A Case Study of Model-Driven Engineering for Automated Timetabling

Large educational institutions such as universities need to efficiently allocate their staff and students into teaching and examination timetables, while respecting hard constraints and taking into account preferences. The timetables produced automatically by commercial products are generally unsatisfactory, and considerable manual modification is required before they can be used. The authors have designed and implemented an algorithm which produces good results, but the next challenge is to integrate it into real universities. This involves synchronizing with existing information systems, as well as presenting a user interface that can be used by timetabling staff to view and manipulate the generated timetables and to configure the algorithm. Given the limited number of developers available, the high productivity offered by model-driven engineering was deemed necessary. In this paper, the authors show how they have combined several transformation tools, persistence frameworks and model-driven UI approaches to deliver a first version of an integrated solution for automated timetabling. The authors identify areas of future work and places where the current state of the art could be further developed.

[1]  Agostinho C. Rosa,et al.  A fast simulated annealing algorithm for the examination timetabling problem , 2019, Expert Syst. Appl..

[2]  Monirul Hasan,et al.  University Timetable Generator Using Tabu Search , 2016 .

[3]  Hartmut Ehrig,et al.  Triple Graph Grammars in the Large for Translating Satellite Procedures , 2014, ICMT.

[4]  Dome Lohpetch,et al.  A Hybrid Multi-objective Genetic Algorithm with a New Local Search Approach for Solving the Post Enrolment Based Course Timetabling Problem , 2016 .

[5]  Richard F. Paige,et al.  The Epsilon Transformation Language , 2008, ICMT@TOOLS.

[6]  Pennee Wangmaeteekul,et al.  Solving a Teaching Assistant Timetabling Using Constraint Based Approach , 2016, 2016 International Conference on Computational Intelligence and Applications (ICCIA).

[7]  Zahid Iqbal,et al.  Study of hybrid approaches used for university course timetable problem (UCTP) , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[8]  Jort van Mourik,et al.  Palette-colouring: a belief-propagation approach , 2011, ArXiv.

[9]  Souad Larabi Marie-Sainte.  A new hybrid particle swarm optimization algorithm for real-world university examination timetabling problem , 2017, 2017 Computing Conference.

[10]  Richard F. Paige,et al.  Parallel Model Validation with Epsilon , 2018, ECMFA.

[11]  Jordi Cabot,et al.  Map-Based Transparent Persistence for Very Large Models , 2015, FASE.

[12]  Jordi Cabot,et al.  Mogwaï: A framework to handle complex queries on large models , 2016, 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS).

[13]  Calvin C. Gotlieb,et al.  The Construction of Class-Teacher Time-Tables , 1962, IFIP Congress.

[14]  Anthony Wren,et al.  Scheduling, Timetabling and Rostering - A Special Relationship? , 1995, PATAT.

[15]  Richard F. Paige,et al.  Eugenia: towards disciplined and automated development of GMF-based graphical model editors , 2017, Software & Systems Modeling.

[16]  Robertas Damaševičius,et al.  Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm , 2019 .

[17]  Alcino Cunha,et al.  Multifocal: A Strategic Bidirectional Transformation Language for XML Schemas , 2012, ICMT@TOOLS.

[18]  Branko Perisic,et al.  Sirius: A rapid development of DSM graphical editor , 2014, IEEE 18th International Conference on Intelligent Engineering Systems INES 2014.

[19]  Nelishia Pillay,et al.  A review of hyper-heuristics for educational timetabling , 2016, Ann. Oper. Res..

[20]  Parham Moradi,et al.  Integrating ABC with genetic grouping for university course timetabling problem , 2015, 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE).

[21]  Jordi Cabot,et al.  NeoEMF: a Multi-database Model Persistence Framework for Very Large Models , 2016, D&P@MoDELS.