A two-phase heuristic evolutionary algorithm for personalizing course timetables: a case study in a Spanish university

Abstract

This paper presents, as a case study, the application of a two-phase heuristic evolutionary algorithm to obtain personalized timetables in a Spanish university. The algorithm consists of a two-phase heuristic, which, starting from an initial ordering of the students, allocates students into groups, taking into account the student's preferences as a primal factor for the assignment. An evolutionary algorithm is then used in order to select the ordering of students which provides the best assignment.The algorithm has been tested in a real problem, the timetable of the Telecommunication Engineering School at Universidade de Vigo (Spain), and has shown good performance in terms of the number of constraints fulÿlled and groups assigned to students. ?

Interactively Solving School Timetabling Problems Using Extensions of Constraint Programming

“…Students need to be assigned to courses in the classical SSP (the timetable is already done) whereas our problem consists in assigning courses to timeslots (the timetable is built according to student's requests). A similar problem is solved in [22] where a feasible and personalized weekly timetable is assigned to every student of a Spanish Engineering School. But in this case again, several groups have been already done for every subject, every group has a fixed timetable and is already scheduled to lecturers.…”

Final sınav programı hazırlama problemine ait bir matematiksel model ve uygulama

“…Bu amaçla, farklı kalitedeki çözümleri oluşturmakta rastgele iteratif bir grafik tabanlı üst-sezgisel sunmuşlardır. Santiago-Mozos, Salcedo-Sanz, Deprado-Cumplido ve Bousono-Calzon (2005) ders çizelgeleri için iki aşamalı bir sezgisel evrimsel algoritma önermiş ve bir İspanyol üniversitesine uygulamışlardır. Burke, Eckersley, McCollum, Petrovic ve Qu (2010) üniversite sınav çizelgeleme problemi için melez bir değişken komşu arama (DKA) yaklaşımı sunmuşlardır.…”

“…Bu amaçla, farklı kalitedeki çözümleri oluşturmakta rastgele iteratif bir grafik tabanlı üst-sezgisel sunmuşlardır. Santiago-Mozos, Salcedo-Sanz, Deprado-Cumplido ve Bousono-Calzon (2005) Bu çalışmada üniversitelerin final sınav çizelgelerinin hazırlanması esnasında karşılaştıkları problemlerin üstesinden gelmek amacıyla karma tamsayılı bir matematiksel model geliştirilmiştir. Karma tamsayılı programlama problemi, çoğu uygulama probleminde olduğu gibi bazı karar değişkenlerinin tamsayı, bazılarının ise reel sayı olması gerektiği doğrusal programlama problemleridir.…”

Applying the threshold accepting metaheuristic to curriculum based course timetabling

“…Known (meta-)heuristic approaches are based graph coloring methods (Burke et al 2007), tabu search (Lü and Hao 2010;White et al 2004), simulated annealing (Thompson and Dowsland 1998), evolutionary algorithms (Santiago-Mozos et al 2005), case based reasoning (Burke et al 2006), or hybrid approaches (Chiarandini et al 2006;De Causmaecker et al 2009). For a recent overview, we may refer to (Lewis 2008).…”