in the student-project allocation problem with ties

The Student-Project Allocation problem with lecturer preferences over Students (spa- s) involves assigning students to projects based on student preferences over projects, lecturer preferences over students, and the maximum number of students that each project and lecturer can accommodate. This classical model assumes that each project is offered by one lecturer and that preference lists are strictly ordered. Here, we study a generalisation of spa-s where ties are allowed in the preference lists of students and lecturers, which we refer to as the Student-Project Allocation problem with lecturer preferences over Students with Ties (spa-st). We investigate stable matchings under the most robust definition of stability in this context, namely super- stability. We describe the first polynomial-time algorithm to find a super-stable matching or to report that no such matching exists, given an instance of spa-st. Our algorithm runs in O(L) time, where L is the total length of all the preference lists. Finally, we present results obtained from an empirical evaluation of the linear-time algorithm based on randomly-generated spa-st instances. Our main finding is that, whilst super-stable matchings can be elusive when ties are present in the students’ and lecturers’ preference lists, the probability of such a matching existing is significantly higher if ties are restricted to the lecturers’ preference lists

ÖĞRENCİ-PROJE ATAMA PROBLEMİNDE FARKLI GRUP KARARLARININ DEĞERLENDİRİLMESİ

Öğrenci-Proje Atama (ÖPA), genel olarak, çeşitli kriterlerin dikkate alınmasıyla öğrenci-proje gruplarının oluşturmasını ve bu gruplara projelerin atanmasını içeren çok-kriterli bir problem olarak tanımlanabilir. Bu çalışmada, problemin çözümü için üç aşamadan oluşan bir yaklaşım önerilmektedir. Yakın tarihli başka bir çalışmada geliştirilmiş olan bir 0-1 tamsayılı-hedef programlama formülasyonundan adapte edilmiş olan matematiksel programlama modeliyle, çalışmanın ilk aşamasında çeşitli kriterler dikkate alınarak öğrenci-proje gruplarının oluşturulması gerçekleştirilmektedir. Söz konusu kriterler ise (i) bir gruptaki öğrenci sayısı, (ii) genel akademik not ortalaması (GANO) değeri, (iii) yabancı dil, (iv) bilgisayar programlama, (v) genel ofis yazılımları ve (vi) veri tabanı yönetimi yetenekleridir. Sonraki aşamada, grup-proje eşleştirmeleri gerçekleştirilmeden önce, oluşturulan grupların proje tercihleri için grup üyelerinin farklı bakış açılarını yansıtan grup kararları belirlenmektedir. Son olarak, öğrenci-proje gruplarının proje tercihlerine yönelik olarak oluşturulan grup kararları kullanılarak bir 0-1 tamsayılı program ile grup-proje atamaları gerçekleştirilmektedir. Çalışmanın literatüre olan katkısı, önerilen üç aşamalı yaklaşımla, grup kararlarının dikkate alınarak ÖPA probleminin çözülmesi şeklinde özetlenebilir. Böylelikle, farklı bakış açılarına sahip çok sayıdaki öğrencinin tercihleri, ÖPA sürecinde önemli bir kriter olan tercih kriteri için yansız ve tek bir grup kararı olarak ele alınabilmektedir. Önerilen yaklaşım, akademik bir kurumdaki gerçek bir ÖPA problemine uygulanmıştır. Elde edilen sonuçlar, ilgili literatürde bulunan diğer atama yaklaşımlarının sonuçları ile çeşitli performans parametreleri açısından karşılaştırılmıştır ve kriterlerin performans skorlarında ortalama %9 oranında iyileşme olduğu gözlenmiştir

The Student-Project Allocation Problem: structure and algorithms

In this thesis we study the Student-Project Allocation problem (SPA), which is a matching problem based on the allocation of students to projects and lecturers. Students have preferences over projects, where each project is offered by one lecturer; whilst lecturers have preferences over students, or over the projects that they offer. We seek stable matchings of students to projects, which guarantee that no student and lecturer have an incentive to deviate from the matching by forming a private arrangement involving some project. We present new structural and algorithmic results for four problems related to SPA . We begin by characterising the stable matchings in an instance of the Student-Project Allocation problem with Lecturer preferences over Students (SPA-S) where the preferences are strictly ordered, in the special case that for each student in the instance, all of the projects in her preference list are offered by different lecturers. We achieve this characterisation by showing that, under this restriction, the set of stable matchings in an instance of SPA-S is a distributive lattice with respect to a natural dominance relation. Next, we study a variant of SPA - S where the preferences may involve ties — the Student- Project Allocation problem with Lecturer preferences over Students with Ties (SPA-ST). The presence of ties in the preference lists gives rise to three different concepts of stability, namely, weak stability, strong stability, and super-stability. We investigate stable matchings under the super-stability (respectively strong stability) concept. We present the first polynomial-time algorithm to find a super-stable (respectively strongly stable) matching or to report that no such matching exists, given an instance of SPA-ST . We also prove some structural results concerning the set of super-stable (respectively strongly stable) matchings in a given instance of SPA - ST . Further, we present results obtained from an empirical evaluation of our algorithms based on randomly-generated SPA-ST instances. Moving away from variants of SPA with lecturer preferences over students, we study the Student-Project Allocation problem with lecturer preferences over Projects (SPA-P). In this context it is known that stable matchings can have different sizes and the problem of finding a maximum size stable matching, denoted MAX-SPA-P , is NP-hard. There are two known approximation algorithms for MAX-SPA-P , with performance guarantees 2 and 3/2 . We show that MAX-SPA-P is polynomial-time solvable if there is only one lecturer involved, and NP-hard to approximate within some constant c > 1 if there are two lecturers involved. We also show that this problem remains NP-hard if each preference list is of length at most 3, with an arbitrary number of lecturers. We then describe an Integer Programming (IP) model to enable MAX-SPA-P to be solved optimally in the general case. Following this, we present results arising from an empirical evaluation that investigates how the solutions produced by the approximation algorithms compare to optimal solutions obtained from the IP model, with respect to the size of the stable matchings constructed, on instances that are both randomly-generated and derived from real datasets

A 3/2-approximation algorithm for the Student-Project Allocation problem

This data corresponds to the data and experiemnts described in Section 5 of the following paper submitted to SEA conference 2018: A 3/2-approximation algorithm for the Student-Project Allocation problem Authors: Frances Cooper and David Manlove The dataset file for this record is too large for automatic download. Please use the request dataset button for access